[Azure External CI] Disable Azure CI on ROCm

2026-04-05 03:01:17 -04:00 · 2026-02-10 19:40:57 +00:00
66 changed files with 3222 additions and 6156 deletions
--- a/.wordlist.txt
+++ b/.wordlist.txt
@@ -83,7 +83,6 @@ Cavium
 CentOS
 ChatGPT
 Cholesky
 cholesky
 CoRR
 Codespaces
 Commitizen
@@ -171,7 +170,6 @@ FluxBenchmark
 Fortran
 Fuyu
 GALB
 GART
 GAT
 GATNE
 GCC
@@ -207,13 +205,11 @@ GPT
 GPU
 GPU's
 GPUDirect
 GPUVM
 GPUs
 GraphBolt
 GraphSage
 GRBM
 GRE
 GTT
 GenAI
 GenZ
 GitHub
@@ -300,11 +296,9 @@ LLMs
 LLVM
 LM
 logsumexp
 LPDDR
 LRU
 LSAN
 LSan
 lstsq
 LTS
 LSTMs
 LteAll
@@ -450,7 +444,6 @@ QPS
 Qcycles
 QoS
 Qwen
 Radix
 RAII
 RAS
 RCCL
@@ -530,7 +523,6 @@ Skylake
 Softmax
 Spack
 SplitK
 Strix
 Supermicro
 Szegedy
 TagRAM
@@ -541,7 +533,6 @@ TCI
 TCIU
 TCP
 TCR
 TTM
 TVM
 THREADGROUPS
 threadgroups
@@ -596,9 +587,6 @@ verl's
 VGPR
 VGPRs
 VM
 VMID
 VMIDs
 VMs
 VMEM
 VMWare
 VRAM
@@ -686,7 +674,6 @@ cmake
 cmd
 coalescable
 codename
 codenamed
 collater
 comgr
 compat
@@ -883,7 +870,6 @@ netplan
 num
 numref
 ocl
 openai
 opencl
 opencv
 openmp
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,84 +4,6 @@ This page is a historical overview of changes made to ROCm components. This
 consolidated changelog documents key modifications and improvements across
 different versions of the ROCm software stack and its components.
 ## ROCm 7.2.1
 See the [ROCm 7.2.1 release notes](https://rocm.docs.amd.com/en/docs-7.2.1/about/release-notes.html#rocm-7-2-1-release-notes)
 for a complete overview of this release.
 ### **AMD SMI** (26.2.2) 
 #### Added
 * GPU board and base board temperature sensors to `amd-smi monitor` command.
 #### Resolved issues
 * JSON output was not formatted correctly when using watch mode with metrics.
 * Output was not properly redirected to file when using JSON format. 
 * CPER component output was not redirected when using the `--follow` option.
 * Invalid CPER files caused garbage output for AFID lists.
 * JSON output was not formatted correctly for reset commands.
 ### **HIP** (7.2.1)
 #### Resolved issues
 * Corrected the validation of stream capture in global‑capture mode. It is no longer affected by any thread‑local capture‑mode sequences occurring in other threads. 
 * Corrected the return value of `hipEventQuery` and `hipEventSynchronize`. The HIP runtime now properly handles and restricts stream capture within these APIs. 
 * Corrected an issue in the batch-dispatch doorbell for AQL packets to avoid a potential CPU hang.
 * To address potential delays in memory‑object destruction that could affect application logic, the HIP runtime disables memory‑object reference counting in direct‑dispatch mode.
 #### Changed
 * The `AMD_DIRECT_DISPATCH` environment variable has been deprecated in the HIP runtime.
 ### **hipBLASLt** (1.2.2)
 #### Changed
 * Enumeration value update for the Sigmoid Activation Function feature.
 ### **rocDecode** (1.7.0)
 #### Upcoming changes
 * The rocDecode GitHub repository will be officially moved to [https://github.com/ROCm/rocm-systems/tree/develop/projects/rocdecode](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocdecode) in an upcoming release.
 ### **rocJPEG** (1.4.0)
 #### Changed
 * Bug fixes and performance improvements.
 #### Upcoming changes
 * The rocJPEG GitHub repository will be officially moved to [https://github.com/ROCm/rocm-systems/tree/develop/projects/rocjpeg](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocjpeg) in an upcoming release.
 ### **rocSHMEM** (3.2.0)
 #### Added
 * Warnings to notify if large BAR is not available.
 #### Resolved issues
 * GDA Backend will disable itself when no GDA compatible NICs are available rather than crashing.
 * Fix memory coherency issues on gfx1201.
 #### Known issues
 * Only 64-bit rocSHMEM atomic APIs are implemented for the GDA conduit.
 ### **RPP** (2.2.1)
 #### Added
 * Error-code capture in test scripts for all C++ tests.
 #### Optimized
 * Optimized F16 variants by replacing scalar load/store operations with AVX2 intrinsics for spatter, log, blend, color_cast, flip, crop_mirror_normalize, and exposure kernels.
 ## ROCm 7.2.0
 See the [ROCm 7.2.0 release notes](https://rocm.docs.amd.com/en/docs-7.2.0/about/release-notes.html#rocm-7-2-0-release-notes)
@@ -769,13 +691,6 @@ for a complete overview of this release.
 #### Resolved issues
 * Test Suite - Error Code Capture updates.
 ### **Tensile** (4.45.0)
 #### Removed
 - `op_sel` modifiers for `v_dot4` from Tensile codegen.
 - Dependency on `rocm-agent-enumerator` during build.
 ## ROCm 7.1.1
 See the [ROCm 7.1.1 release notes](https://rocm.docs.amd.com/en/docs-7.1.1/about/release-notes.html#rocm-7-1-1-release-notes)
--- a/README.md
+++ b/README.md
@@ -1,165 +1,49 @@
-<div align="center">
+# AMD ROCm Software
 <img src="docs/data/amd-rocm-logo.png" width="200px" alt="ROCm logo">
 <h3 align="center">
 Open-source stack designed for GPU computation
 </h3>
 <p align="center">
 <a href="https://rocm.docs.amd.com/en/latest/"><b>Docs</b></a> • <a href="https://rocm.blogs.amd.com/"><b>Blogs</b></a> • <a href="https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/"><b>Tutorials</b></a> • <a href="https://rocm.docs.amd.com/en/latest/how-to/deep-learning-rocm.html"><b>Deep learning frameworks</b></a> • <a href="https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/index.html"><b>ROCm for AI</b></a>
 </p>
 </div>
 # AMD ROCm™ software
 ROCm is an open-source stack, composed primarily of open-source software, designed for graphics
 processing unit (GPU) computation. ROCm consists of a collection of drivers, development tools, and
 APIs that enable GPU programming from low-level kernel to end-user applications.
-You can customize the ROCm software to meet your specific needs. You can develop,
+With ROCm, you can customize your GPU software to meet your specific needs. You can develop,
-collaborate, test, and deploy your applications in a free, open-source, integrated, and secure software
+collaborate, test, and deploy your applications in a free, open source, integrated, and secure software
 ecosystem. ROCm is particularly well-suited to GPU-accelerated high-performance computing (HPC),
-artificial intelligence (AI), scientific computing, and computer-aided design (CAD).
+artificial intelligence (AI), scientific computing, and computer aided design (CAD).
-ROCm is powered by [HIP](https://github.com/ROCm/rocm-systems/tree/develop/projects/hip),
+ROCm is powered by AMD’s
-a C++ runtime API and kernel language for AMD GPUs. HIP allows developers to create portable
+[Heterogeneous-computing Interface for Portability (HIP)](https://github.com/ROCm/HIP),
-applications by providing a programming interface that is similar to NVIDIA CUDA™.
+an open-source software C++ GPU programming environment and its corresponding runtime. HIP
 allows ROCm developers to create portable applications on different platforms by deploying code on a
 range of platforms, from dedicated gaming GPUs to exascale HPC clusters.
-ROCm supports programming models, such as OpenMP and OpenCL, and includes all necessary
+ROCm supports programming models, such as OpenMP and OpenCL, and includes all necessary open
-open-source software compilers, debuggers, and libraries. ROCm is fully integrated into machine learning
+source software compilers, debuggers, and libraries. ROCm is fully integrated into machine learning
 (ML) frameworks, such as PyTorch and TensorFlow.
 > [!IMPORTANT]
-> A new open-source build platform for ROCm is under development at
+> A new open source build platform for ROCm is under development at
 > https://github.com/ROCm/TheRock, featuring a unified CMake build with bundled
-> dependencies, Microsoft Windows support, and more.
+> dependencies, Windows support, and more.
-## Table of contents
+## Getting and Building ROCm from Source
- [Supported hardware and operating systems](#supported-hardware-and-operating-systems)
+Please use [TheRock](https://github.com/ROCm/TheRock) build system to build ROCm from source.
 - [Quick start](#quick-start)
  - [Get started with ROCm](#get-started-with-rocm)
  - [Get started with PyTorch on ROCm](#get-started-with-pytorch-on-rocm)
 - [Core components](#core-components)
  - [Math libraries](#math-libraries)
  - [ML and computer vision](#ml-and-computer-vision)
  - [Collective communication and primitives](#collective-communication-and-primitives)
  - [System management tools](#system-management-tools)
  - [Profiling tools](#profiling-tools)
  - [Development tools](#development-tools)
  - [Runtimes and compilers](#runtimes-and-compilers)
 - [Release notes](#release-notes)
 - [Licenses](#licenses)
 - [ROCm release history](#rocm-release-history)
 - [Contribute](#contribute)
---
+## ROCm documentation
-## Supported hardware and operating systems
+This repository contains the [manifest file](https://gerrit.googlesource.com/git-repo/+/HEAD/docs/manifest-format.md)
 for ROCm releases, changelogs, and release information.
-Use the [Compatibility matrix](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html) for official support across ROCm versions, operating system kernels, and GPU architectures (CDNA/Instinct™, RDNA/Radeon™, and Radeon Pro). Recent releases cover Ubuntu, RHEL, SLES, Oracle Linux, Debian, Rocky Linux, and more. GPU targets include CDNA4, CDNA3, CDNA2, RDNA4, and RDNA3.
+The `default.xml` file contains information for all repositories and the associated commit used to build
 the current ROCm release; `default.xml` uses the [Manifest Format repository](https://gerrit.googlesource.com/git-repo/).
-If you’re using AMD Radeon GPUs or Ryzen APUs in a workstation setting with a display connected, see the [ROCm on Radeon and Ryzen documentation](https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html) for operating system/framework support and step-by-step installation instructions.
+Source code for our documentation is located in the `/docs` folder of most ROCm repositories. The
 `develop` branch of our repositories contains content for the next ROCm release.
---
+The ROCm documentation homepage is [rocm.docs.amd.com](https://rocm.docs.amd.com).
-## Quick start
+For information on how to contribute to the ROCm documentation, see [Contributing to the ROCm documentation](https://rocm.docs.amd.com/en/latest/contribute/contributing.html).
-Follow these instructions to start using ROCm.
+## Older ROCm releases
-### Get started with ROCm
+For release information for older ROCm releases, refer to the
 Follow the [ROCm installation guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/quick-start.html) to install ROCm on your system.
 ### Get started with PyTorch on ROCm
 Follow the [PyTorch on ROCm installation guide](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/pytorch-install.html) to install PyTorch with ROCm support in a Docker environment.
 ---
 ## Core components
 The core ROCm stack consists of the following components:
 ### Math libraries
 - [rocBLAS](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocblas), [hipBLAS](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipblas), and [hipBLASLt](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipblaslt)
 - [rocFFT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocfft) and [hipFFT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipfft)
 - [rocRAND](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocrand) and [hipRAND](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hiprand)
 - [rocSOLVER](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocsolver) and [hipSOLVER](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipsolver)
 - [rocSPARSE](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocsparse) and [hipSPARSE](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipsparse)
 - [rocWMMA](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocwmma) and [hipTensor](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hiptensor)
 ### ML and computer vision
 - [Composable Kernel](https://github.com/ROCm/rocm-libraries/tree/develop/projects/composablekernel)
 - [MIGraphX](https://github.com/ROCm/AMDMIGraphX/)
 - [MIOpen](https://github.com/ROCm/rocm-libraries/tree/develop/projects/miopen)
 - [MIVisionX](https://github.com/ROCm/MIVisionX)
 - [ROCm Performance Primitives (RPP)](https://github.com/ROCm/rpp)
 ### Collective communication and primitives
 - [hipCUB](https://github.com/ROCm/rocm-libraries/tree/develop/projects/hipcub)
 - [RCCL](https://github.com/ROCm/rocm-systems/tree/develop/projects/rccl)
 - [rocPRIM](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocprim)
 - [rocSHMEM](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocshmem)
 - [rocThrust](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocthrust)
 ### System management tools
 - [AMD SMI](https://github.com/ROCm/rocm-systems/tree/develop/projects/amdsmi)
 - [rocminfo](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocminfo)
 ### Profiling tools
 - [ROCprofiler-SDK](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler-sdk)
 - [ROCm Compute Profiler](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocprofiler-compute)
 ### Development tools
 - [ROCm Debugger (ROCgdb)](https://github.com/ROCm/ROCgdb)
 - [ROCdbgapi](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocdbgapi)
 ### Runtimes and compilers
 - [HIP](https://github.com/ROCm/rocm-systems/tree/develop/projects/hip)
 - [LLVM](https://github.com/ROCm/llvm-project)
 - [ROCR Runtime (ROCR)](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocr-runtime)
 For a complete list of ROCm components and version information, see the
 [ROCm components](https://rocm.docs.amd.com/en/latest/about/release-notes.html#rocm-components).
 ---
 ## Release notes
 - [Latest version of ROCm](https://rocm.docs.amd.com/en/latest/about/release-notes.html) - production
 - [ROCm 7.12.0](https://rocm.docs.amd.com/en/7.12.0-preview/about/release-notes.html) – preview stream
 ---
 ## Licenses
 - [ROCm licenses](https://rocm.docs.amd.com/en/latest/about/license.html)
 ---
 ## ROCm release history
 For information on older ROCm releases, see the
 [ROCm release history](https://rocm.docs.amd.com/en/latest/release/versions.html).
 ---
 ## Contribute
 AMD welcomes ROCm contributions using GitHub PRs or issues. See the links
 below for contribution guidelines.
 - [ROCm](CONTRIBUTING.md)
 - [TheRock](https://github.com/ROCm/TheRock/blob/main/CONTRIBUTING.md)
 - [ROCm documentation](https://rocm.docs.amd.com/en/latest/contribute/contributing.html)
 - [ROCm Systems](https://github.com/ROCm/rocm-systems/blob/develop/CONTRIBUTING.md)
 - [ROCm Libraries](https://github.com/ROCm/rocm-libraries/blob/develop/CONTRIBUTING.md)
--- a/RELEASE.md
+++ b/RELEASE.md
--- a/docs/about/license.md
+++ b/docs/about/license.md
@@ -31,7 +31,7 @@ additional licenses. Please review individual repositories for more information.
 | [aomp-extras](https://github.com/ROCm/aomp-extras/) | [MIT](https://github.com/ROCm/aomp-extras/blob/aomp-dev/LICENSE) |
 | [AQLprofile](https://github.com/ROCm/rocm-systems/tree/develop/projects/aqlprofile/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/aqlprofile/LICENSE.md) |
 | [Code Object Manager (Comgr)](https://github.com/ROCm/llvm-project/tree/amd-staging/amd/comgr) | [The University of Illinois/NCSA](https://github.com/ROCm/llvm-project/blob/amd-staging/amd/comgr/LICENSE.txt) |
-| [Composable Kernel](https://github.com/ROCm/rocm-libraries/tree/develop/projects/composablekernel) | [MIT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/composablekernel/LICENSE) |
+| [Composable Kernel](https://github.com/ROCm/composable_kernel) | [MIT](https://github.com/ROCm/composable_kernel/blob/develop/LICENSE) |
 | [half](https://github.com/ROCm/half/) | [MIT](https://github.com/ROCm/half/blob/rocm/LICENSE.txt) |
 | [HIP](https://github.com/ROCm/rocm-systems/tree/develop/projects/hip/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/hip/LICENSE.md) |
 | [hipamd](https://github.com/ROCm/rocm-systems/tree/develop/projects/clr/hipamd/) | [MIT](https://github.com/ROCm/rocm-systems/blob/develop/projects/clr/hipamd/LICENSE.md) |
@@ -56,10 +56,10 @@ additional licenses. Please review individual repositories for more information.
 | [rocALUTION](https://github.com/ROCm/rocALUTION/) | [MIT](https://github.com/ROCm/rocALUTION/blob/develop/LICENSE.md) |
 | [rocBLAS](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocblas/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocblas/LICENSE.md) |
 | [ROCdbgapi](https://github.com/ROCm/ROCdbgapi/) | [MIT](https://github.com/ROCm/ROCdbgapi/blob/amd-staging/LICENSE.txt) |
-| [rocDecode](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocdecode) | [MIT](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocdecode/LICENSE) |
+| [rocDecode](https://github.com/ROCm/rocDecode) | [MIT](https://github.com/ROCm/rocDecode/blob/develop/LICENSE) |
 | [rocFFT](https://github.com/ROCm/rocm-libraries/tree/develop/projects/rocfft/) | [MIT](https://github.com/ROCm/rocm-libraries/blob/develop/projects/rocfft/LICENSE.md) |
 | [ROCgdb](https://github.com/ROCm/ROCgdb/) | [GNU General Public License v3.0](https://github.com/ROCm/ROCgdb/blob/amd-staging/COPYING3) |
-| [rocJPEG](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocjpeg) | [MIT](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocjpeg/LICENSE) |
+| [rocJPEG](https://github.com/ROCm/rocJPEG/) | [MIT](https://github.com/ROCm/rocJPEG/blob/develop/LICENSE) |
 | [ROCK-Kernel-Driver](https://github.com/ROCm/ROCK-Kernel-Driver/) | [GPL 2.0 WITH Linux-syscall-note](https://github.com/ROCm/ROCK-Kernel-Driver/blob/master/COPYING) |
 | [rocminfo](https://github.com/ROCm/rocm-systems/tree/develop/projects/rocminfo/) | [The University of Illinois/NCSA](https://github.com/ROCm/rocm-systems/blob/develop/projects/rocminfo/License.txt) |
 | [ROCm Bandwidth Test](https://github.com/ROCm/rocm_bandwidth_test/) | [MIT](https://github.com/ROCm/rocm_bandwidth_test/blob/master/LICENSE.txt) |
--- a/docs/compatibility/compatibility-matrix-historical-6.0.csv
+++ b/docs/compatibility/compatibility-matrix-historical-6.0.csv
@@ -1,130 +1,136 @@
-ROCm Version,7.2.1,7.2.0,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
+ROCm Version,7.2.0,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0, 6.1.5, 6.1.2, 6.1.1, 6.1.0, 6.0.2, 6.0.0
-      :ref:`Operating systems & kernels <OS-kernel-versions>` [#os-compatibility-past-60]_,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,,
+      :ref:`Operating systems & kernels <OS-kernel-versions>` [#os-compatibility-past-60]_,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,Ubuntu 24.04.2,"Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04","Ubuntu 24.04.1, 24.04",Ubuntu 24.04,,,,,,
-      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
+      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5,"Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4","Ubuntu 22.04.5, 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3","Ubuntu 22.04.4, 22.04.3, 22.04.2","Ubuntu 22.04.4, 22.04.3, 22.04.2"
-      ,,,,,,,,,,,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
+      ,,,,,,,,,,,,,,,,,,"Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5","Ubuntu 20.04.6, 20.04.5"
-      ,"RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.0, 9.6, 9.4","RHEL 10.0, 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
+      ,"RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.0, 9.6, 9.4","RHEL 10.0, 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.6, 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.5, 9.4","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.4, 9.3, 9.2","RHEL 9.3, 9.2","RHEL 9.3, 9.2"
-      ,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
+      ,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,RHEL 8.10,"RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.10, 8.9","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8","RHEL 8.9, 8.8"
-      ,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,"SLES 15 SP7, SP6","SLES 15 SP7, SP6",SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
+      ,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,SLES 15 SP7,"SLES 15 SP7, SP6","SLES 15 SP7, SP6",SLES 15 SP6,SLES 15 SP6,"SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP6, SP5","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4","SLES 15 SP5, SP4"
-      ,,,,,,,,,,,,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
+      ,,,,,,,,,,,,,,,,,,,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9,CentOS 7.9
-      ,"Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8",Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,,,
+      ,"Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 10, 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8","Oracle Linux 9, 8",Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.10,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,Oracle Linux 8.9,,,
-      ,"Debian 13, 12","Debian 13, 12","Debian 13, 12","Debian 13, 12","Debian 13, 12",Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,,,,,,,,,,,
+      ,"Debian 13, 12","Debian 13, 12","Debian 13, 12","Debian 13, 12",Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,Debian 12,,,,,,,,,,,
-      ,,,,,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,,,,,,,,,,,,
+      ,,,,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,Azure Linux 3.0,,,,,,,,,,,,
-      ,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,,,,,,,,,,,,,,,,,,
+      ,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,Rocky Linux 9,,,,,,,,,,,,,,,,,,
-      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,CDNA4,CDNA4,CDNA4,CDNA4,,,,,,,,,,,,,,,,,,
+      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,CDNA4,CDNA4,CDNA4,CDNA4,,,,,,,,,,,,,,,,,,
-      ,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
+      ,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
-      ,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
+      ,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2,CDNA2
-      ,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
+      ,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA,CDNA
-      ,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,,,,,,,,,,,,,,,
+      ,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,RDNA4,,,,,,,,,,,,,,,
-      ,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
+      ,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3,RDNA3
-      ,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
+      ,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2,RDNA2
-      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`  [#gpu-compatibility-past-60]_,gfx950,gfx950,gfx950,gfx950,gfx950,gfx950,,,,,,,,,,,,,,,,,,
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`  [#gpu-compatibility-past-60]_,gfx950,gfx950,gfx950,gfx950,gfx950,,,,,,,,,,,,,,,,,,
-      ,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,,,,,,,,,,,,,,,
+      ,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,gfx1201,,,,,,,,,,,,,,,
-      ,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,,,,,,,,,,,,,,,
+      ,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,gfx1200,,,,,,,,,,,,,,,
-      ,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,,,,,,,,,,,,,,,
+      ,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,gfx1101,,,,,,,,,,,,,,,
-      ,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
+      ,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
-      ,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
+      ,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
-      ,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942, gfx942, gfx942, gfx942, gfx942, gfx942, gfx942
+      ,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942, gfx942, gfx942, gfx942, gfx942, gfx942, gfx942
-      ,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
+      ,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
-      ,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
+      ,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908,gfx908
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9.1, 2.8.0, 2.7.1","2.9.1, 2.8.0, 2.7.1","2.9, 2.8, 2.7","2.8, 2.7, 2.6","2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
+      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9.1, 2.8.0, 2.7.1","2.9, 2.8, 2.7","2.8, 2.7, 2.6","2.8, 2.7, 2.6","2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 1.13","2.4, 2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.3, 2.2, 2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13","2.1, 2.0, 1.13"
-      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
+      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.19.1, 2.18.1, 2.17.1 [#tf-mi350-past-60]_","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.17.0, 2.16.2, 2.15.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.16.1, 2.15.1, 2.14.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.15.0, 2.14.0, 2.13.1","2.14.0, 2.13.1, 2.12.1","2.14.0, 2.13.1, 2.12.1"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.8.2,0.8.0,0.7.1,0.7.1,0.6.0,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
+      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.8.0,0.7.1,0.7.1,0.6.0,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,2.4.0,2.4.0,N/A,N/A,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat-past-60]_,N/A,N/A,N/A,N/A,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,N/A
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.2,1.23.2,1.23.1,1.22.0,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
+      :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` [#stanford-megatron-lm_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,85f95ae,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat-past-60]_,N/A,N/A,N/A,N/A,2.4.0,2.4.0,N/A,N/A,2.4.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>` [#megablocks_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>` [#ray_compat-past-60]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,2.48.0.post0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      `UCC <https://github.com/ROCm/ucc>`_,>=1.6.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
+      :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat-past-60]_,N/A,N/A,N/A,N/A,b6652,b6356,b6356,b6356,b5997,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      `UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
+      :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>` [#flashinfer_compat-past-60]_,N/A,v0.2.5,N/A,N/A,N/A,N/A,N/A,v0.2.5,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.2,1.23.1,1.22.0,1.22.0,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
-      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      Thrust,2.8.5,2.8.5,2.8.5,2.8.5,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      CUB,2.8.5,2.8.5,2.8.5,2.8.5,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
+      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      `UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.4.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.3.0,>=1.2.0,>=1.2.0
-     DRIVER & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      `UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.17.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.15.0,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1,>=1.14.1
-      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.30.1, 30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      Thrust,2.8.5,2.8.5,2.8.5,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      :doc:`Composable Kernel <composable_kernel:index>`,1.2.0,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
+      CUB,2.8.5,2.8.5,2.8.5,2.6.0,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
-      :doc:`MIGraphX <amdmigraphx:index>`,2.15.0,2.15.0,2.14.0,2.14.0,2.13.0,2.13.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`MIOpen <miopen:index>`,3.5.1,3.5.1,3.5.1,3.5.1,3.5.0,3.5.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+     DRIVER & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`MIVisionX <mivisionx:index>`,3.5.0,3.5.0,3.4.0,3.4.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
+      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.20.1, 30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.20.0 [#mi325x_KVM-past-60]_, 30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x","30.10.2, 30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x","30.10.1 [#driver_patch-past-60]_, 30.10, 6.4.x, 6.3.x, 6.2.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.4.x, 6.3.x, 6.2.x, 6.1.x, 6.0.x, 5.7.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x","6.2.x, 6.1.x, 6.0.x, 5.7.x, 5.6.x"
-      :doc:`rocAL <rocal:index>`,2.5.0,2.5.0,2.4.0,2.4.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`rocDecode <rocdecode:index>`,1.7.0,1.5.0,1.4.0,1.4.0,1.0.0,1.0.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
+      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`rocJPEG <rocjpeg:index>`,1.4.0,1.3.0,1.2.0,1.2.0,1.1.0,1.1.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`Composable Kernel <composable_kernel:index>`,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0
-      :doc:`rocPyDecode <rocpydecode:index>`,0.8.0,0.8.0,0.7.0,0.7.0,0.6.0,0.6.0,0.3.1,0.3.1,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`MIGraphX <amdmigraphx:index>`,2.15.0,2.14.0,2.14.0,2.13.0,2.13.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.0,2.11.0,2.11.0,2.11.0,2.10.0,2.10.0,2.10.0,2.10.0,2.9.0,2.9.0,2.9.0,2.9.0,2.8.0,2.8.0
-      :doc:`RPP <rpp:index>`,2.2.1,2.2.0,2.1.0,2.1.0,2.0.0,2.0.0,1.9.10,1.9.10,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
+      :doc:`MIOpen <miopen:index>`,3.5.1,3.5.1,3.5.1,3.5.0,3.5.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`MIVisionX <mivisionx:index>`,3.5.0,3.4.0,3.4.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0,2.5.0
-      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`rocAL <rocal:index>`,2.5.0,2.4.0,2.4.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0,2.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.27.7,2.27.7,2.26.6,2.26.6,2.22.3,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
+      :doc:`rocDecode <rocdecode:index>`,1.5.0,1.4.0,1.4.0,1.0.0,1.0.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,N/A,N/A
-      :doc:`rocSHMEM <rocshmem:index>`,3.2.0,3.2.0,3.1.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`rocJPEG <rocjpeg:index>`,1.3.0,1.2.0,1.2.0,1.1.0,1.1.0,0.8.0,0.8.0,0.8.0,0.8.0,0.6.0,0.6.0,0.6.0,0.6.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`rocPyDecode <rocpydecode:index>`,0.8.0,0.7.0,0.7.0,0.6.0,0.6.0,0.3.1,0.3.1,0.3.1,0.3.1,0.2.0,0.2.0,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0,N/A,N/A,N/A,N/A,N/A,N/A
-      MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`RPP <rpp:index>`,2.2.0,2.1.0,2.1.0,2.0.0,2.0.0,1.9.10,1.9.10,1.9.10,1.9.10,1.9.1,1.9.1,1.9.1,1.9.1,1.8.0,1.8.0,1.8.0,1.8.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0
-      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`hipBLAS <hipblas:index>`,3.2.0,3.2.0,3.1.0,3.1.0,3.0.2,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
+      COMMUNICATION,.. _commlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`hipBLASLt <hipblaslt:index>`,1.2.2,1.2.1,1.1.0,1.1.0,1.0.0,1.0.0,0.12.1,0.12.1,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
+      :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.27.7,2.26.6,2.26.6,2.22.3,2.22.3,2.22.3,2.22.3,2.21.5,2.21.5,2.21.5,2.21.5,2.20.5,2.20.5,2.20.5,2.20.5,2.18.6,2.18.6,2.18.6,2.18.6,2.18.3,2.18.3
-      :doc:`hipFFT <hipfft:index>`,1.0.22,1.0.22,1.0.21,1.0.21,1.0.20,1.0.20,1.0.18,1.0.18,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
+      :doc:`rocSHMEM <rocshmem:index>`,3.2.0,3.1.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.0,2.0.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`hipfort <hipfort:index>`,0.7.1,0.7.1,0.7.1,0.7.1,0.7.0,0.7.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`hipRAND <hiprand:index>`,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
+      MATH LIBS,.. _mathlibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`hipSOLVER <hipsolver:index>`,3.2.0,3.2.0,3.1.0,3.1.0,3.0.0,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
+      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0,1.12.0
-      :doc:`hipSPARSE <hipsparse:index>`,4.2.0,4.2.0,4.1.0,4.1.0,4.0.1,4.0.1,3.2.0,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
+      :doc:`hipBLAS <hipblas:index>`,3.2.0,3.1.0,3.1.0,3.0.2,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.0,2.0.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.6,0.2.6,0.2.5,0.2.5,0.2.4,0.2.4,0.2.3,0.2.3,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
+      :doc:`hipBLASLt <hipblaslt:index>`,1.2.1,1.1.0,1.1.0,1.0.0,1.0.0,0.12.1,0.12.1,0.12.1,0.12.0,0.10.0,0.10.0,0.10.0,0.10.0,0.8.0,0.8.0,0.8.0,0.8.0,0.7.0,0.7.0,0.7.0,0.7.0,0.6.0,0.6.0
-      :doc:`rocALUTION <rocalution:index>`,4.1.0,4.1.0,4.0.1,4.0.1,4.0.0,4.0.0,3.2.3,3.2.3,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
+      :doc:`hipFFT <hipfft:index>`,1.0.22,1.0.21,1.0.21,1.0.20,1.0.20,1.0.18,1.0.18,1.0.18,1.0.18,1.0.17,1.0.17,1.0.17,1.0.17,1.0.16,1.0.15,1.0.15,1.0.14,1.0.14,1.0.14,1.0.14,1.0.14,1.0.13,1.0.13
-      :doc:`rocBLAS <rocblas:index>`,5.2.0,5.2.0,5.1.1,5.1.0,5.0.2,5.0.0,4.4.1,4.4.1,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
+      :doc:`hipfort <hipfort:index>`,0.7.1,0.7.1,0.7.1,0.7.0,0.7.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.1,0.5.1,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0
-      :doc:`rocFFT <rocfft:index>`,1.0.36,1.0.36,1.0.35,1.0.35,1.0.34,1.0.34,1.0.32,1.0.32,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
+      :doc:`hipRAND <hiprand:index>`,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0,2.12.0,2.12.0,2.12.0,2.12.0,2.11.1,2.11.1,2.11.1,2.11.0,2.11.1,2.11.0,2.11.0,2.11.0,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16,2.10.16
-      :doc:`rocRAND <rocrand:index>`,4.2.0,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
+      :doc:`hipSOLVER <hipsolver:index>`,3.2.0,3.1.0,3.1.0,3.0.0,3.0.0,2.4.0,2.4.0,2.4.0,2.4.0,2.3.0,2.3.0,2.3.0,2.3.0,2.2.0,2.2.0,2.2.0,2.2.0,2.1.1,2.1.1,2.1.1,2.1.0,2.0.0,2.0.0
-      :doc:`rocSOLVER <rocsolver:index>`,3.32.0,3.32.0,3.31.0,3.31.0,3.30.1,3.30.0,3.28.2,3.28.2,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
+      :doc:`hipSPARSE <hipsparse:index>`,4.2.0,4.1.0,4.1.0,4.0.1,4.0.1,3.2.0,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.1.1,3.1.1,3.1.1,3.1.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      :doc:`rocSPARSE <rocsparse:index>`,4.2.0,4.2.0,4.1.0,4.1.0,4.0.2,4.0.2,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
+      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.6,0.2.5,0.2.5,0.2.4,0.2.4,0.2.3,0.2.3,0.2.3,0.2.3,0.2.2,0.2.2,0.2.2,0.2.2,0.2.1,0.2.1,0.2.1,0.2.1,0.2.0,0.2.0,0.1.0,0.1.0,0.1.0,0.1.0
-      :doc:`rocWMMA <rocwmma:index>`,2.2.0,2.2.0,2.1.0,2.0.0,2.0.0,2.0.0,1.7.0,1.7.0,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
+      :doc:`rocALUTION <rocalution:index>`,4.1.0,4.0.1,4.0.1,4.0.0,4.0.0,3.2.3,3.2.3,3.2.3,3.2.2,3.2.1,3.2.1,3.2.1,3.2.1,3.2.1,3.2.0,3.2.0,3.2.0,3.1.1,3.1.1,3.1.1,3.1.1,3.0.3,3.0.3
-      :doc:`Tensile <tensile:src/index>`,4.45.0,4.45.0,4.44.0,4.44.0,4.44.0,4.44.0,4.43.0,4.43.0,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
+      :doc:`rocBLAS <rocblas:index>`,5.2.0,5.1.1,5.1.0,5.0.2,5.0.0,4.4.1,4.4.1,4.4.0,4.4.0,4.3.0,4.3.0,4.3.0,4.3.0,4.2.4,4.2.1,4.2.1,4.2.0,4.1.2,4.1.2,4.1.0,4.1.0,4.0.0,4.0.0
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`rocFFT <rocfft:index>`,1.0.36,1.0.35,1.0.35,1.0.34,1.0.34,1.0.32,1.0.32,1.0.32,1.0.32,1.0.31,1.0.31,1.0.31,1.0.31,1.0.30,1.0.29,1.0.29,1.0.28,1.0.27,1.0.27,1.0.27,1.0.26,1.0.25,1.0.23
-      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`rocRAND <rocrand:index>`,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.0,3.2.0,3.2.0,3.2.0,3.1.1,3.1.0,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,2.10.17
-      :doc:`hipCUB <hipcub:index>`,4.2.0,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`rocSOLVER <rocsolver:index>`,3.32.0,3.31.0,3.31.0,3.30.1,3.30.0,3.28.2,3.28.2,3.28.0,3.28.0,3.27.0,3.27.0,3.27.0,3.27.0,3.26.2,3.26.0,3.26.0,3.26.0,3.25.0,3.25.0,3.25.0,3.25.0,3.24.0,3.24.0
-      :doc:`hipTensor <hiptensor:index>`,2.2.0,2.2.0,2.0.0,2.0.0,2.0.0,2.0.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
+      :doc:`rocSPARSE <rocsparse:index>`,4.2.0,4.1.0,4.1.0,4.0.2,4.0.2,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.2,3.1.2,3.1.2,3.1.2,3.0.2,3.0.2
-      :doc:`rocPRIM <rocprim:index>`,4.2.0,4.2.0,4.1.0,4.1.0,4.0.1,4.0.0,3.4.1,3.4.1,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
+      :doc:`rocWMMA <rocwmma:index>`,2.2.0,2.1.0,2.0.0,2.0.0,2.0.0,1.7.0,1.7.0,1.7.0,1.7.0,1.6.0,1.6.0,1.6.0,1.6.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0
-      :doc:`rocThrust <rocthrust:index>`,4.2.0,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
+      :doc:`Tensile <tensile:src/index>`,4.44.0,4.44.0,4.44.0,4.44.0,4.44.0,4.43.0,4.43.0,4.43.0,4.43.0,4.42.0,4.42.0,4.42.0,4.42.0,4.41.0,4.41.0,4.41.0,4.41.0,4.40.0,4.40.0,4.40.0,4.40.0,4.39.0,4.39.0
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      SUPPORT LIBS,,,,,,,,,,,,,,,,,,,,,,,,
+      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      `hipother <https://github.com/ROCm/hipother>`_,7.2.53211,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43483,6.4.43483,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      :doc:`hipCUB <hipcub:index>`,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.4.0,3.4.0,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.1,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.2.1,7.2.0,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
+      :doc:`hipTensor <hiptensor:index>`,2.2.0,2.0.0,2.0.0,2.0.0,2.0.0,1.5.0,1.5.0,1.5.0,1.5.0,1.4.0,1.4.0,1.4.0,1.4.0,1.3.0,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0
-      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
+      :doc:`rocPRIM <rocprim:index>`,4.2.0,4.1.0,4.1.0,4.0.1,4.0.0,3.4.1,3.4.1,3.4.0,3.4.0,3.3.0,3.3.0,3.3.0,3.3.0,3.2.2,3.2.0,3.2.0,3.2.0,3.1.0,3.1.0,3.1.0,3.1.0,3.0.0,3.0.0
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`rocThrust <rocthrust:index>`,4.2.0,4.1.0,4.1.0,4.0.0,4.0.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.3.0,3.1.1,3.1.0,3.1.0,3.0.1,3.0.1,3.0.1,3.0.1,3.0.1,3.0.0,3.0.0
-      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`AMD SMI <amdsmi:index>`,26.2.2,26.2.1,26.2.0,26.1.0,26.0.2,26.0.0,25.5.1,25.5.1,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
+      SUPPORT LIBS,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`ROCm Data Center Tool <rdc:index>`,1.2.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
+      `hipother <https://github.com/ROCm/hipother>`_,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43483,6.4.43483,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
-      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.2.0,7.1.1,7.1.0,7.0.2,7.0.1/7.0.0,6.4.3,6.4.2,6.4.1,6.4.0,6.3.3,6.3.2,6.3.1,6.3.0,6.2.4,6.2.2,6.2.1,6.2.0,6.1.5,6.1.2,6.1.1,6.1.0,6.0.2,6.0.0
-      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.8.0,7.8.0,7.8.0,7.8.0,7.7.0,7.5.0,7.5.0,7.5.0,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
+      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,N/A [#ROCT-rocr-past-60]_,20240607.5.7,20240607.5.7,20240607.4.05,20240607.1.4246,20240125.5.08,20240125.5.08,20240125.5.08,20240125.3.30,20231016.2.245,20231016.2.245
-      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.3.0,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      PERFORMANCE TOOLS,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`AMD SMI <amdsmi:index>`,26.2.1,26.2.0,26.1.0,26.0.2,26.0.0,25.5.1,25.5.1,25.4.2,25.3.0,24.7.1,24.7.1,24.7.1,24.7.1,24.6.3,24.6.3,24.6.3,24.6.2,24.5.1,24.5.1,24.5.1,24.4.1,23.4.2,23.4.2
-      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,2.6.0,2.6.0,2.6.0,2.6.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
+      :doc:`ROCm Data Center Tool <rdc:index>`,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0,0.3.0
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.4.0,3.4.0,3.3.1,3.3.0,3.2.3,3.2.3,3.1.1,3.1.1,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.3.0,1.3.0,1.2.1,1.2.0,1.1.1,1.1.0,1.0.2,1.0.2,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.8.0,7.8.0,7.8.0,7.7.0,7.5.0,7.5.0,7.5.0,7.4.0,7.4.0,7.4.0,7.4.0,7.3.0,7.3.0,7.3.0,7.3.0,7.2.0,7.2.0,7.0.0,7.0.0,6.0.2,6.0.0
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70201,2.0.70200,2.0.70101,2.0.70100,2.0.70002,2.0.70000,2.0.60403,2.0.60402,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
+      :doc:`ROCm Validation Suite <rocmvalidationsuite:index>`,1.3.0,1.3.0,1.2.0,1.2.0,1.2.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.1.0,1.0.60204,1.0.60202,1.0.60201,1.0.60200,1.0.60105,1.0.60102,1.0.60101,1.0.60100,1.0.60002,1.0.60000
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.1.0,1.1.0,1.0.0,1.0.0,1.0.0,1.0.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`ROCTracer <roctracer:index>`,4.1.70201,4.1.70200,4.1.70101,4.1.70100,4.1.70002,4.1.70000,4.1.60403,4.1.60402,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
+      PERFORMANCE TOOLS,,,,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,2.6.0,2.6.0,2.6.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0,1.4.0
-      DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.4.0,3.3.1,3.3.0,3.2.3,3.2.3,3.1.1,3.1.1,3.1.0,3.1.0,3.0.0,3.0.0,3.0.0,3.0.0,2.0.1,2.0.1,2.0.1,2.0.1,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`HIPIFY <hipify:index>`,22.0.0,22.0.0,20.0.0,20.0.0,20.0.0,20.0.0,19.0.0,19.0.0,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.3.0,1.2.1,1.2.0,1.1.1,1.1.0,1.0.2,1.0.2,1.0.1,1.0.0,0.1.2,0.1.1,0.1.0,0.1.0,1.11.2,1.11.2,1.11.2,1.11.2,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
+      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70200,2.0.70101,2.0.70100,2.0.70002,2.0.70000,2.0.60403,2.0.60402,2.0.60401,2.0.60400,2.0.60303,2.0.60302,2.0.60301,2.0.60300,2.0.60204,2.0.60202,2.0.60201,2.0.60200,2.0.60105,2.0.60102,2.0.60101,2.0.60100,2.0.60002,2.0.60000
-      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.4,0.77.4,0.77.4,0.77.4,0.77.3,0.77.2,0.77.2,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
+      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.1.0,1.0.0,1.0.0,1.0.0,1.0.0,0.6.0,0.6.0,0.6.0,0.6.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,N/A,N/A,N/A,N/A,N/A,N/A
-      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,16.3.0,16.3.0,16.3.0,16.3.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
+      :doc:`ROCTracer <roctracer:index>`,4.1.70200,4.1.70101,4.1.70100,4.1.70002,4.1.70000,4.1.60403,4.1.60402,4.1.60401,4.1.60400,4.1.60303,4.1.60302,4.1.60301,4.1.60300,4.1.60204,4.1.60202,4.1.60201,4.1.60200,4.1.60105,4.1.60102,4.1.60101,4.1.60100,4.1.60002,4.1.60000
-      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.4,2.0.4,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
+      DEVELOPMENT TOOLS,,,,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`HIPIFY <hipify:index>`,22.0.0,20.0.0,20.0.0,20.0.0,20.0.0,19.0.0,19.0.0,19.0.0,19.0.0,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.14.0,0.13.0,0.13.0,0.13.0,0.13.0,0.12.0,0.12.0,0.12.0,0.12.0,0.11.0,0.11.0
-      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
+      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.4,0.77.4,0.77.4,0.77.3,0.77.2,0.77.2,0.77.2,0.77.2,0.77.0,0.77.0,0.77.0,0.77.0,0.76.0,0.76.0,0.76.0,0.76.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0,0.71.0
-      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
+      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,16.3.0,16.3.0,16.3.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,15.2.0,14.2.0,14.2.0,14.2.0,14.2.0,14.1.0,14.1.0,14.1.0,14.1.0,13.2.0,13.2.0
-      `Flang <https://github.com/ROCm/flang>`_,22.0.0.26084,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      `rocprofiler-register <https://github.com/ROCm/rocprofiler-register>`_,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.4.0,0.3.0,0.3.0,0.3.0,0.3.0,N/A,N/A
-      :doc:`llvm-project <llvm-project:index>`,22.0.0.26084,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      :doc:`ROCr Debug Agent <rocr_debug_agent:index>`,2.1.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.4,2.0.4,2.0.4,2.0.4,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3,2.0.3
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,22.0.0.26084,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
+      ,,,,,,,,,,,,,,,,,,,,,,,
-      ,,,,,,,,,,,,,,,,,,,,,,,,
+      COMPILERS,.. _compilers-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
-      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,,
+      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0,0.5.0
-      :doc:`AMD CLR <hip:understand/amd_clr>`,7.2.53211,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.1.1,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0,1.0.0
-      :doc:`HIP <hip:index>`,7.2.53211,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
+      `Flang <https://github.com/ROCm/flang>`_,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24455,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
+      :doc:`llvm-project <llvm-project:index>`,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
-      :doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.18.0,1.18.0,1.18.0,1.18.0,1.15.0,1.15.0,1.15.0,1.15.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
+      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,22.0.0.26014,20.0.025444,20.0.025425,20.0.0.25385,20.0.0.25314,19.0.0.25224,19.0.0.25224,19.0.0.25184,19.0.0.25133,18.0.0.25012,18.0.0.25012,18.0.0.24491,18.0.0.24491,18.0.0.24392,18.0.0.24355,18.0.0.24355,18.0.0.24232,17.0.0.24193,17.0.0.24193,17.0.0.24154,17.0.0.24103,17.0.0.24012,17.0.0.23483
      ,,,,,,,,,,,,,,,,,,,,,,,
      RUNTIMES,.. _runtime-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,,,,,
      :doc:`AMD CLR <hip:understand/amd_clr>`,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
      :doc:`HIP <hip:index>`,7.2.26015,7.1.52802,7.1.25424,7.0.51831,7.0.51830,6.4.43484,6.4.43484,6.4.43483,6.4.43482,6.3.42134,6.3.42134,6.3.42133,6.3.42131,6.2.41134,6.2.41134,6.2.41134,6.2.41133,6.1.40093,6.1.40093,6.1.40092,6.1.40091,6.1.32831,6.1.32830
      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0,2.0.0
      :doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.18.0,1.18.0,1.18.0,1.15.0,1.15.0,1.15.0,1.15.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.14.0,1.13.0,1.13.0,1.13.0,1.13.0,1.13.0,1.12.0,1.12.0
--- a/docs/compatibility/compatibility-matrix.rst
+++ b/docs/compatibility/compatibility-matrix.rst
@@ -22,10 +22,10 @@ compatibility and system requirements.
 .. container:: format-big-table
  .. csv-table::
-      :header: "ROCm Version", "7.2.1", "7.2.0", "6.4.0"
+      :header: "ROCm Version", "7.2.0", "7.1.1", "6.4.0"
      :stub-columns: 1
-      :ref:`Operating systems & kernels <OS-kernel-versions>` [#os-compatibility]_,Ubuntu 24.04.4,Ubuntu 24.04.3,Ubuntu 24.04.2
+      :ref:`Operating systems & kernels <OS-kernel-versions>` [#os-compatibility]_,Ubuntu 24.04.3,Ubuntu 24.04.3,Ubuntu 24.04.2
      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5
      ,"RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 10.1, 10.0, 9.7, 9.6, 9.4","RHEL 9.5, 9.4"
      ,RHEL 8.10,RHEL 8.10,RHEL 8.10
@@ -54,14 +54,16 @@ compatibility and system requirements.
      ,gfx908,gfx908,gfx908
      ,,,
      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9.1, 2.8.0, 2.7.1","2.9.1, 2.8.0, 2.7.1","2.6, 2.5, 2.4, 2.3"
+      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.9.1, 2.8.0, 2.7.1","2.9, 2.8, 2.7","2.6, 2.5, 2.4, 2.3"
      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.20.0, 2.19.1, 2.18.1","2.20.0, 2.19.1, 2.18.1","2.18.1, 2.17.1, 2.16.2"
-      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.8.2,0.8.0,0.4.35
+      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.8.0,0.7.1,0.4.35
      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat]_,N/A,N/A,2.4.0
-      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.2,1.23.2,1.20.0
+      :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat]_,N/A,N/A,b5997
      :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>` [#flashinfer_compat]_,N/A,v0.2.5,N/A
      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.23.2,1.23.1,1.20.0
      ,,,
      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
-      `UCC <https://github.com/ROCm/ucc>`_,>=1.6.0,>=1.4.0,>=1.3.0
+      `UCC <https://github.com/ROCm/ucc>`_,>=1.4.0,>=1.4.0,>=1.3.0
      `UCX <https://github.com/ROCm/ucx>`_,>=1.17.0,>=1.17.0,>=1.15.0
      ,,,
      THIRD PARTY ALGORITHM,.. _thirdpartyalgorithm-support-compatibility-matrix:,,
@@ -69,55 +71,55 @@ compatibility and system requirements.
      CUB,2.8.5,2.8.5,2.5.0
      ,,,
      DRIVER & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
-      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.30.1, 30.30.0, 30.20.1, |br| 30.20.0 [#mi325x_KVM]_, 30.10.2, |br| 30.10.1 [#driver_patch]_, 30.10, 6.4.x","30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM]_, |br| 30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
+      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.30.0, 30.20.1, 30.20.0 [#mi325x_KVM]_, |br| 30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x","30.20.1, 30.20.0 [#mi325x_KVM]_, |br| 30.10.2, 30.10.1 [#driver_patch]_, |br| 30.10, 6.4.x","6.4.x, 6.3.x, 6.2.x, 6.1.x"
      ,,,
      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
-      :doc:`Composable Kernel <composable_kernel:index>`,1.2.0,1.2.0,1.1.0
+      :doc:`Composable Kernel <composable_kernel:index>`,1.2.0,1.1.0,1.1.0
-      :doc:`MIGraphX <amdmigraphx:index>`,2.15.0,2.15.0,2.12.0
+      :doc:`MIGraphX <amdmigraphx:index>`,2.15.0,2.14.0,2.12.0
      :doc:`MIOpen <miopen:index>`,3.5.1,3.5.1,3.4.0
-      :doc:`MIVisionX <mivisionx:index>`,3.5.0,3.5.0,3.2.0
+      :doc:`MIVisionX <mivisionx:index>`,3.5.0,3.4.0,3.2.0
-      :doc:`rocAL <rocal:index>`,2.5.0,2.5.0,2.2.0
+      :doc:`rocAL <rocal:index>`,2.5.0,2.4.0,2.2.0
-      :doc:`rocDecode <rocdecode:index>`,1.7.0,1.5.0,0.10.0
+      :doc:`rocDecode <rocdecode:index>`,1.5.0,1.4.0,0.10.0
-      :doc:`rocJPEG <rocjpeg:index>`,1.4.0,1.3.0,0.8.0
+      :doc:`rocJPEG <rocjpeg:index>`,1.3.0,1.2.0,0.8.0
-      :doc:`rocPyDecode <rocpydecode:index>`,0.8.0,0.8.0,0.3.1
+      :doc:`rocPyDecode <rocpydecode:index>`,0.8.0,0.7.0,0.3.1
-      :doc:`RPP <rpp:index>`,2.2.1,2.2.0,1.9.10
+      :doc:`RPP <rpp:index>`,2.2.0,2.1.0,1.9.10
      ,,,
      COMMUNICATION,.. _commlibs-support-compatibility-matrix:,,
      :doc:`RCCL <rccl:index>`,2.27.7,2.27.7,2.22.3
-      :doc:`rocSHMEM <rocshmem:index>`,3.2.0,3.2.0,2.0.0
+      :doc:`rocSHMEM <rocshmem:index>`,3.2.0,3.1.0,2.0.0
      ,,,
      MATH LIBS,.. _mathlibs-support-compatibility-matrix:,,
      `half <https://github.com/ROCm/half>`_ ,1.12.0,1.12.0,1.12.0
-      :doc:`hipBLAS <hipblas:index>`,3.2.0,3.2.0,2.4.0
+      :doc:`hipBLAS <hipblas:index>`,3.2.0,3.1.0,2.4.0
-      :doc:`hipBLASLt <hipblaslt:index>`,1.2.2,1.2.1,0.12.0
+      :doc:`hipBLASLt <hipblaslt:index>`,1.2.1,1.1.0,0.12.0
-      :doc:`hipFFT <hipfft:index>`,1.0.22,1.0.22,1.0.18
+      :doc:`hipFFT <hipfft:index>`,1.0.22,1.0.21,1.0.18
      :doc:`hipfort <hipfort:index>`,0.7.1,0.7.1,0.6.0
      :doc:`hipRAND <hiprand:index>`,3.1.0,3.1.0,2.12.0
-      :doc:`hipSOLVER <hipsolver:index>`,3.2.0,3.2.0,2.4.0
+      :doc:`hipSOLVER <hipsolver:index>`,3.2.0,3.1.0,2.4.0
-      :doc:`hipSPARSE <hipsparse:index>`,4.2.0,4.2.0,3.2.0
+      :doc:`hipSPARSE <hipsparse:index>`,4.2.0,4.1.0,3.2.0
-      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.6,0.2.6,0.2.3
+      :doc:`hipSPARSELt <hipsparselt:index>`,0.2.6,0.2.5,0.2.3
-      :doc:`rocALUTION <rocalution:index>`,4.1.0,4.1.0,3.2.2
+      :doc:`rocALUTION <rocalution:index>`,4.1.0,4.0.1,3.2.2
-      :doc:`rocBLAS <rocblas:index>`,5.2.0,5.2.0,4.4.0
+      :doc:`rocBLAS <rocblas:index>`,5.2.0,5.1.1,4.4.0
-      :doc:`rocFFT <rocfft:index>`,1.0.36,1.0.36,1.0.32
+      :doc:`rocFFT <rocfft:index>`,1.0.36,1.0.35,1.0.32
-      :doc:`rocRAND <rocrand:index>`,4.2.0,4.2.0,3.3.0
+      :doc:`rocRAND <rocrand:index>`,4.2.0,4.1.0,3.3.0
-      :doc:`rocSOLVER <rocsolver:index>`,3.32.0,3.32.0,3.28.0
+      :doc:`rocSOLVER <rocsolver:index>`,3.32.0,3.31.0,3.28.0
-      :doc:`rocSPARSE <rocsparse:index>`,4.2.0,4.2.0,3.4.0
+      :doc:`rocSPARSE <rocsparse:index>`,4.2.0,4.1.0,3.4.0
-      :doc:`rocWMMA <rocwmma:index>`,2.2.0,2.2.0,1.7.0
+      :doc:`rocWMMA <rocwmma:index>`,2.2.0,2.1.0,1.7.0
-      :doc:`Tensile <tensile:src/index>`,4.45.0,4.45.0,4.43.0
+      :doc:`Tensile <tensile:src/index>`,4.44.0,4.44.0,4.43.0
      ,,,
      PRIMITIVES,.. _primitivelibs-support-compatibility-matrix:,,
-      :doc:`hipCUB <hipcub:index>`,4.2.0,4.2.0,3.4.0
+      :doc:`hipCUB <hipcub:index>`,4.2.0,4.1.0,3.4.0
-      :doc:`hipTensor <hiptensor:index>`,2.2.0,2.2.0,1.5.0
+      :doc:`hipTensor <hiptensor:index>`,2.2.0,2.0.0,1.5.0
-      :doc:`rocPRIM <rocprim:index>`,4.2.0,4.2.0,3.4.0
+      :doc:`rocPRIM <rocprim:index>`,4.2.0,4.1.0,3.4.0
-      :doc:`rocThrust <rocthrust:index>`,4.2.0,4.2.0,3.3.0
+      :doc:`rocThrust <rocthrust:index>`,4.2.0,4.1.0,3.3.0
      ,,,
      SUPPORT LIBS,,,
-      `hipother <https://github.com/ROCm/hipother>`_,7.2.53211,7.2.26015,6.4.43482
+      `hipother <https://github.com/ROCm/hipother>`_,7.2.26015,7.1.52802,6.4.43482
-      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.2.1,7.2.0,6.4.0
+      `rocm-core <https://github.com/ROCm/rocm-core>`_,7.2.0,7.1.1,6.4.0
      `ROCT-Thunk-Interface <https://github.com/ROCm/ROCT-Thunk-Interface>`_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_,N/A [#ROCT-rocr]_
      ,,,
      SYSTEM MGMT TOOLS,.. _tools-support-compatibility-matrix:,,
-      :doc:`AMD SMI <amdsmi:index>`,26.2.2,26.2.1,25.3.0
+      :doc:`AMD SMI <amdsmi:index>`,26.2.1,26.2.0,25.3.0
      :doc:`ROCm Data Center Tool <rdc:index>`,1.2.0,1.2.0,0.3.0
      :doc:`rocminfo <rocminfo:index>`,1.0.0,1.0.0,1.0.0
      :doc:`ROCm SMI <rocm_smi_lib:index>`,7.8.0,7.8.0,7.5.0
@@ -125,14 +127,14 @@ compatibility and system requirements.
      ,,,
      PERFORMANCE TOOLS,,,
      :doc:`ROCm Bandwidth Test <rocm_bandwidth_test:index>`,2.6.0,2.6.0,1.4.0
-      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.4.0,3.4.0,3.1.0
+      :doc:`ROCm Compute Profiler <rocprofiler-compute:index>`,3.4.0,3.3.1,3.1.0
-      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.3.0,1.3.0,1.0.0
+      :doc:`ROCm Systems Profiler <rocprofiler-systems:index>`,1.3.0,1.2.1,1.0.0
-      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70201,2.0.70200,2.0.60400
+      :doc:`ROCProfiler <rocprofiler:index>`,2.0.70200,2.0.70101,2.0.60400
-      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.1.0,1.1.0,0.6.0
+      :doc:`ROCprofiler-SDK <rocprofiler-sdk:index>`,1.1.0,1.0.0,0.6.0
-      :doc:`ROCTracer <roctracer:index>`,4.1.70201,4.1.70200,4.1.60400
+      :doc:`ROCTracer <roctracer:index>`,4.1.70200,4.1.70101,4.1.60400
      ,,,
      DEVELOPMENT TOOLS,,,
-      :doc:`HIPIFY <hipify:index>`,22.0.0,22.0.0,19.0.0
+      :doc:`HIPIFY <hipify:index>`,22.0.0,20.0.0,19.0.0
      :doc:`ROCm CMake <rocmcmakebuildtools:index>`,0.14.0,0.14.0,0.14.0
      :doc:`ROCdbgapi <rocdbgapi:index>`,0.77.4,0.77.4,0.77.2
      :doc:`ROCm Debugger (ROCgdb) <rocgdb:index>`,16.3.0,16.3.0,15.2.0
@@ -142,22 +144,24 @@ compatibility and system requirements.
      COMPILERS,.. _compilers-support-compatibility-matrix:,,
      `clang-ocl <https://github.com/ROCm/clang-ocl>`_,N/A,N/A,N/A
      :doc:`hipCC <hipcc:index>`,1.1.1,1.1.1,1.1.1
-      `Flang <https://github.com/ROCm/flang>`_,22.0.0.26084,22.0.0.26014,19.0.0.25133
+      `Flang <https://github.com/ROCm/flang>`_,22.0.0.26014,20.0.025444,19.0.0.25133
-      :doc:`llvm-project <llvm-project:index>`,22.0.0.26084,22.0.0.26014,19.0.0.25133
+      :doc:`llvm-project <llvm-project:index>`,22.0.0.26014,20.0.025444,19.0.0.25133
-      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,22.0.0.26084,22.0.0.26014,19.0.0.25133
+      `OpenMP <https://github.com/ROCm/llvm-project/tree/amd-staging/openmp>`_,22.0.0.26014,20.0.025444,19.0.0.25133
      ,,,
      RUNTIMES,.. _runtime-support-compatibility-matrix:,,
-      :doc:`AMD CLR <hip:understand/amd_clr>`,7.2.53211,7.2.26015,6.4.43482
+      :doc:`AMD CLR <hip:understand/amd_clr>`,7.2.26015,7.1.52802,6.4.43482
-      :doc:`HIP <hip:index>`,7.2.53211,7.2.26015,6.4.43482
+      :doc:`HIP <hip:index>`,7.2.26015,7.1.52802,6.4.43482
      `OpenCL Runtime <https://github.com/ROCm/clr/tree/develop/opencl>`_,2.0.0,2.0.0,2.0.0
      :doc:`ROCr Runtime <rocr-runtime:index>`,1.18.0,1.18.0,1.15.0
 .. rubric:: Footnotes
-.. [#os-compatibility] Some operating systems are supported on specific GPUs. For detailed information about operating systems supported on ROCm 7.2.1, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.2.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.2.0/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.
+.. [#os-compatibility] Some operating systems are supported on specific GPUs. For detailed information about operating systems supported on ROCm 7.2.0, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.
-.. [#gpu-compatibility] Some GPUs have limited operating system support. For detailed information about GPUs supporting ROCm 7.2.1, see the latest :ref:`supported_GPUs`. For version specific information, see `ROCm 7.2.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.2.0/reference/system-requirements.html#supported-gpus>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-gpus>`__.
+.. [#gpu-compatibility] Some GPUs have limited operating system support. For detailed information about GPUs supporting ROCm 7.2.0, see the latest :ref:`supported_GPUs`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-gpus>`__, `ROCm 7.1.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.0/reference/system-requirements.html#supported-gpus>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-gpus>`__.
-.. [#dgl_compat] DGL is supported only on ROCm 7.0.0, ROCm 6.4.3, and ROCm 6.4.0.
+.. [#dgl_compat] DGL is only supported on ROCm 7.0.0, 6.4.3 and 6.4.0.
 .. [#llama-cpp_compat] llama.cpp is only supported on ROCm 7.0.0 and 6.4.x.
 .. [#flashinfer_compat] FlashInfer is only supported on ROCm 7.1.1 and 6.4.1.
 .. [#mi325x_KVM] For AMD Instinct MI325X KVM SR-IOV users, do not use AMD GPU Driver (amdgpu) 30.20.0.
 .. [#driver_patch] AMD GPU Driver (amdgpu) 30.10.1 is a quality release that resolves an issue identified in the 30.10 release. There are no other significant changes or feature additions in ROCm 7.0.1 from ROCm 7.0.0. AMD GPU Driver (amdgpu) 30.10.1 is compatible with ROCm 7.0.1 and ROCm 7.0.0.
 .. [#kfd_support] As of ROCm 6.4.0, forward and backward compatibility between the AMD GPU Driver (amdgpu) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and AMD GPU Driver support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
@@ -168,13 +172,12 @@ compatibility and system requirements.
 Operating systems, kernel and Glibc versions
 *********************************************
-For detailed information on operating system supported on ROCm 7.2.1 and associated Kernel and Glibc version, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.2.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.2.0/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.
+For detailed information on operating system supported on ROCm 7.2.0 and associated Kernel and Glibc version, see the latest :ref:`supported_distributions`. For version specific information, see `ROCm 7.1.1 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-7.1.1/reference/system-requirements.html#supported-operating-systems>`__, and `ROCm 6.4.0 <https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.0/reference/system-requirements.html#supported-operating-systems>`__.
 .. note::
  * See `Red Hat Enterprise Linux Release Dates <https://access.redhat.com/articles/3078>`_ to learn about the specific kernel versions supported on Red Hat Enterprise Linux (RHEL).
  * See `List of SUSE Linux Enterprise Server kernel <https://www.suse.com/support/kb/doc/?id=000019587>`_ to learn about the specific kernel version supported on SUSE Linux Enterprise Server (SLES).
 ..
   Footnotes and ref anchors in below historical tables should be appended with "-past-60", to differentiate from the
   footnote references in the above, latest, compatibility matrix.  It also allows to easily find & replace.
@@ -182,6 +185,7 @@ For detailed information on operating system supported on ROCm 7.2.1 and associa
   delete the columns you don't need, to build the current compatibility matrix to use in above table.  Find & replace all
   instances of "-past-60" to make it ready for above table.
 .. _past-rocm-compatibility-matrix:
 Past versions of ROCm compatibility matrix
@@ -203,7 +207,13 @@ Expand for full historical view of:
   .. [#os-compatibility-past-60] Some operating systems are supported on specific GPUs. For detailed information, see :ref:`supported_distributions` and select the required ROCm version for version specific support.
   .. [#gpu-compatibility-past-60] Some GPUs have limited operating system support. For detailed information, see :ref:`supported_GPUs` and select the required ROCm version for version specific support.
   .. [#tf-mi350-past-60] TensorFlow 2.17.1 is not supported on AMD Instinct MI350 Series GPUs. Use TensorFlow 2.19.1 or 2.18.1 with MI350 Series GPUs instead.
-   .. [#dgl_compat-past-60] DGL is supported only on ROCm 7.0.0, ROCm 6.4.3, and ROCm 6.4.0.
+   .. [#verl_compat-past-60] verl is only supported on ROCm 7.0.0 and 6.2.0.
   .. [#stanford-megatron-lm_compat-past-60] Stanford Megatron-LM is only supported on ROCm 6.3.0.
   .. [#dgl_compat-past-60] DGL is only supported on ROCm 7.0.0, 6.4.3 and 6.4.0.
   .. [#megablocks_compat-past-60] Megablocks is only supported on ROCm 6.3.0.
   .. [#ray_compat-past-60] Ray is only supported on ROCm 7.0.0 and 6.4.1.
   .. [#llama-cpp_compat-past-60] llama.cpp is only supported on ROCm 7.0.0 and 6.4.x.
   .. [#flashinfer_compat-past-60] FlashInfer is only supported on ROCm 7.1.1 and 6.4.1.
   .. [#mi325x_KVM-past-60] For AMD Instinct MI325X KVM SR-IOV users, do not use AMD GPU Driver (amdgpu) 30.20.0.
   .. [#driver_patch-past-60] AMD GPU Driver (amdgpu) 30.10.1 is a quality release that resolves an issue identified in the 30.10 release. There are no other significant changes or feature additions in ROCm 7.0.1 from ROCm 7.0.0. AMD GPU Driver (amdgpu) 30.10.1 is compatible with ROCm 7.0.1 and ROCm 7.0.0.
   .. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD GPU Driver (amdgpu) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The supported user space versions on this page were accurate as of the time of initial ROCm release. For the most up-to-date information, see the latest version of this information at `User and AMD GPU Driver support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
--- a/docs/compatibility/ml-compatibility/flashinfer-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/flashinfer-compatibility.rst
@@ -0,0 +1,113 @@
 :orphan:
 .. meta::
    :description: FlashInfer compatibility
    :keywords: GPU, LLM, FlashInfer, deep learning, framework compatibility
 .. version-set:: rocm_version latest
 ********************************************************************************
 FlashInfer compatibility
 ********************************************************************************
 `FlashInfer <https://docs.flashinfer.ai/index.html>`__ is a library and kernel generator 
 for Large Language Models (LLMs) that provides a high-performance implementation of graphics 
 processing units (GPUs) kernels. FlashInfer focuses on LLM serving and inference, as well 
 as advanced performance across diverse scenarios.
 FlashInfer features highly efficient attention kernels, load-balanced scheduling, and memory-optimized 
 techniques, while supporting customized attention variants. It’s compatible with ``torch.compile``, and 
 offers high-performance LLM-specific operators, with easy integration through PyTorch, and C++ APIs.
 .. note::
  The ROCm port of FlashInfer is under active development, and some features are not yet available. 
  For the latest feature compatibility matrix, refer to the ``README`` of the 
  `https://github.com/ROCm/flashinfer <https://github.com/ROCm/flashinfer>`__ repository.
 Support overview
 ================================================================================
 - The ROCm-supported version of FlashInfer is maintained in the official `https://github.com/ROCm/flashinfer 
  <https://github.com/ROCm/flashinfer>`__ repository, which differs from the 
  `https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`__ 
  upstream repository.
 - To get started and install FlashInfer on ROCm, use the prebuilt :ref:`Docker images <flashinfer-docker-compat>`, 
  which include ROCm, FlashInfer, and all required dependencies.
  - See the :doc:`ROCm FlashInfer installation guide <rocm-install-on-linux:install/3rd-party/flashinfer-install>` 
    for installation and setup instructions.
  - You can also consult the upstream `Installation guide <https://docs.flashinfer.ai/installation.html>`__ 
    for additional context.
 .. _flashinfer-docker-compat:
 Compatibility matrix
 ================================================================================
 .. |docker-icon| raw:: html
   <i class="fab fa-docker"></i>
 AMD validates and publishes `FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tag and associated
 inventories represent the latest available FlashInfer version from the official Docker Hub. 
 Click |docker-icon| to view the image on Docker Hub.
 .. list-table:: 
    :header-rows: 1
    :class: docker-image-compatibility
    * - Docker image
      - ROCm
      - FlashInfer
      - PyTorch
      - Ubuntu
      - Python
      - GPU
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/flashinfer/flashinfer-0.2.5.amd2_rocm7.1.1_ubuntu24.04_py3.12_pytorch2.8/images/sha256-9ab6426750a11dbab9bcddeaccaf492683bfd96a1d60b21dd9fc3a609a98175b"><i class="fab fa-docker fa-lg"></i> rocm/flashinfer</a>
      - `7.1.1 <https://repo.radeon.com/rocm/apt/7.1.1/>`__
      - `v0.2.5 <https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.2.5>`__
      - `2.8.0 <https://github.com/ROCm/pytorch/releases/tag/v2.8.0>`__
      - 24.04
      - `3.12 <https://www.python.org/downloads/release/python-3129/>`__
      - MI325X, MI300X
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/flashinfer/flashinfer-0.2.5_rocm6.4_ubuntu24.04_py3.12_pytorch2.7/images/sha256-558914838821c88c557fb6d42cfbc1bdb67d79d19759f37c764a9ee801f93313"><i class="fab fa-docker fa-lg"></i> rocm/flashinfer</a>
      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
      - `v0.2.5 <https://github.com/flashinfer-ai/flashinfer/releases/tag/v0.2.5>`__
      - `2.7.1 <https://github.com/ROCm/pytorch/releases/tag/v2.7.1>`__
      - 24.04
      - `3.12 <https://www.python.org/downloads/release/python-3129/>`__
      - MI300X
 .. _flashinfer-recommendations:
 Use cases and recommendations
 ================================================================================
 FlashInfer on ROCm enables you to perform LLM inference for both prefill and decode:
 during prefill, your model efficiently processes input prompts to build KV caches
 and internal activations; during decode, it generates tokens sequentially based on
 prior outputs and context. Use the attention mode supported upstream (Multi-Head
 Attention, Grouped-Query Attention, or Multi-Query Attention) that matches your
 model configuration.
 FlashInfer on ROCm also includes capabilities such as load balancing, 
 sparse and dense attention optimizations, and single and batch decode, alongside
 prefill for high‑performance execution on MI300X GPUs.
 For currently supported use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/search.html?q=flashinfer>`__, 
 where you can search for examples and best practices to optimize your workloads on AMD GPUs.
 Previous versions
 ===============================================================================
 See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/flashinfer-history` to find documentation for previous releases
 of the ``ROCm/flashinfer`` Docker image.
--- a/docs/compatibility/ml-compatibility/jax-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/jax-compatibility.rst
@@ -56,15 +56,15 @@ between JAX Plugin–PJRT and JAX/JAXLIB.
    * - JAX Plugin-PJRT
      - JAX/JAXLIB
      - ROCm
    * - 0.8.2
      - 0.8.2
      - 7.2.1
    * - 0.8.0
      - 0.8.0
      - 7.2.0
    * - 0.7.1
      - 0.7.1
      - 7.1.1, 7.1.0
    * - 0.6.0
      - 0.6.2, 0.6.0
      - 7.0.2, 7.0.1, 7.0.0
 Use cases and recommendations
 ================================================================================
--- a/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst
@@ -0,0 +1,275 @@
 :orphan:
 .. meta::
    :description: llama.cpp compatibility
    :keywords: GPU, GGML, llama.cpp, deep learning, framework compatibility
 .. version-set:: rocm_version latest
 ********************************************************************************
 llama.cpp compatibility
 ********************************************************************************
 `llama.cpp <https://github.com/ggml-org/llama.cpp>`__ is an open-source framework 
 for Large Language Model (LLM) inference that runs on both central processing units 
 (CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing 
 a simple, dependency-free setup. 
 The framework supports multiple quantization options, from 1.5-bit to 8-bit integers, 
 to accelerate inference and reduce memory usage. Originally built as a CPU-first library, 
 llama.cpp is easy to integrate with other programming environments and is widely 
 adopted across diverse platforms, including consumer devices. 
 Support overview
 ================================================================================
 - The ROCm-supported version of llama.cpp is maintained in the official `https://github.com/ROCm/llama.cpp 
  <https://github.com/ROCm/llama.cpp>`__ repository, which differs from the 
  `https://github.com/ggml-org/llama.cpp <https://github.com/ggml-org/llama.cpp>`__ upstream repository.
 - To get started and install llama.cpp on ROCm, use the prebuilt :ref:`Docker images <llama-cpp-docker-compat>`, 
  which include ROCm, llama.cpp, and all required dependencies.
  - See the :doc:`ROCm llama.cpp installation guide <rocm-install-on-linux:install/3rd-party/llama-cpp-install>` 
    for installation and setup instructions.
  - You can also consult the upstream `Installation guide <https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md>`__ 
    for additional context.
 .. _llama-cpp-docker-compat:
 Compatibility matrix
 ================================================================================
 .. |docker-icon| raw:: html
   <i class="fab fa-docker"></i>
 AMD validates and publishes `llama.cpp images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tags and associated
 inventories represent the latest available llama.cpp versions from the official Docker Hub.
 Click |docker-icon| to view the image on Docker Hub.
 .. important::
   Tag endings of ``_full``, ``_server``, and ``_light`` serve different purposes for entrypoints as follows:
   - Full: This image includes both the main executable file and the tools to convert ``LLaMA`` models into ``ggml`` and convert into 4-bit quantization.
   - Server: This image only includes the server executable file.
   - Light: This image only includes the main executable file.
 .. list-table::
    :header-rows: 1
    :class: docker-image-compatibility
    * - Full Docker
      - Server Docker
      - Light Docker
      - llama.cpp
      - ROCm
      - Ubuntu
      - GPU
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6652.amd0_rocm7.0.0_ubuntu24.04_full/images/sha256-a94f0c7a598cc6504ff9e8371c016d7a2f93e69bf54a36c870f9522567201f10g"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6652.amd0_rocm7.0.0_ubuntu24.04_server/images/sha256-be175932c3c96e882dfbc7e20e0e834f58c89c2925f48b222837ee929dfc47ee"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6652.amd0_rocm7.0.0_ubuntu24.04_light/images/sha256-d8ba0c70603da502c879b1f8010b439c8e7fa9f6cbdac8bbbbbba97cb41ebc9e"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6652 <https://github.com/ROCm/llama.cpp/tree/release/b6652>`__
      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
      - 24.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6652.amd0_rocm7.0.0_ubuntu22.04_full/images/sha256-37582168984f25dce636cc7288298e06d94472ea35f65346b3541e6422b678ee"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6652.amd0_rocm7.0.0_ubuntu22.04_server/images/sha256-7e70578e6c3530c6591cc2c26da24a9ee68a20d318e12241de93c83224f83720"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6652.amd0_rocm7.0.0_ubuntu22.04_light/images/sha256-9a5231acf88b4a229677bc2c636ea3fe78a7a80f558bd80910b919855de93ad5"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6652 <https://github.com/ROCm/llama.cpp/tree/release/b6652>`__
      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
      - 22.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_full/images/sha256-5960fc850024a8a76451f9eaadd89b7e59981ae9f393b407310c1ddf18892577"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_server/images/sha256-1b79775d9f546065a6aaf9ca426e1dd4ed4de0b8f6ee83687758cc05af6538e6"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu24.04_light/images/sha256-8f863c4c2857ae42bebd64e4f1a0a1e7cc3ec4503f243e32b4a4dcad070ec361"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
      - `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
      - 24.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_full/images/sha256-888879b3ee208f9247076d7984524b8d1701ac72611689e89854a1588bec9867"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_server/images/sha256-90e4ff99a66743e33fd00728cd71a768588e5f5ef355aaa196669fe65ac70672"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.3_ubuntu22.04_light/images/sha256-bd447a049939cb99054f8fbf3f2352870fe906a75e2dc3339c845c08b9c53f9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
      - `6.4.3 <https://repo.radeon.com/rocm/apt/6.4.3/>`__
      - 22.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_full/images/sha256-5b3a1bc4889c1fcade434b937fbf9cc1c22ff7dc0317c130339b0c9238bc88c4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_server/images/sha256-5228ff99d0f627a9032d668f4381b2e80dc1e301adc3e0821f26d8354b175271"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu24.04_light/images/sha256-b12723b332a826a89b7252dddf868cbe4d1a869562fc4aa4032f59e1a683b968"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
      - `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
      - 24.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_full/images/sha256-cd6e21a6a73f59b35dd5309b09dd77654a94d783bf13a55c14eb8dbf8e9c2615"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_server/images/sha256-c2b4689ab2c47e6626e8fea22d7a63eb03d47c0fde9f5ef8c9f158d15c423e58"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.2_ubuntu22.04_light/images/sha256-1acc28f29ed87db9cbda629cb29e1989b8219884afe05f9105522be929e94da4"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
      - `6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__
      - 22.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_full/images/sha256-2f8ae8a44510d96d52dea6cb398b224f7edeb7802df7ec488c6f63d206b3cdc9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_server/images/sha256-fece497ff9f4a28b12f645de52766941da8ead8471aa1ea84b61d4b4568e51f2"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu24.04_light/images/sha256-3e14352fa6f8c6128b23cf9342531c20dbfb522550b626e09d83b260a1947022"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
      - 24.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_full/images/sha256-80763062ef0bec15038c35fd01267f1fc99a5dd171d4b48583cc668b15efad69"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_server/images/sha256-db2a6c957555ed83b819bbc54aea884a93192da0fb512dae63d32e0dc4e8ab8f"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm6.4.1_ubuntu22.04_light/images/sha256-c6dbb07cc655fb079d5216e4b77451cb64a9daa0585d23b6fb8b32cb22021197"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
      - 22.04
      - MI325X, MI300X, MI210
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_full/images/sha256-f78f6c81ab2f8e957469415fe2370a1334fe969c381d1fe46050c85effaee9d5"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_server/images/sha256-275ad9e18f292c26a00a2de840c37917e98737a88a3520bdc35fd3fc5c9a6a9b"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b5997_rocm6.4.0_ubuntu24.04_light/images/sha256-cc324e6faeedf0e400011f07b49d2dc41a16bae257b2b7befa0f4e2e97231320"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
      - `b5997 <https://github.com/ROCm/llama.cpp/tree/release/b5997>`__
      - `6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__
      - 24.04
      - MI300X, MI210
 .. _llama-cpp-key-rocm-libraries:
 Key ROCm libraries for llama.cpp
 ================================================================================
 llama.cpp functionality on ROCm is determined by its underlying library
 dependencies. These ROCm components affect the capabilities, performance, and
 feature set available to developers. Ensure you have the required libraries for 
 your corresponding ROCm version.
 .. list-table::
    :header-rows: 1
    * - ROCm library
      - ROCm 7.0.0 version
      - ROCm 6.4.x version
      - Purpose
      - Usage
    * - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
      - 3.0.0
      - 2.4.0
      - Provides GPU-accelerated Basic Linear Algebra Subprograms (BLAS) for
        matrix and vector operations.
      - Supports operations such as matrix multiplication, matrix-vector
        products, and tensor contractions. Utilized in both dense and batched
        linear algebra operations.
    * - `hipBLASLt <https://github.com/ROCm/hipBLASLt>`__
      - 1.0.0
      - 0.12.0
      - hipBLASLt is an extension of the hipBLAS library, providing additional
        features like epilogues fused into the matrix multiplication kernel or
        use of integer tensor cores.
      - By setting the flag ``ROCBLAS_USE_HIPBLASLT``, you can dispatch hipblasLt
        kernels where possible.
    * - `rocWMMA <https://github.com/ROCm/rocWMMA>`__
      - 2.0.0
      - 1.7.0
      - Accelerates warp-level matrix-multiply and matrix-accumulate to speed up matrix
        multiplication (GEMM) and accumulation operations with mixed precision
        support.
      - Can be used to enhance the flash attention performance on AMD compute, by enabling
        the flag during compile time.
 .. _llama-cpp-uses-recommendations:
 Use cases and recommendations
 ================================================================================
 llama.cpp can be applied in a variety of scenarios, particularly when you need to meet one or more of the following requirements:
 - Plain C/C++ implementation with no external dependencies
 - Support for 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory usage
 - Custom HIP (Heterogeneous-compute Interface for Portability) kernels for running large language models (LLMs) on AMD GPUs (graphics processing units)
 - CPU (central processing unit) + GPU (graphics processing unit) hybrid inference for partially accelerating models larger than the total available VRAM (video random-access memory)
 llama.cpp is also used in a range of real-world applications, including:
 - Games such as `Lucy's Labyrinth <https://github.com/MorganRO8/Lucys_Labyrinth>`__:
  A simple maze game where AI-controlled agents attempt to trick the player.
 - Tools such as `Styled Lines <https://marketplace.unity.com/packages/tools/ai-ml-integration/style-text-webgl-ios-stand-alone-llm-llama-cpp-wrapper-292902>`__:
  A proprietary, asynchronous inference wrapper for Unity3D game development, including pre-built mobile and web platform wrappers and a model example.
 - Various other AI applications use llama.cpp as their inference engine;  
  for a detailed list, see the `user interfaces (UIs) section <https://github.com/ggml-org/llama.cpp?tab=readme-ov-file#description>`__.
 For more use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
 where you can search for llama.cpp examples and best practices to optimize your workloads on AMD GPUs.
 - The `Llama.cpp Meets Instinct: A New Era of Open-Source AI Acceleration <https://rocm.blogs.amd.com/ecosystems-and-partners/llama-cpp/README.html>`__ 
  blog post outlines how the open-source llama.cpp framework enables efficient LLM inference—including interactive inference with ``llama-cli``, 
  server deployment with ``llama-server``, GGUF model preparation and quantization, performance benchmarking, and optimizations tailored for 
  AMD Instinct GPUs within the ROCm ecosystem. 
 Previous versions
 ===============================================================================
 See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/llama-cpp-history` to find documentation for previous releases
 of the ``ROCm/llama.cpp`` Docker image.
--- a/docs/compatibility/ml-compatibility/megablocks-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/megablocks-compatibility.rst
@@ -0,0 +1,104 @@
 :orphan:
 .. meta::
    :description: Megablocks compatibility
    :keywords: GPU, megablocks, deep learning, framework compatibility
 .. version-set:: rocm_version latest
 ********************************************************************************
 Megablocks compatibility
 ********************************************************************************
 `Megablocks <https://github.com/databricks/megablocks>`__ is a lightweight library 
 for mixture-of-experts `(MoE) <https://huggingface.co/blog/moe>`__ training. 
 The core of the system is efficient "dropless-MoE" and standard MoE layers. 
 Megablocks is integrated with `https://github.com/stanford-futuredata/Megatron-LM 
 <https://github.com/stanford-futuredata/Megatron-LM>`__, 
 where data and pipeline parallel training of MoEs is supported.
 Support overview
 ================================================================================
 - The ROCm-supported version of Megablocks is maintained in the official `https://github.com/ROCm/megablocks 
  <https://github.com/ROCm/megablocks>`__ repository, which differs from the 
  `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`__ upstream repository.
 - To get started and install Megablocks on ROCm, use the prebuilt :ref:`Docker image <megablocks-docker-compat>`, 
  which includes ROCm, Megablocks, and all required dependencies.
  - See the :doc:`ROCm Megablocks installation guide <rocm-install-on-linux:install/3rd-party/megablocks-install>` 
    for installation and setup instructions.
  - You can also consult the upstream `Installation guide <https://github.com/databricks/megablocks>`__ 
    for additional context.
 .. _megablocks-docker-compat:
 Compatibility matrix
 ================================================================================
 .. |docker-icon| raw:: html
   <i class="fab fa-docker"></i>
 AMD validates and publishes `Megablocks images <https://hub.docker.com/r/rocm/megablocks/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tag and associated
 inventories represent the latest available Megablocks version from the official Docker Hub. 
 Click |docker-icon| to view the image on Docker Hub.
 .. list-table:: 
    :header-rows: 1
    :class: docker-image-compatibility
    * - Docker image
      - ROCm
      - Megablocks
      - PyTorch
      - Ubuntu
      - Python
      - GPU
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/megablocks/megablocks-0.7.0_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-372ff89b96599019b8f5f9db469c84add2529b713456781fa62eb9a148659ab4"><i class="fab fa-docker fa-lg"></i> rocm/megablocks</a>
      - `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
      - `0.7.0 <https://github.com/databricks/megablocks/releases/tag/v0.7.0>`_
      - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
      - 24.04
      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
      - MI300X
 Supported models and features with ROCm 6.3.0
 ================================================================================
 This section summarizes the Megablocks features supported by ROCm.
 * Distributed Pre-training
 * Activation Checkpointing and Recomputation
 * Distributed Optimizer
 * Mixture-of-Experts
 * dropless-Mixture-of-Experts
 .. _megablocks-recommendations:
 Use cases and recommendations
 ================================================================================
 * The `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs 
  <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`__ 
  blog post guides how to leverage the ROCm platform for pre-training using the 
  Megablocks framework. It introduces a streamlined approach for training Mixture-of-Experts 
  (MoE) models using the Megablocks library on AMD hardware. Focusing on GPT-2, it 
  demonstrates how block-sparse computations can enhance scalability and efficiency in MoE 
  training. The guide provides step-by-step instructions for setting up the environment, 
  including cloning the repository, building the Docker image, and running the training container. 
  Additionally, it offers insights into utilizing the ``oscar-1GB.json`` dataset for pre-training 
  language models. By leveraging Megablocks and the ROCm platform, you can optimize your MoE 
  training workflows for large-scale transformer models.
 It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
 * Single-GPU pre-training
 * Multi-GPU pre-training
--- a/docs/compatibility/ml-compatibility/pytorch-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/pytorch-compatibility.rst
@@ -399,20 +399,6 @@ with ROCm.
        **Note:** Only official release exists.
 Key features and enhancements for PyTorch 2.9 with ROCm 7.2.1
 ================================================================================
 - Added Triton 3.6.x performance optimization for reduction, POI, and GEMM kernels.
 - Updated native reduction kernel config for better performance on AMD GPUs.
 - Optimized single-block TopK kernels with warp-level compaction.
 - Optimized Radix Select by caching data on shared memory.
 - Optimized Flex-Attention occupancy for head_dim=128.
 - Enabled hipSOLVER path for linalg operations - cholesky, lstsq, and gels.
 Key features and enhancements for PyTorch 2.9 with ROCm 7.1.1
 ================================================================================
 - Scaled Dot Product Attention (SDPA) upgraded to use AOTriton version 0.11b.
--- a/docs/compatibility/ml-compatibility/ray-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/ray-compatibility.rst
@@ -0,0 +1,114 @@
 :orphan:
 .. meta::
    :description: Ray compatibility
    :keywords: GPU, Ray, deep learning, framework compatibility
 .. version-set:: rocm_version latest
 *******************************************************************************
 Ray compatibility
 *******************************************************************************
 Ray is a unified framework for scaling AI and Python applications from your laptop 
 to a full cluster, without changing your code. Ray consists of `a core distributed 
 runtime  <https://docs.ray.io/en/latest/ray-core/walkthrough.html>`__ and a set of 
 `AI libraries <https://docs.ray.io/en/latest/ray-air/getting-started.html>`__ for 
 simplifying machine learning computations.
 Ray is a general-purpose framework that runs many types of workloads efficiently. 
 Any Python application can be scaled with Ray, without extra infrastructure.
 Support overview
 ================================================================================
 - The ROCm-supported version of Ray is maintained in the official `https://github.com/ROCm/ray 
  <https://github.com/ROCm/ray>`__ repository, which differs from the 
  `https://github.com/ray-project/ray <https://github.com/ray-project/ray>`__ upstream repository.
 - To get started and install Ray on ROCm, use the prebuilt :ref:`Docker image <ray-docker-compat>`, 
  which includes ROCm, Ray, and all required dependencies.
  - See the :doc:`ROCm Ray installation guide <rocm-install-on-linux:install/3rd-party/ray-install>`
    for installation and setup instructions.
  - You can also consult the upstream `Installation guide <https://docs.ray.io/en/latest/ray-overview/installation.html>`__ 
    for additional context.
 .. _ray-docker-compat:
 Compatibility matrix
 ================================================================================
 .. |docker-icon| raw:: html
   <i class="fab fa-docker"></i>
 AMD validates and publishes `ROCm Ray Docker images <https://hub.docker.com/r/rocm/ray/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tags and
 associated inventories represent the latest Ray version from the official Docker Hub.
 Click |docker-icon| to view the image on Docker Hub.
 .. list-table::
    :header-rows: 1
    :class: docker-image-compatibility
    * - Docker image
      - ROCm
      - Ray
      - Pytorch
      - Ubuntu
      - Python
      - GPU
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/ray/ray-2.51.1_rocm7.0.0_ubuntu22.04_py3.12_pytorch2.9.0/images/sha256-a02f6766b4ba406f88fd7e85707ec86c04b569834d869a08043ec9bcbd672168"><i class="fab fa-docker fa-lg"></i> rocm/ray</a>
      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
      - `2.51.1 <https://github.com/ROCm/ray/tree/release/2.51.1>`__
      - 2.9.0a0+git1c57644
      - 22.04
      - `3.12.12 <https://www.python.org/downloads/release/python-31212/>`__
      - MI300X
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/ray/ray-2.48.0.post0_rocm6.4.1_ubuntu24.04_py3.12_pytorch2.6.0/images/sha256-0d166fe6bdced38338c78eedfb96eff92655fb797da3478a62dd636365133cc0"><i class="fab fa-docker fa-lg"></i> rocm/ray</a>
      - `6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__
      - `2.48.0.post0 <https://github.com/ROCm/ray/tree/release/2.48.0.post0>`__
      - 2.6.0+git684f6f2
      - 24.04
      - `3.12.10 <https://www.python.org/downloads/release/python-31210/>`__
      - MI300X, MI210
 Use cases and recommendations
 ================================================================================
 * The `Reinforcement Learning from Human Feedback on AMD GPUs with verl and ROCm 
  Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__  
  blog provides an overview of Volcano Engine Reinforcement Learning (verl) 
  for large language models (LLMs) and discusses its benefits in large-scale 
  reinforcement learning from human feedback (RLHF). It uses Ray as part of a 
  hybrid orchestration engine to schedule and coordinate training and inference 
  tasks in parallel, enabling optimized resource utilization and potential overlap 
  between these phases. This dynamic resource allocation strategy significantly 
  improves overall system efficiency. The blog presents verl’s performance results, 
  focusing on throughput and convergence accuracy achieved on AMD Instinct™ MI300X 
  GPUs. Follow this guide to get started with verl on AMD Instinct GPUs and 
  accelerate your RLHF training with ROCm-optimized performance.
 * The `Exploring Use Cases for Scalable AI: Implementing Ray with ROCm Support for Efficient ML Workflows 
  <https://rocm.blogs.amd.com/artificial-intelligence/rocm-ray/README.html>`__
  blog post describes key use cases such as training and inference for large language models (LLMs), 
  model serving, hyperparameter tuning, reinforcement learning, and the orchestration of large-scale 
  workloads using Ray in the ROCm environment.
 For more use cases and recommendations, see the AMD GPU tabs in the `Accelerator Support 
 topic <https://docs.ray.io/en/latest/ray-core/scheduling/accelerators.html#accelerator-support>`__ 
 of the Ray core documentation and refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
 where you can search for Ray examples and best practices to optimize your workloads on AMD GPUs.
 Previous versions
 ===============================================================================
 See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/ray-history` to find documentation for previous releases
 of the ``ROCm/ray`` Docker image.
--- a/docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
@@ -0,0 +1,116 @@
 :orphan:
 .. meta::
    :description: Stanford Megatron-LM compatibility
    :keywords: Stanford, Megatron-LM, deep learning, framework compatibility
 .. version-set:: rocm_version latest
 ********************************************************************************
 Stanford Megatron-LM compatibility
 ********************************************************************************
 Stanford Megatron-LM is a large-scale language model training framework developed 
 by NVIDIA at `https://github.com/NVIDIA/Megatron-LM <https://github.com/NVIDIA/Megatron-LM>`_. 
 It is designed to train massive transformer-based language models efficiently by model 
 and data parallelism. 
 It provides efficient tensor, pipeline, and sequence-based model parallelism for 
 pre-training transformer-based language models such as GPT (Decoder Only), BERT 
 (Encoder Only), and T5 (Encoder-Decoder). 
 Support overview
 ================================================================================
 - The ROCm-supported version of Stanford Megatron-LM is maintained in the official `https://github.com/ROCm/Stanford-Megatron-LM 
  <https://github.com/ROCm/Stanford-Megatron-LM>`__ repository, which differs from the 
  `https://github.com/stanford-futuredata/Megatron-LM <https://github.com/stanford-futuredata/Megatron-LM>`__ upstream repository.
 - To get started and install Stanford Megatron-LM on ROCm, use the prebuilt :ref:`Docker image <megatron-lm-docker-compat>`, 
  which includes ROCm, Stanford Megatron-LM, and all required dependencies.
  - See the :doc:`ROCm Stanford Megatron-LM installation guide <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>` 
    for installation and setup instructions.
  - You can also consult the upstream `Installation guide <https://github.com/NVIDIA/Megatron-LM>`__ 
    for additional context.
 .. _megatron-lm-docker-compat:
 Compatibility matrix
 ================================================================================
 .. |docker-icon| raw:: html
   <i class="fab fa-docker"></i>
 AMD validates and publishes `Stanford Megatron-LM images <https://hub.docker.com/r/rocm/stanford-megatron-lm/tags>`_
 with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
 inventories represent the latest Stanford Megatron-LM version from the official Docker Hub.
 Click |docker-icon| to view the image on Docker Hub.
 .. list-table:: 
    :header-rows: 1
    :class: docker-image-compatibility
    * - Docker image
      - ROCm
      - Stanford Megatron-LM
      - PyTorch
      - Ubuntu
      - Python
      - GPU
    * - .. raw:: html
           <a href="https://hub.docker.com/layers/rocm/stanford-megatron-lm/stanford-megatron-lm85f95ae_rocm6.3.0_ubuntu24.04_py3.12_pytorch2.4.0/images/sha256-070556f078be10888a1421a2cb4f48c29f28b02bfeddae02588d1f7fc02a96a6"><i class="fab fa-docker fa-lg"></i> rocm/stanford-megatron-lm</a>
      - `6.3.0 <https://repo.radeon.com/rocm/apt/6.3/>`_
      - `85f95ae <https://github.com/stanford-futuredata/Megatron-LM/commit/85f95aef3b648075fe6f291c86714fdcbd9cd1f5>`_
      - `2.4.0 <https://github.com/ROCm/pytorch/tree/release/2.4>`_
      - 24.04
      - `3.12.9 <https://www.python.org/downloads/release/python-3129/>`_
      - MI300X
 Supported models and features with ROCm 6.3.0
 ================================================================================
 This section details models & features that are supported by the ROCm version on Stanford Megatron-LM.
 Models:
 * BERT
 * GPT
 * T5
 * ICT
 Features:
 * Distributed Pre-training
 * Activation Checkpointing and Recomputation
 * Distributed Optimizer
 * Mixture-of-Experts
 .. _megatron-lm-recommendations:
 Use cases and recommendations
 ================================================================================
 The following blog post mentions Megablocks, but you can run Stanford Megatron-LM with the same steps to pre-process datasets on AMD GPUs:
 * The `Efficient MoE training on AMD ROCm: How-to use Megablocks on AMD GPUs 
  <https://rocm.blogs.amd.com/artificial-intelligence/megablocks/README.html>`__ 
  blog post guides how to leverage the ROCm platform for pre-training using the 
  Megablocks framework. It introduces a streamlined approach for training Mixture-of-Experts 
  (MoE) models using the Megablocks library on AMD hardware. Focusing on GPT-2, it 
  demonstrates how block-sparse computations can enhance scalability and efficiency in MoE 
  training. The guide provides step-by-step instructions for setting up the environment, 
  including cloning the repository, building the Docker image, and running the training container. 
  Additionally, it offers insights into utilizing the ``oscar-1GB.json`` dataset for pre-training 
  language models. By leveraging Megablocks and the ROCm platform, you can optimize your MoE 
  training workflows for large-scale transformer models.
 It features how to pre-process datasets and how to begin pre-training on AMD GPUs through:
 * Single-GPU pre-training
 * Multi-GPU pre-training
--- a/docs/compatibility/ml-compatibility/verl-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/verl-compatibility.rst
@@ -0,0 +1,118 @@
 :orphan:
 .. meta::
   :description: verl compatibility
   :keywords: GPU, verl, deep learning, framework compatibility
 .. version-set:: rocm_version latest
 *******************************************************************************
 verl compatibility
 *******************************************************************************
 Volcano Engine Reinforcement Learning for LLMs (`verl <https://verl.readthedocs.io/en/latest/>`__)  
 is a reinforcement learning framework designed for large language models (LLMs). 
 verl offers a scalable, open-source fine-tuning solution by using a hybrid programming model 
 that makes it easy to define and run complex post-training dataflows efficiently. 
 Its modular APIs separate computation from data, allowing smooth integration with other frameworks. 
 It also supports flexible model placement across GPUs for efficient scaling on different cluster sizes.
 verl achieves high training and generation throughput by building on existing LLM frameworks. 
 Its 3D-HybridEngine reduces memory use and communication overhead when switching between training 
 and inference, improving overall performance.
 Support overview
 ================================================================================
 - The ROCm-supported version of verl is maintained in the official `https://github.com/ROCm/verl 
  <https://github.com/ROCm/verl>`__ repository, which differs from the 
  `https://github.com/volcengine/verl <https://github.com/volcengine/verl>`__ upstream repository.
 - To get started and install verl on ROCm, use the prebuilt :ref:`Docker image <verl-docker-compat>`, 
  which includes ROCm, verl, and all required dependencies.
  - See the :doc:`ROCm verl installation guide <rocm-install-on-linux:install/3rd-party/verl-install>`
    for installation and setup instructions.
  - You can also consult the upstream `verl documentation <https://verl.readthedocs.io/en/latest/>`__ 
    for additional context.
 .. _verl-docker-compat:
 Compatibility matrix
 ================================================================================
 .. |docker-icon| raw:: html
   <i class="fab fa-docker"></i>
 AMD validates and publishes `verl Docker images <https://hub.docker.com/r/rocm/verl/tags>`_
 with ROCm backends on Docker Hub. The following Docker image tag and associated inventories 
 represent the latest verl version from the official Docker Hub. 
 Click |docker-icon| to view the image on Docker Hub.
 .. list-table::
   :header-rows: 1
   :class: docker-image-compatibility
   * - Docker image
     - ROCm
     - verl
     - Ubuntu
     - PyTorch
     - Python
     - vllm
     - GPU
   * - .. raw:: html
        <a href="https://hub.docker.com/layers/rocm/verl/verl-0.6.0.amd0_rocm7.0_vllm0.11.0.dev/images/sha256-f70a3ebc94c1f66de42a2fcc3f8a6a8d6d0881eb0e65b6958d7d6d24b3eecb0d"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
     - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
     - `0.6.0 <https://github.com/volcengine/verl/releases/tag/v0.6.0>`__
     - 22.04
     - `2.9.0 <https://github.com/ROCm/pytorch/tree/release/2.9-rocm7.x-gfx115x>`__
     - `3.12.11 <https://www.python.org/downloads/release/python-31211/>`__
     - `0.11.0 <https://github.com/vllm-project/vllm/releases/tag/v0.11.0>`__
     - MI300X
   * - .. raw:: html
        <a href="https://hub.docker.com/layers/rocm/verl/verl-0.3.0.post0_rocm6.2_vllm0.6.3/images/sha256-cbe423803fd7850448b22444176bee06f4dcf22cd3c94c27732752d3a39b04b2"><i class="fab fa-docker fa-lg"></i> rocm/verl</a>
     - `6.2.0 <https://repo.radeon.com/rocm/apt/6.2/>`__
     - `0.3.0.post0 <https://github.com/volcengine/verl/releases/tag/v0.3.0.post0>`__
     - 20.04
     - `2.5.0 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
     - `3.9.19 <https://www.python.org/downloads/release/python-3919/>`__
     - `0.6.3 <https://github.com/vllm-project/vllm/releases/tag/v0.6.3>`__
     - MI300X
 .. _verl-supported_features:
 Supported modules with verl on ROCm
 ===============================================================================
 The following GPU-accelerated modules are supported with verl on ROCm:
 - ``FSDP``: Training engine
 - ``vllm``: Inference engine
 .. _verl-recommendations:
 Use cases and recommendations
 ================================================================================
 * The benefits of verl in large-scale reinforcement learning from human feedback 
  (RLHF) are discussed in the `Reinforcement Learning from Human Feedback on AMD 
  GPUs with verl and ROCm Integration <https://rocm.blogs.amd.com/artificial-intelligence/verl-large-scale/README.html>`__ 
  blog. The blog post outlines how the Volcano Engine Reinforcement Learning 
  (verl) framework integrates with the AMD ROCm platform to optimize training on 
  AMD Instinct™ GPUs. The guide details the process of building a Docker image, 
  setting up single-node and multi-node training environments, and highlights 
  performance benchmarks demonstrating improved throughput and convergence accuracy. 
  This resource serves as a comprehensive starting point for deploying verl on AMD GPUs, 
  facilitating efficient RLHF training workflows.
 Previous versions
 ===============================================================================
 See :doc:`rocm-install-on-linux:install/3rd-party/previous-versions/verl-history` to find documentation for previous releases
 of the ``ROCm/verl`` Docker image.
--- a/docs/conceptual/gpu-arch.md
+++ b/docs/conceptual/gpu-arch.md
@@ -66,7 +66,7 @@ architecture.
 * [AMD Instinct MI50/Vega 7nm ISA](https://www.amd.com/system/files/TechDocs/vega-7nm-shader-instruction-set-architecture.pdf)
 * [AMD Instinct MI25/Vega ISA](https://www.amd.com/system/files/TechDocs/vega-shader-instruction-set-architecture.pdf)
 * [AMD GCN3 ISA](https://www.amd.com/system/files/TechDocs/gcn3-instruction-set-architecture.pdf)
-* AMD Vega Architecture White Paper
+* [AMD Vega Architecture White Paper](https://en.wikichip.org/w/images/a/a1/vega-whitepaper.pdf)
 :::
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -81,7 +81,7 @@ latex_elements = {
 }
 html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "rocm.docs.amd.com")
-html_context = {"docs_header_version": "7.2.1"}
+html_context = {"docs_header_version": "7.1.1"}
 if os.environ.get("READTHEDOCS", "") == "True":
    html_context["READTHEDOCS"] = True
@@ -92,22 +92,28 @@ official_branch = run(["git", "rev-parse", "--abbrev-ref", "HEAD"], capture_outp
 project = "ROCm Documentation"
 project_path = os.path.abspath(".").replace("\\", "/")
 author = "Advanced Micro Devices, Inc."
-copyright = "Copyright (c) 2026 Advanced Micro Devices, Inc. All rights reserved."
+copyright = "Copyright (c) 2025 Advanced Micro Devices, Inc. All rights reserved."
-version = "7.2.1"
+version = "7.2.0"
-release = "7.2.1"
+release = "7.2.0"
 setting_all_article_info = True
 all_article_info_os = ["linux", "windows"]
 all_article_info_author = ""
 # pages with specific settings
 article_pages = [
-    {"file": "about/release-notes", "os": ["linux"], "date": "2026-03-25"},
+    {"file": "about/release-notes", "os": ["linux"], "date": "2026-01-21"},
    {"file": "release/changelog", "os": ["linux"],},
    {"file": "compatibility/compatibility-matrix", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/pytorch-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/tensorflow-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/jax-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/verl-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/stanford-megatron-lm-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/dgl-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/megablocks-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/ray-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/llama-cpp-compatibility", "os": ["linux"]},
    {"file": "compatibility/ml-compatibility/flashinfer-compatibility", "os": ["linux"]},
    {"file": "how-to/deep-learning-rocm", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/index", "os": ["linux"]},
@@ -162,7 +168,6 @@ article_pages = [
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.5", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.9", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.11", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v26.1", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/fine-tuning/index", "os": ["linux"]},
@@ -202,8 +207,6 @@ article_pages = [
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.11", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.12", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.13", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.1", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.2", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/inference/deploy-your-model", "os": ["linux"]},
@@ -225,8 +228,6 @@ article_pages = [
    {"file": "how-to/tuning-guides/mi300x/workload", "os": ["linux"]},
    {"file": "how-to/system-debugging", "os": ["linux"]},
    {"file": "how-to/gpu-enabled-mpi", "os": ["linux"]},
    {"file": "reference/rocm-tools", "os": ["linux"],},
 ]
 external_toc_path = "./sphinx/_toc.yml"
@@ -244,7 +245,7 @@ external_projects_current_project = "rocm"
 # external_projects_remote_repository = ""
 html_baseurl = os.environ.get("READTHEDOCS_CANONICAL_URL", "https://rocm-stg.amd.com/")
-html_context = {"docs_header_version": "7.2.1"}
+html_context = {"docs_header_version": "7.1.0"}
 if os.environ.get("READTHEDOCS", "") == "True":
    html_context["READTHEDOCS"] = True
@@ -280,6 +281,3 @@ html_context = {
 # Disable figure and table numbering
 numfig = False
 # Uncomment if facing rate limit exceed issue with local build
 external_projects_remote_repository = ""
--- a/docs/data/amd-rocm-logo.png
+++ b/docs/data/amd-rocm-logo.png
--- a/docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250715-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/inference/previous-versions/vllm_0.9.1_20250715-benchmark-models.yaml
@@ -110,7 +110,7 @@ vllm_benchmark:
      - model: DBRX Instruct
        mad_tag: pyt_vllm_dbrx-instruct
        model_repo: databricks/dbrx-instruct
-        url: https://huggingface.co/databricks
+        url: https://huggingface.co/databricks/dbrx-instruct
        precision: float16
      - model: DBRX Instruct FP8
        mad_tag: pyt_vllm_dbrx_fp8
--- a/docs/data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
@@ -1,105 +0,0 @@
 docker:
  pull_tag: rocm/pytorch-xdit:v25.13
  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef
  ROCm: 7.11.0
  whats_new:
    - "Flux.1 Kontext support"
    - "Flux.2 Dev support"
    - "Flux FP8 GEMM support"
    - "Hybrid FP8 attention support for Wan models"
  components:
    TheRock: 
      version: 1728a81
      url: https://github.com/ROCm/TheRock
    rccl:
      version: d23d18f
      url: https://github.com/ROCm/rccl
    composable_kernel:
      version: ab0101c
      url: https://github.com/ROCm/composable_kernel
    rocm-libraries:
      version: a2f7c35
      url: https://github.com/ROCm/rocm-libraries
    rocm-systems:
      version: 659737c
      url: https://github.com/ROCm/rocm-systems
    torch:
      version: 91be249
      url: https://github.com/ROCm/pytorch
    torchvision:
      version: b919bd0
      url: https://github.com/pytorch/vision
    triton:
      version: a272dfa
      url: https://github.com/ROCm/triton
    accelerate:
      version: b521400f
      url: https://github.com/huggingface/accelerate
    aiter:
      version: de14bec0
      url: https://github.com/ROCm/aiter
    diffusers:
      version: a1f36ee3e
      url: https://github.com/huggingface/diffusers
    xfuser:
      version: adf2681
      url: https://github.com/xdit-project/xDiT
    yunchang:
      version: 2c9b712
      url: https://github.com/feifeibear/long-context-attention
  supported_models:
    - group: Hunyuan Video
      js_tag: hunyuan
      models:
        - model: Hunyuan Video
          model_repo: tencent/HunyuanVideo
          revision: refs/pr/18
          url: https://huggingface.co/tencent/HunyuanVideo
          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
          mad_tag: pyt_xdit_hunyuanvideo
          js_tag: hunyuan_tag
    - group: Wan-AI
      js_tag: wan
      models:
        - model: Wan2.1
          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
          github: https://github.com/Wan-Video/Wan2.1
          mad_tag: pyt_xdit_wan_2_1
          js_tag: wan_21_tag
        - model: Wan2.2
          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
          github: https://github.com/Wan-Video/Wan2.2
          mad_tag: pyt_xdit_wan_2_2
          js_tag: wan_22_tag
    - group: FLUX
      js_tag: flux
      models:
        - model: FLUX.1
          model_repo: black-forest-labs/FLUX.1-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux
          js_tag: flux_1_tag
        - model: FLUX.1 Kontext
          model_repo: black-forest-labs/FLUX.1-Kontext-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux_kontext
          js_tag: flux_1_kontext_tag
        - model: FLUX.2
          model_repo: black-forest-labs/FLUX.2-dev
          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
          github: https://github.com/black-forest-labs/flux2
          mad_tag: pyt_xdit_flux_2
          js_tag: flux_2_tag
    - group: StableDiffusion
      js_tag: stablediffusion
      models:
        - model: stable-diffusion-3.5-large
          model_repo: stabilityai/stable-diffusion-3.5-large
          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
          github: https://github.com/Stability-AI/sd3.5
          mad_tag: pyt_xdit_sd_3_5
          js_tag: stable_diffusion_3_5_large_tag
--- a/docs/data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
@@ -1,105 +0,0 @@
 docker:
  pull_tag: rocm/pytorch-xdit:v25.13
  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef
  ROCm: 7.11.0
  whats_new:
    - "Flux.1 Kontext support"
    - "Flux.2 Dev support"
    - "Flux FP8 GEMM support"
    - "Hybrid FP8 attention support for Wan models"
  components:
    TheRock: 
      version: 1728a81
      url: https://github.com/ROCm/TheRock
    rccl:
      version: d23d18f
      url: https://github.com/ROCm/rccl
    composable_kernel:
      version: ab0101c
      url: https://github.com/ROCm/composable_kernel
    rocm-libraries:
      version: a2f7c35
      url: https://github.com/ROCm/rocm-libraries
    rocm-systems:
      version: 659737c
      url: https://github.com/ROCm/rocm-systems
    torch:
      version: 91be249
      url: https://github.com/ROCm/pytorch
    torchvision:
      version: b919bd0
      url: https://github.com/pytorch/vision
    triton:
      version: a272dfa
      url: https://github.com/ROCm/triton
    accelerate:
      version: b521400f
      url: https://github.com/huggingface/accelerate
    aiter:
      version: de14bec0
      url: https://github.com/ROCm/aiter
    diffusers:
      version: a1f36ee3e
      url: https://github.com/huggingface/diffusers
    xfuser:
      version: adf2681
      url: https://github.com/xdit-project/xDiT
    yunchang:
      version: 2c9b712
      url: https://github.com/feifeibear/long-context-attention
  supported_models:
    - group: Hunyuan Video
      js_tag: hunyuan
      models:
        - model: Hunyuan Video
          model_repo: tencent/HunyuanVideo
          revision: refs/pr/18
          url: https://huggingface.co/tencent/HunyuanVideo
          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
          mad_tag: pyt_xdit_hunyuanvideo
          js_tag: hunyuan_tag
    - group: Wan-AI
      js_tag: wan
      models:
        - model: Wan2.1
          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
          github: https://github.com/Wan-Video/Wan2.1
          mad_tag: pyt_xdit_wan_2_1
          js_tag: wan_21_tag
        - model: Wan2.2
          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
          github: https://github.com/Wan-Video/Wan2.2
          mad_tag: pyt_xdit_wan_2_2
          js_tag: wan_22_tag
    - group: FLUX
      js_tag: flux
      models:
        - model: FLUX.1
          model_repo: black-forest-labs/FLUX.1-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux
          js_tag: flux_1_tag
        - model: FLUX.1 Kontext
          model_repo: black-forest-labs/FLUX.1-Kontext-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux_kontext
          js_tag: flux_1_kontext_tag
        - model: FLUX.2
          model_repo: black-forest-labs/FLUX.2-dev
          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
          github: https://github.com/black-forest-labs/flux2
          mad_tag: pyt_xdit_flux_2
          js_tag: flux_2_tag
    - group: StableDiffusion
      js_tag: stablediffusion
      models:
        - model: stable-diffusion-3.5-large
          model_repo: stabilityai/stable-diffusion-3.5-large
          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
          github: https://github.com/Stability-AI/sd3.5
          mad_tag: pyt_xdit_sd_3_5
          js_tag: stable_diffusion_3_5_large_tag
--- a/docs/data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
@@ -1,311 +0,0 @@
 docker:
  pull_tag: rocm/pytorch-xdit:v26.2
  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v26.2/images/sha256-e2c504af438bb9cf60e3869c499baa5102b3d3f62141b99c49743e755ae44008
  ROCm: 7.11.0
  whats_new:
    - "LTX-2 support"
    - "Flux.2 Klein support"
    - "Aiter update to support opt_groupnorm, dynamic scaling in FP8 and Sage attention v1"
  components:
    TheRock:
      version: 1728a81
      url: https://github.com/ROCm/TheRock
    rccl:
      version: d23d18f
      url: https://github.com/ROCm/rccl
    composable_kernel:
      version: ab0101c
      url: https://github.com/ROCm/composable_kernel
    rocm-libraries:
      version: a2f7c35
      url: https://github.com/ROCm/rocm-libraries
    rocm-systems:
      version: 659737c
      url: https://github.com/ROCm/rocm-systems
    torch:
      version: 91be249
      url: https://github.com/ROCm/pytorch
    torchvision:
      version: b919bd0
      url: https://github.com/pytorch/vision
    triton:
      version: a272dfa
      url: https://github.com/ROCm/triton
    accelerate:
      version: b7f2212
      url: https://github.com/huggingface/accelerate
    aiter:
      version: 42ae0ad
      url: https://github.com/ROCm/aiter
    diffusers:
      version: a3dcd9
      url: https://github.com/huggingface/diffusers
    xfuser:
      version: 635fc29
      url: https://github.com/xdit-project/xDiT
    yunchang:
      version: 631bdfd
      url: https://github.com/feifeibear/long-context-attention
  supported_models:
    - group: Hunyuan Video
      js_tag: hunyuan
      models:
        - model: Hunyuan Video
          model_repo: tencent/HunyuanVideo
          revision: refs/pr/18
          url: https://huggingface.co/tencent/HunyuanVideo
          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
          mad_tag: pyt_xdit_hunyuanvideo
          js_tag: hunyuan_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
            - '--batch_size 1 \'
            - '--height 720 --width 1280 \'
            - '--seed 1168860793 \'
            - '--num_frames 129 \'
            - '--num_inference_steps 50 \'
            - '--warmup_calls 1 \'
            - '--num_iterations 1 \'
            - '--ulysses_degree 8 \'
            - '--enable_tiling --enable_slicing \'
            - '--guidance_scale 6.0 \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: Hunyuan Video 1.5
          model_repo: hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
          url: https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
          github: https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5
          mad_tag: pyt_xdit_hunyuanvideo_1_5
          js_tag: hunyuan_1_5_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
            - '--task t2v \'
            - '--height 720 --width 1280 \'
            - '--seed 1168860793 \'
            - '--num_frames 129 \'
            - '--num_inference_steps 50 \'
            - '--num_iterations 1 \'
            - '--ulysses_degree 8 \'
            - '--enable_tiling --enable_slicing \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: Wan-AI
      js_tag: wan
      models:
        - model: Wan2.1
          model_repo: Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.1-I2V-14B-720P-Diffusers
          github: https://github.com/Wan-Video/Wan2.1
          mad_tag: pyt_xdit_wan_2_1
          js_tag: wan_21_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
            - '--height 720 \'
            - '--width 1280 \'
            - '--input_images /app/data/wan_input.jpg \'
            - '--num_frames 81 \'
            - '--ulysses_degree 8 \'
            - '--seed 42 \'
            - '--num_iterations 1 \'
            - '--num_inference_steps 40 \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: Wan2.2
          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
          github: https://github.com/Wan-Video/Wan2.2
          mad_tag: pyt_xdit_wan_2_2
          js_tag: wan_22_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
            - '--height 720 \'
            - '--width 1280 \'
            - '--input_images /app/data/wan_input.jpg \'
            - '--num_frames 81 \'
            - '--ulysses_degree 8 \'
            - '--seed 42 \'
            - '--num_iterations 1 \'
            - '--num_inference_steps 40 \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: FLUX
      js_tag: flux
      models:
        - model: FLUX.1
          model_repo: black-forest-labs/FLUX.1-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux
          js_tag: flux_1_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A small cat" \'
            - '--height 1024 \'
            - '--width 1024 \'
            - '--num_inference_steps 25 \'
            - '--max_sequence_length 256 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--guidance_scale 0.0 \'
            - '--num_iterations 50 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: FLUX.1 Kontext
          model_repo: black-forest-labs/FLUX.1-Kontext-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux_kontext
          js_tag: flux_1_kontext_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Add a cool hat to the cat" \'
            - '--height 1024 \'
            - '--width 1024 \'
            - '--num_inference_steps 30 \'
            - '--max_sequence_length 512 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--input_images /app/data/flux_cat.png \'
            - '--guidance_scale 2.5 \'
            - '--num_iterations 25 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: FLUX.2
          model_repo: black-forest-labs/FLUX.2-dev
          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
          github: https://github.com/black-forest-labs/flux2
          mad_tag: pyt_xdit_flux_2
          js_tag: flux_2_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Add a cool hat to the cat" \'
            - '--height 1024 \'
            - '--width 1024 \'
            - '--num_inference_steps 50 \'
            - '--max_sequence_length 512 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--input_images /app/data/flux_cat.png \'
            - '--guidance_scale 4.0 \'
            - '--num_iterations 25 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: FLUX.2 Klein
          model_repo: black-forest-labs/FLUX.2-klein-9B
          url: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
          github: https://github.com/black-forest-labs/flux2
          mad_tag: pyt_xdit_flux_2_klein
          js_tag: flux_2_klein_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A spectacular sunset over the ocean" \'
            - '--height 2048 \'
            - '--width 2048 \'
            - '--num_inference_steps 4 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--guidance_scale 1.0 \'
            - '--num_iterations 25 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: StableDiffusion
      js_tag: stablediffusion
      models:
        - model: stable-diffusion-3.5-large
          model_repo: stabilityai/stable-diffusion-3.5-large
          url: https://huggingface.co/stabilityai/stable-diffusion-3.5-large
          github: https://github.com/Stability-AI/sd3.5
          mad_tag: pyt_xdit_sd_3_5
          js_tag: stable_diffusion_3_5_large_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "A capybara holding a sign that reads Hello World" \'
            - '--num_iterations 50 \'
            - '--num_inference_steps 28 \'
            - '--pipefusion_parallel_degree 4 \'
            - '--use_cfg_parallel \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: Z-Image
      js_tag: z_image
      models:
        - model: Z-Image Turbo
          model_repo: Tongyi-MAI/Z-Image-Turbo
          url: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
          github: https://github.com/Tongyi-MAI/Z-Image
          mad_tag: pyt_xdit_z_image_turbo
          js_tag: z_image_turbo_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A crowded beach" \'
            - '--height 1088 \'
            - '--width 1920 \'
            - '--num_inference_steps 9 \'
            - '--ulysses_degree 2 \'
            - '--use_torch_compile \'
            - '--guidance_scale 0.0 \'
            - '--num_iterations 50 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: LTX
      js_tag: ltx
      models:
        - model: LTX-2
          model_repo: Lightricks/LTX-2
          url: https://huggingface.co/Lightricks/LTX-2
          github: https://github.com/Lightricks/LTX-2
          mad_tag: pyt_xdit_ltx2
          js_tag: ltx2_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Cinematic action packed shot. The man says silently: \"We need to run.\". The camera zooms in on his mouth then immediately screams: \"NOW!\". The camera zooms back out, he turns around and bolts it." \'
            - '--height 1088 \'
            - '--width 1920 \'
            - '--num_inference_steps 40 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--guidance_scale 4.0 \'
            - '--num_iterations 1 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
--- a/docs/data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/inference/xdit-inference-models.yaml
@@ -1,28 +1,31 @@
 docker:
-  pull_tag: rocm/pytorch-xdit:v26.3
+  pull_tag: rocm/pytorch-xdit:v25.13
-  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v26.3/images/sha256-ac78a03d2911bf1b49c001d3be2e8bd745c1bc455cb49ae972825a7986880902
+  docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef
-  ROCm: 7.12.0
+  ROCm: 7.11.0
  whats_new:
-    - "Qwen-Image support"
+    - "Flux.1 Kontext support"
-    - "Qwen-Image-Edit support"
+    - "Flux.2 Dev support"
-    - "Aiter update to support Sage attention v2"
+    - "Flux FP8 GEMM support"
-    - "xDiT update to support MXFP4 GEMMs in Wan2.2, Wan2.1 and Flux.2"
+    - "Hybrid FP8 attention support for Wan models"
  components:
    TheRock: 
-      version: e40a6da
+      version: 1728a81
      url: https://github.com/ROCm/TheRock
    rccl:
      version: d23d18f
      url: https://github.com/ROCm/rccl
    composable_kernel:
      version: ab0101c
      url: https://github.com/ROCm/composable_kernel
    rocm-libraries:
-      version: 9e9e900
+      version: a2f7c35
      url: https://github.com/ROCm/rocm-libraries
    rocm-systems:
-      version: ca89a1a
+      version: 659737c
      url: https://github.com/ROCm/rocm-systems
    torch:
      version: 91be249
      url: https://github.com/ROCm/pytorch
    torchaudio:
      version: e3c6ee2
      url: https://github.com/pytorch/audio
    torchvision:
      version: b919bd0
      url: https://github.com/pytorch/vision
@@ -30,19 +33,19 @@ docker:
      version: a272dfa
      url: https://github.com/ROCm/triton
    accelerate:
-      version: 46ba481
+      version: b521400f
      url: https://github.com/huggingface/accelerate
    aiter:
-      version: 82d733f
+      version: de14bec0
      url: https://github.com/ROCm/aiter
    diffusers:
-      version: a80b192
+      version: a1f36ee3e
      url: https://github.com/huggingface/diffusers
    xfuser:
-      version: 2882027
+      version: adf2681
      url: https://github.com/xdit-project/xDiT
    yunchang:
-      version: 631bdfd
+      version: 2c9b712
      url: https://github.com/feifeibear/long-context-attention
  supported_models:
    - group: Hunyuan Video
@@ -55,46 +58,6 @@ docker:
          github: https://github.com/Tencent-Hunyuan/HunyuanVideo
          mad_tag: pyt_xdit_hunyuanvideo
          js_tag: hunyuan_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
            - '--batch_size 1 \'
            - '--height 720 --width 1280 \'
            - '--seed 1168860793 \'
            - '--num_frames 129 \'
            - '--num_inference_steps 50 \'
            - '--warmup_calls 1 \'
            - '--num_iterations 1 \'
            - '--ulysses_degree 8 \'
            - '--enable_tiling --enable_slicing \'
            - '--guidance_scale 6.0 \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: Hunyuan Video 1.5
          model_repo: hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
          url: https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
          github: https://github.com/Tencent-Hunyuan/HunyuanVideo-1.5
          mad_tag: pyt_xdit_hunyuanvideo_1_5
          js_tag: hunyuan_1_5_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "In the large cage, two puppies were wagging their tails at each other." \'
            - '--task t2v \'
            - '--height 720 --width 1280 \'
            - '--seed 1168860793 \'
            - '--num_frames 129 \'
            - '--num_inference_steps 50 \'
            - '--num_iterations 1 \'
            - '--ulysses_degree 8 \'
            - '--enable_tiling --enable_slicing \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: Wan-AI
      js_tag: wan
      models:
@@ -104,44 +67,12 @@ docker:
          github: https://github.com/Wan-Video/Wan2.1
          mad_tag: pyt_xdit_wan_2_1
          js_tag: wan_21_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
            - '--height 720 \'
            - '--width 1280 \'
            - '--input_images /app/data/wan_input.jpg \'
            - '--num_frames 81 \'
            - '--ulysses_degree 8 \'
            - '--seed 42 \'
            - '--num_iterations 1 \'
            - '--num_inference_steps 40 \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: Wan2.2
          model_repo: Wan-AI/Wan2.2-I2V-A14B-Diffusers
          url: https://huggingface.co/Wan-AI/Wan2.2-I2V-A14B-Diffusers
          github: https://github.com/Wan-Video/Wan2.2
          mad_tag: pyt_xdit_wan_2_2
          js_tag: wan_22_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline''s intricate details and the refreshing atmosphere of the seaside." \'
            - '--height 720 \'
            - '--width 1280 \'
            - '--input_images /app/data/wan_input.jpg \'
            - '--num_frames 81 \'
            - '--ulysses_degree 8 \'
            - '--seed 42 \'
            - '--num_iterations 1 \'
            - '--num_inference_steps 40 \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: FLUX
      js_tag: flux
      models:
@@ -151,93 +82,18 @@ docker:
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux
          js_tag: flux_1_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A small cat" \'
            - '--height 1024 \'
            - '--width 1024 \'
            - '--num_inference_steps 25 \'
            - '--max_sequence_length 256 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--guidance_scale 0.0 \'
            - '--num_iterations 50 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: FLUX.1 Kontext
          model_repo: black-forest-labs/FLUX.1-Kontext-dev
          url: https://huggingface.co/black-forest-labs/FLUX.1-Kontext-dev
          github: https://github.com/black-forest-labs/flux
          mad_tag: pyt_xdit_flux_kontext
          js_tag: flux_1_kontext_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Add a cool hat to the cat" \'
            - '--height 1024 \'
            - '--width 1024 \'
            - '--num_inference_steps 30 \'
            - '--max_sequence_length 512 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--input_images /app/data/flux_cat.png \'
            - '--guidance_scale 2.5 \'
            - '--num_iterations 25 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: FLUX.2
          model_repo: black-forest-labs/FLUX.2-dev
          url: https://huggingface.co/black-forest-labs/FLUX.2-dev
          github: https://github.com/black-forest-labs/flux2
          mad_tag: pyt_xdit_flux_2
          js_tag: flux_2_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Add a cool hat to the cat" \'
            - '--height 1024 \'
            - '--width 1024 \'
            - '--num_inference_steps 50 \'
            - '--max_sequence_length 512 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--input_images /app/data/flux_cat.png \'
            - '--guidance_scale 4.0 \'
            - '--num_iterations 25 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: FLUX.2 Klein
          model_repo: black-forest-labs/FLUX.2-klein-9B
          url: https://huggingface.co/black-forest-labs/FLUX.2-klein-9B
          github: https://github.com/black-forest-labs/flux2
          mad_tag: pyt_xdit_flux_2_klein
          js_tag: flux_2_klein_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A spectacular sunset over the ocean" \'
            - '--height 2048 \'
            - '--width 2048 \'
            - '--num_inference_steps 4 \'
            - '--warmup_calls 5 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--guidance_scale 1.0 \'
            - '--num_iterations 25 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: StableDiffusion
      js_tag: stablediffusion
      models:
@@ -247,108 +103,3 @@ docker:
          github: https://github.com/Stability-AI/sd3.5
          mad_tag: pyt_xdit_sd_3_5
          js_tag: stable_diffusion_3_5_large_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--prompt "A capybara holding a sign that reads Hello World" \'
            - '--num_iterations 50 \'
            - '--num_inference_steps 28 \'
            - '--pipefusion_parallel_degree 4 \'
            - '--use_cfg_parallel \'
            - '--use_torch_compile \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: Z-Image
      js_tag: z_image
      models:
        - model: Z-Image Turbo
          model_repo: Tongyi-MAI/Z-Image-Turbo
          url: https://huggingface.co/Tongyi-MAI/Z-Image-Turbo
          github: https://github.com/Tongyi-MAI/Z-Image
          mad_tag: pyt_xdit_z_image_turbo
          js_tag: z_image_turbo_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A crowded beach" \'
            - '--height 1088 \'
            - '--width 1920 \'
            - '--num_inference_steps 9 \'
            - '--ulysses_degree 2 \'
            - '--use_torch_compile \'
            - '--guidance_scale 0.0 \'
            - '--num_iterations 50 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: LTX
      js_tag: ltx
      models:
        - model: LTX-2
          model_repo: Lightricks/LTX-2
          url: https://huggingface.co/Lightricks/LTX-2
          github: https://github.com/Lightricks/LTX-2
          mad_tag: pyt_xdit_ltx2
          js_tag: ltx2_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Cinematic action packed shot. The man says silently: \"We need to run.\". The camera zooms in on his mouth then immediately screams: \"NOW!\". The camera zooms back out, he turns around and bolts it." \'
            - '--height 1088 \'
            - '--width 1920 \'
            - '--num_inference_steps 40 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--guidance_scale 4.0 \'
            - '--num_iterations 1 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
    - group: Qwen-Image
      js_tag: qwen_image
      models:
        - model: Qwen-Image
          model_repo: Qwen/Qwen-Image
          url: https://huggingface.co/Qwen/Qwen-Image
          github: https://github.com/QwenLM/Qwen-Image
          mad_tag: pyt_xdit_qwen_image
          js_tag: qwen_image_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "A cat holding a sign that says hello world" \'
            - '--height 2048 \'
            - '--width 2048 \'
            - '--num_inference_steps 50 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--num_iterations 1 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
        - model: Qwen-Image-Edit
          model_repo: Qwen/Qwen-Image-Edit
          url: https://huggingface.co/Qwen/Qwen-Image-Edit
          github: https://github.com/QwenLM/Qwen-Image
          mad_tag: pyt_xdit_qwen_image_edit
          js_tag: qwen_image_edit_tag
          benchmark_command:
            - mkdir results
            - 'xdit \'
            - '--model {model_repo} \'
            - '--seed 42 \'
            - '--prompt "Add a cool hat to the cat." \'
            - '--negative_prompt "" \'
            - '--input_images /app/data/flux_cat.png \'
            - '--height 2048 \'
            - '--width 2048 \'
            - '--num_inference_steps 50 \'
            - '--ulysses_degree 8 \'
            - '--use_torch_compile \'
            - '--num_iterations 1 \'
            - '--attention_backend aiter \'
            - '--output_directory results'
--- a/docs/data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
@@ -1,6 +1,6 @@
 dockers:
-  - pull_tag: rocm/jax-training:maxtext-v26.2
+  - pull_tag: rocm/jax-training:maxtext-v26.1
-    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.2/images/sha256-a89643388487b1e2fc6b6ef7bd3c44378c05d217309c977a1c18c72d05ebcaeb
+    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.1/images/sha256-901083bde353fe6362ada3036e452c792b2c96124e5900f4e9b5946c02ff9d6a
    components:
      ROCm: 7.1.1
      JAX: 0.8.2
@@ -15,7 +15,6 @@ model_groups:
        mad_tag: jax_maxtext_train_llama-2-7b
        model_repo: Llama-2-7B
        precision: bf16
        primus_config_name: llama2_7B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_llama2_7b.yml
          gfx942: env_scripts/llama2_7b.yml
@@ -24,21 +23,18 @@ model_groups:
        mad_tag: jax_maxtext_train_llama-2-70b
        model_repo: Llama-2-70B
        precision: bf16
        primus_config_name: llama2_70B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_llama2_70b.yml
          gfx942: env_scripts/llama2_70b.yml
        doc_options: ["single-node", "multi-node"]
-      - model: Llama 3 8B
+      - model: Llama 3 8B (multi-node)
        mad_tag: jax_maxtext_train_llama-3-8b
        primus_config_name: llama3_8B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_llama3_8b.yml
          gfx942: env_scripts/llama3_8b.yml
        doc_options: ["multi-node"]
-      - model: Llama 3 70B
+      - model: Llama 3 70B (multi-node)
        mad_tag: jax_maxtext_train_llama-3-70b
        primus_config_name: llama3_70B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_llama3_70b.yml
          gfx942: env_scripts/llama3_70b.yml
@@ -64,7 +60,6 @@ model_groups:
        mad_tag: jax_maxtext_train_llama-3.3-70b
        model_repo: Llama-3.3-70B
        precision: bf16
        primus_config_name: llama3.3_70B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_llama3.3_70b.yml
          gfx942: env_scripts/llama3.3_70b.yml
@@ -76,7 +71,6 @@ model_groups:
        mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
        model_repo: DeepSeek-V2-lite
        precision: bf16
        primus_config_name: deepseek_v2_16B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_deepseek2_16b.yml
          gfx942: env_scripts/deepseek2_16b.yml
@@ -88,7 +82,6 @@ model_groups:
        mad_tag: jax_maxtext_train_mixtral-8x7b
        model_repo: Mixtral-8x7B
        precision: bf16
        primus_config_name: mixtral_8x7B-pretrain.yaml
        multinode_config:
          gfx950: env_scripts/gfx950_mixtral_8x7b.yml
          gfx942: env_scripts/llama3_8x7b.yml
--- a/docs/data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/megatron-lm-benchmark-models.yaml
@@ -1,14 +1,14 @@
 docker:
-  pull_tag: rocm/primus:v26.2
+  pull_tag: rocm/primus:v26.1
  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
  components:
-    ROCm: 7.2.0
+    ROCm: 7.1.0
-    PyTorch: 2.10.0+git94c6e04
+    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.12.3"
+    Python: "3.10"
-    Transformer Engine: 2.8.0.dev0+51f74fa7
+    Transformer Engine: 2.6.0.dev0+f141f34b
    Flash Attention: 2.8.3
    hipBLASLt: 34459f66ea
-    Triton: 3.5.0
+    Triton: 3.4.0
    RCCL: 2.27.7
 model_groups:
  - group: Meta Llama
--- a/docs/data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v26.1-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v26.1-benchmark-models.yaml
@@ -1,88 +0,0 @@
 dockers:
  - pull_tag: rocm/jax-training:maxtext-v26.1
    docker_hub_url: https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.1/images/sha256-901083bde353fe6362ada3036e452c792b2c96124e5900f4e9b5946c02ff9d6a
    components:
      ROCm: 7.1.1
      JAX: 0.8.2
      Python: 3.12
      Transformer Engine: 2.8.0.dev0+aec00a7f
      hipBLASLt: 1.2.x
 model_groups:
  - group: Meta Llama
    tag: llama
    models:
      - model: Llama 2 7B
        mad_tag: jax_maxtext_train_llama-2-7b
        model_repo: Llama-2-7B
        precision: bf16
        multinode_config:
          gfx950: env_scripts/gfx950_llama2_7b.yml
          gfx942: env_scripts/llama2_7b.yml
        doc_options: ["single-node", "multi-node"]
      - model: Llama 2 70B
        mad_tag: jax_maxtext_train_llama-2-70b
        model_repo: Llama-2-70B
        precision: bf16
        multinode_config:
          gfx950: env_scripts/gfx950_llama2_70b.yml
          gfx942: env_scripts/llama2_70b.yml
        doc_options: ["single-node", "multi-node"]
      - model: Llama 3 8B (multi-node)
        mad_tag: jax_maxtext_train_llama-3-8b
        multinode_config:
          gfx950: env_scripts/gfx950_llama3_8b.yml
          gfx942: env_scripts/llama3_8b.yml
        doc_options: ["multi-node"]
      - model: Llama 3 70B (multi-node)
        mad_tag: jax_maxtext_train_llama-3-70b
        multinode_config:
          gfx950: env_scripts/gfx950_llama3_70b.yml
          gfx942: env_scripts/llama3_70b.yml
        doc_options: ["multi-node"]
      - model: Llama 3.1 8B
        mad_tag: jax_maxtext_train_llama-3.1-8b
        model_repo: Llama-3.1-8B
        precision: bf16
        doc_options: ["single-node"]
      - model: Llama 3.1 70B
        mad_tag: jax_maxtext_train_llama-3.1-70b
        model_repo: Llama-3.1-70B
        precision: bf16
        doc_options: ["single-node"]
      - model: Llama 3.1 405B (multi-node)
        mad_tag: jax_maxtext_train_llama-3.1-405b
        model_repo: Llama-3.1-405B
        precision: bf16
        multinode_config:
          gfx950: env_scripts/gfx950_llama3_405b.yml
        doc_options: ["multi-node"]
      - model: Llama 3.3 70B
        mad_tag: jax_maxtext_train_llama-3.3-70b
        model_repo: Llama-3.3-70B
        precision: bf16
        multinode_config:
          gfx950: env_scripts/gfx950_llama3.3_70b.yml
          gfx942: env_scripts/llama3.3_70b.yml
        doc_options: ["single-node", "multi-node"]
  - group: DeepSeek
    tag: deepseek
    models:
      - model: DeepSeek-V2-Lite (16B)
        mad_tag: jax_maxtext_train_deepseek-v2-lite-16b
        model_repo: DeepSeek-V2-lite
        precision: bf16
        multinode_config:
          gfx950: env_scripts/gfx950_deepseek2_16b.yml
          gfx942: env_scripts/deepseek2_16b.yml
        doc_options: ["single-node", "multi-node"]
  - group: Mistral AI
    tag: mistral
    models:
      - model: Mixtral 8x7B
        mad_tag: jax_maxtext_train_mixtral-8x7b
        model_repo: Mixtral-8x7B
        precision: bf16
        multinode_config:
          gfx950: env_scripts/gfx950_mixtral_8x7b.yml
          gfx942: env_scripts/llama3_8x7b.yml
        doc_options: ["single-node", "multi-node"]
--- a/docs/data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v26.1-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/previous-versions/primus-megatron-v26.1-benchmark-models.yaml
@@ -1,58 +0,0 @@
 docker:
  pull_tag: rocm/primus:v26.1
  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
  components:
    ROCm: 7.1.0
    PyTorch: 2.10.0.dev20251112+rocm7.1
    Python: "3.10"
    Transformer Engine: 2.6.0.dev0+f141f34b
    Flash Attention: 2.8.3
    hipBLASLt: 34459f66ea
    Triton: 3.4.0
    RCCL: 2.27.7
 model_groups:
  - group: Meta Llama
    tag: llama
    models:
      - model: Llama 3.3 70B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
        config_name: llama3.3_70B-pretrain.yaml
      - model: Llama 3.1 70B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
        config_name: llama3.1_70B-pretrain.yaml
      - model: Llama 3.1 8B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
        config_name: llama3.1_8B-pretrain.yaml
      - model: Llama 2 7B
        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
        config_name: llama2_7B-pretrain.yaml
      - model: Llama 2 70B
        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
        config_name: llama2_70B-pretrain.yaml
  - group: DeepSeek
    tag: deepseek
    models:
      - model: DeepSeek-V3 (proxy)
        mad_tag: primus_pyt_megatron_lm_train_deepseek-v3-proxy
        config_name: deepseek_v3-pretrain.yaml
      - model: DeepSeek-V2-Lite
        mad_tag: primus_pyt_megatron_lm_train_deepseek-v2-lite-16b
        config_name: deepseek_v2_lite-pretrain.yaml
  - group: Mistral AI
    tag: mistral
    models:
      - model: Mixtral 8x7B
        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x7b
        config_name: mixtral_8x7B_v0.1-pretrain.yaml
      - model: Mixtral 8x22B (proxy)
        mad_tag: primus_pyt_megatron_lm_train_mixtral-8x22b-proxy
        config_name: mixtral_8x22B_v0.1-pretrain.yaml
  - group: Qwen
    tag: qwen
    models:
      - model: Qwen 2.5 7B
        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
        config_name: primus_qwen2.5_7B-pretrain.yaml
      - model: Qwen 2.5 72B
        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-72b
        config_name: qwen2.5_72B-pretrain.yaml
--- a/docs/data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v26.1-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v26.1-benchmark-models.yaml
@@ -1,32 +0,0 @@
 docker:
  pull_tag: rocm/primus:v26.1
  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
  components:
    ROCm: 7.1.0
    PyTorch: 2.10.0.dev20251112+rocm7.1
    Python: "3.10"
    Transformer Engine: 2.6.0.dev0+f141f34b
    Flash Attention: 2.8.3
    hipBLASLt: 34459f66ea
 model_groups:
  - group: Meta Llama
    tag: llama
    models:
      - model: Llama 3.1 8B
        mad_tag: primus_pyt_train_llama-3.1-8b
        model_repo: Llama-3.1-8B
        url: https://huggingface.co/meta-llama/Llama-3.1-8B
        precision: BF16
      - model: Llama 3.1 70B
        mad_tag: primus_pyt_train_llama-3.1-70b
        model_repo: Llama-3.1-70B
        url: https://huggingface.co/meta-llama/Llama-3.1-70B
        precision: BF16
  - group: DeepSeek
    tag: deepseek
    models:
      - model: DeepSeek V3 16B
        mad_tag: primus_pyt_train_deepseek-v3-16b
        model_repo: DeepSeek-V3
        url: https://huggingface.co/deepseek-ai/DeepSeek-V3
        precision: BF16
--- a/docs/data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/primus-megatron-benchmark-models.yaml
@@ -1,14 +1,14 @@
 docker:
-  pull_tag: rocm/primus:v26.2
+  pull_tag: rocm/primus:v26.1
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.2/images/sha256-9148d1bfcd579bf92f44bd89090e0d8c958f149c134b4b34b9674ab559244585
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
  components:
-    ROCm: 7.2.0
+    ROCm: 7.1.0
-    PyTorch: 2.10.0a0+git449b176
+    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.12.3"
+    Python: "3.10"
-    Transformer Engine: 2.8.0.dev0+51f74fa7
+    Transformer Engine: 2.6.0.dev0+f141f34b
    Flash Attention: 2.8.3
-    hipBLASLt: 1.2.0-de5c1aebb6
+    hipBLASLt: 34459f66ea
-    Triton: 3.6.0
+    Triton: 3.4.0
    RCCL: 2.27.7
 model_groups:
  - group: Meta Llama
@@ -17,30 +17,18 @@ model_groups:
      - model: Llama 3.3 70B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.3-70b
        config_name: llama3.3_70B-pretrain.yaml
      - model: Llama 3.1 8B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
        config_name: llama3.1_8B-pretrain.yaml
      - model: Llama 3.1 70B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-70b
        config_name: llama3.1_70B-pretrain.yaml
      - model: Llama 3.1 8B
        mad_tag: primus_pyt_megatron_lm_train_llama-3.1-8b
        config_name: llama3.1_8B-pretrain.yaml
      - model: Llama 2 7B
        mad_tag: primus_pyt_megatron_lm_train_llama-2-7b
        config_name: llama2_7B-pretrain.yaml
      - model: Llama 2 70B
        mad_tag: primus_pyt_megatron_lm_train_llama-2-70b
        config_name: llama2_70B-pretrain.yaml
  - group: AMD Zebra-Llama
    tag: zebra-llama
    models:
      - model: Zebra-Llama 1B
        mad_tag: primus_pyt_megatron_lm_train_zebra-llama-1b
        config_name: zebra_llama_1b-pretrain.yaml
      - model: Zebra-Llama 3B
        mad_tag: primus_pyt_megatron_lm_train_zebra-llama-3b
        config_name: zebra_llama_3b-pretrain.yaml
      - model: Zebra-Llama 8B
        mad_tag: primus_pyt_megatron_lm_train_zebra-llama-8b
        config_name: zebra_llama_8b-pretrain.yaml
  - group: DeepSeek
    tag: deepseek
    models:
@@ -62,11 +50,6 @@ model_groups:
  - group: Qwen
    tag: qwen
    models:
      - model: Qwen 3 32B SFT
        mad_tag: primus_pyt_megatron_lm_train_qwen3-32b-sft
      - model: Qwen 3 32B LoRA
        mad_tag: primus_pyt_megatron_lm_train_qwen3-32b-lora
        config_name: primus_qwen2.5_7B-pretrain.yaml
      - model: Qwen 2.5 7B
        mad_tag: primus_pyt_megatron_lm_train_qwen2.5-7b
        config_name: primus_qwen2.5_7B-pretrain.yaml
--- a/docs/data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
@@ -1,15 +1,13 @@
 docker:
-  pull_tag: rocm/primus:v26.2
+  pull_tag: rocm/primus:v26.1
-  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.2/images/sha256-9148d1bfcd579bf92f44bd89090e0d8c958f149c134b4b34b9674ab559244585
+  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
  components:
-    ROCm: 7.2.0
+    ROCm: 7.1.0
-    PyTorch: 2.10.0a0+git449b176
+    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.12.3"
+    Python: "3.10"
-    Transformer Engine: 2.8.0.dev0+51f74fa7
+    Transformer Engine: 2.6.0.dev0+f141f34b
    Flash Attention: 2.8.3
-    hipBLASLt: 1.2.0-de5c1aebb6
+    hipBLASLt: 34459f66ea
    Triton: 3.6.0
    RCCL: 2.27.7
 model_groups:
  - group: Meta Llama
    tag: llama
--- a/docs/data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/pytorch-training-benchmark-models.yaml
@@ -1,11 +1,11 @@
 docker:
-  pull_tag: rocm/primus:v26.2
+  pull_tag: rocm/primus:v26.1
  docker_hub_url: https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d
  components:
-    ROCm: 7.2.0
+    ROCm: 7.1.0
-    PyTorch: 2.10.0+git94c6e04
+    PyTorch: 2.10.0.dev20251112+rocm7.1
-    Python: "3.12.3"
+    Python: "3.10"
-    Transformer Engine: 2.8.0.dev0+51f74fa7
+    Transformer Engine: 2.6.0.dev0+f141f34b
    Flash Attention: 2.8.3
    hipBLASLt: 34459f66ea
 model_groups:
--- a/docs/data/rocm-software-stack-7_2_1.png
+++ b/docs/data/rocm-software-stack-7_2_1.png
--- a/docs/how-to/deep-learning-rocm.rst
+++ b/docs/how-to/deep-learning-rocm.rst
@@ -52,6 +52,22 @@ The table below summarizes information about ROCm-enabled deep learning framewor
          <a href="https://github.com/ROCm/jax"><i class="fab fa-github fa-lg"></i></a>
    * - :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/verl-install>`
      - 
        - Docker image
      - .. raw:: html
          <a href="https://github.com/ROCm/verl"><i class="fab fa-github fa-lg"></i></a>
    * - :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/stanford-megatron-lm-install>`
      - 
        - Docker image
      - .. raw:: html
          <a href="https://github.com/ROCm/Stanford-Megatron-LM"><i class="fab fa-github fa-lg"></i></a>
    * - :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/dgl-install>`
      - 
@@ -60,6 +76,42 @@ The table below summarizes information about ROCm-enabled deep learning framewor
          <a href="https://github.com/ROCm/dgl"><i class="fab fa-github fa-lg"></i></a> 
    * - :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/megablocks-install>`
      - 
        - Docker image
      - .. raw:: html
          <a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>
    * - :doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/ray-install>`
      - 
        - Docker image
        - Wheels package
        - ROCm Base Docker image
      - .. raw:: html
          <a href="https://github.com/ROCm/ray"><i class="fab fa-github fa-lg"></i></a>
    * - :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/llama-cpp-install>`
      - 
        - Docker image
        - ROCm Base Docker image
      - .. raw:: html
          <a href="https://github.com/ROCm/llama.cpp"><i class="fab fa-github fa-lg"></i></a>
    * - :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>`
      - :doc:`link <rocm-install-on-linux:install/3rd-party/flashinfer-install>`
      - 
        - Docker image
        - ROCm Base Docker image
      - .. raw:: html
          <a href="https://github.com/ROCm/flashinfer"><i class="fab fa-github fa-lg"></i></a>
 Learn how to use your ROCm deep learning environment for training, fine-tuning, inference, and performance optimization
 through the following guides.
--- a/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.12.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.12.rst
@@ -11,7 +11,7 @@ xDiT diffusion inference
 .. caution::
-   This documentation does not reflect the latest version of xDiT diffusion
+   This documentation does not reflect the latest version of ROCm vLLM
   inference performance documentation. See
   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
   version.
--- a/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.13.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-25.13.rst
@@ -1,474 +0,0 @@
 :orphan:
 .. meta::
   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
                 prebuilt and optimized docker images.
   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
 ************************
 xDiT diffusion inference
 ************************
 .. caution::
   This documentation does not reflect the latest version of the xDiT diffusion
   inference performance documentation. See
   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
   version.
 .. _xdit-video-diffusion-2513:
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
   a prebuilt, optimized environment based on `xDiT
   <https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion model
   video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
   and MI300X (gfx942) GPUs.
   The image runs a preview version of ROCm using the new `TheRock
   <https://github.com/ROCm/TheRock>`__ build system and includes the following
   components:
   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
      .. list-table::
         :header-rows: 1
         * - Software component
           - Version
         {% for component_name, component_data in docker.components.items() %}
         * - `{{ component_name }} <{{ component_data.url }}>`_
           - {{ component_data.version }}
         {% endfor %}
 Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
 For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
 What's new
 ==========
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   {% for item in docker.whats_new %}
   * {{ item }}
   {% endfor %}
 .. _xdit-video-diffusion-supported-models-2513:
 Supported models
 ================
 The following models are supported for inference performance benchmarking.
 Some instructions, commands, and recommendations in this documentation might
 vary by model -- select one to get started.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   .. raw:: html
      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
          <div class="row gx-0">
              <div class="col-2 me-1 px-2 model-param-head">Model</div>
              <div class="row col-10 pe-0">
        {% for model_group in docker.supported_models %}
               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
        {% endfor %}
              </div>
          </div>
          <div class="row gx-0 pt-1">
              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
              <div class="row col-10 pe-0">
        {% for model_group in docker.supported_models %}
            {% set models = model_group.models %}
            {% for model in models %}
                {% if models|length % 3 == 0 %}
                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
                {% else %}
                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
                {% endif %}
            {% endfor %}
        {% endfor %}
              </div>
          </div>
      </div>
   {% for model_group in docker.supported_models %}
       {% for model in model_group.models %}
   .. container:: model-doc {{ model.js_tag }}
      .. note::
         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
         external license agreement through a third party.
       {% endfor %}
   {% endfor %}
 Performance measurements
 ========================
 To evaluate performance, the `Performance results with AMD ROCm software
 <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8543b7e6d-item-9eda09e707-tab>`__
 page provides reference throughput and serving measurements for inferencing popular AI models.
 .. important::
   The performance data presented in `Performance results with AMD ROCm
   software
   <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8543b7e6d-item-9eda09e707-tab>`__
   only reflects the latest version of this inference benchmarking environment.
   The listed measurements should not be interpreted as the peak performance
   achievable by AMD Instinct GPUs or ROCm software.
 System validation
 =================
 Before running AI workloads, it's important to validate that your AMD hardware is configured
 correctly and performing optimally.
 If you have already validated your system settings, including aspects like NUMA auto-balancing, you
 can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
 optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting.
 To test for optimal performance, consult the recommended :ref:`System health benchmarks
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 Pull the Docker image
 =====================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
   Pull the image using the following command:
   .. code-block:: shell
      docker pull {{ docker.pull_tag }}
 Validate and benchmark
 ======================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   Once the image has been downloaded you can follow these steps to
   run benchmarks and generate outputs.
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{model.js_tag}}
      The following commands are written for {{ model.model }}.
      See :ref:`xdit-video-diffusion-supported-models-2513` to switch to another available model.
     {% endfor %}
   {% endfor %}
 Choose your setup method
 ------------------------
 You can either use an existing Hugging Face cache or download the model fresh inside the container.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{model.js_tag}}
      .. tab-set::
         .. tab-item:: Option 1: Use existing Hugging Face cache
            If you already have models downloaded on your host system, you can mount your existing cache.
            1. Set your Hugging Face cache location.
               .. code-block:: shell
                  export HF_HOME=/your/hf_cache/location
            2. Download the model (if not already cached).
               .. code-block:: shell
                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
            3. Launch the container with mounted cache.
               .. code-block:: shell
                  docker run \
                      -it --rm \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --user root \
                      --device=/dev/kfd \
                      --device=/dev/dri \
                      --group-add video \
                      --ipc=host \
                      --network host \
                      --privileged \
                      --shm-size 128G \
                      --name pytorch-xdit \
                      -e HSA_NO_SCRATCH_RECLAIM=1 \
                      -e OMP_NUM_THREADS=16 \
                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
                      -e HF_HOME=/app/huggingface_models \
                      -v $HF_HOME:/app/huggingface_models \
                      {{ docker.pull_tag }}
         .. tab-item:: Option 2: Download inside container
            If you prefer to keep the container self-contained or don't have an existing cache.
            1. Launch the container
               .. code-block:: shell
                  docker run \
                      -it --rm \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --user root \
                      --device=/dev/kfd \
                      --device=/dev/dri \
                      --group-add video \
                      --ipc=host \
                      --network host \
                      --privileged \
                      --shm-size 128G \
                      --name pytorch-xdit \
                      -e HSA_NO_SCRATCH_RECLAIM=1 \
                      -e OMP_NUM_THREADS=16 \
                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
                      {{ docker.pull_tag }}
            2. Inside the container, set the Hugging Face cache location and download the model.
               .. code-block:: shell
                  export HF_HOME=/app/huggingface_models
                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
               .. warning::
                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
     {% endfor %}
   {% endfor %}
 Run inference
 =============
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_25.13-inference-models.yaml
   {% set docker = data.docker %}
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{ model.js_tag }}
      .. tab-set::
         .. tab-item:: MAD-integrated benchmarking
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. On the host machine, use this command to run the performance benchmark test on
               the `{{model.model}} <{{ model.url }}>`_ model using one node.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{model.mad_tag}} \
                      --keep-model-dir \
                      --live-output
            MAD launches a Docker container with the name
            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
            and ``{{ model.mad_tag }}_serving.csv``.
         .. tab-item:: Standalone benchmarking
            To run the benchmarks for {{ model.model }}, use the following command:
            .. code-block:: shell
            {% if model.model == "Hunyuan Video" %}
               cd /app/Hunyuanvideo
               mkdir results
               torchrun --nproc_per_node=8 run.py \
                  --model {{ model.model_repo }} \
                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
                  --height 720 --width 1280 --num_frames 129 \
                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
                  --ulysses_degree 8 \
                  --enable_tiling --enable_slicing \
                  --use_torch_compile \
                  --bench_output results
            {% endif %}
            {% if model.model == "Wan2.1" %}
               cd /app/Wan
               mkdir results
               torchrun --nproc_per_node=8 /app/Wan/run.py \
                  --task i2v \
                  --height 720 \
                  --width 1280 \
                  --model {{ model.model_repo }} \
                  --img_file_path /app/Wan/i2v_input.JPG \
                  --ulysses_degree 8 \
                  --seed 42 \
                  --num_frames 81 \
                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
                  --num_repetitions 1 \
                  --num_inference_steps 40 \
                  --use_torch_compile
            {% endif %}
            {% if model.model == "Wan2.2" %}
               cd /app/Wan
               mkdir results
               torchrun --nproc_per_node=8 /app/Wan/run.py \
                  --task i2v \
                  --height 720 \
                  --width 1280 \
                  --model {{ model.model_repo }} \
                  --img_file_path /app/Wan/i2v_input.JPG \
                  --ulysses_degree 8 \
                  --seed 42 \
                  --num_frames 81 \
                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
                  --num_repetitions 1 \
                  --num_inference_steps 40 \
                  --use_torch_compile
            {% endif %}
            {% if model.model == "FLUX.1" %}
               cd /app/Flux
               mkdir results
               torchrun --nproc_per_node=8 /app/Flux/run.py \
                  --model {{ model.model_repo }} \
                  --seed 42 \
                  --prompt "A small cat" \
                  --height 1024 \
                  --width 1024 \
                  --num_inference_steps 25 \
                  --max_sequence_length 256 \
                  --warmup_steps 5 \
                  --no_use_resolution_binning \
                  --ulysses_degree 8 \
                  --use_torch_compile \
                  --num_repetitions 50
            {% endif %}
            {% if model.model == "FLUX.1 Kontext" %}
               cd /app/Flux
               mkdir results
               torchrun --nproc_per_node=8 /app/Flux/run_usp.py \
                  --model {{ model.model_repo }} \
                  --seed 42 \
                  --prompt "Add a cool hat to the cat" \
                  --height 1024 \
                  --width 1024 \
                  --num_inference_steps 30 \
                  --max_sequence_length 512 \
                  --warmup_steps 5 \
                  --no_use_resolution_binning \
                  --ulysses_degree 8 \
                  --use_torch_compile \
                  --img_file_path /app/Flux/cat.png \
                  --model_type flux_kontext \
                  --guidance_scale 2.5 \
                  --num_repetitions 25
            {% endif %}
            {% if model.model == "FLUX.2" %}
               cd /app/Flux
               mkdir results
               torchrun --nproc_per_node=8 /app/Flux/run_usp.py \
                  --model {{ model.model_repo }} \
                  --seed 42 \
                  --prompt "Add a cool hat to the cat" \
                  --height 1024 \
                  --width 1024 \
                  --num_inference_steps 50 \
                  --max_sequence_length 512 \
                  --warmup_steps 5 \
                  --no_use_resolution_binning \
                  --ulysses_degree 8 \
                  --use_torch_compile \
                  --img_file_paths /app/Flux/cat.png \
                  --model_type flux2 \
                  --guidance_scale 4.0 \
                  --num_repetitions 25
            {% endif %}
            {% if model.model == "stable-diffusion-3.5-large" %}
               cd /app/StableDiffusion3.5
               mkdir results
               torchrun --nproc_per_node=8 /app/StableDiffusion3.5/run.py \
                  --model {{ model.model_repo }} \
                  --num_inference_steps 28 \
                  --prompt "A capybara holding a sign that reads Hello World" \
                  --use_torch_compile \
                  --pipefusion_parallel_degree 4 \
                  --use_cfg_parallel \
                  --num_repetitions 50 \
                  --dtype torch.float16 \
                  --output_path results
            {% endif %}
            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model in ["FLUX.1", "FLUX.1 Kontext", "FLUX.2"] %}results/timing.json{% elif model.model == "stable-diffusion-3.5-large"%}benchmark_results.csv{% endif %}
            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
      {% endfor %}
    {% endfor %}
 Previous versions
 =================
 See
 :doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
 to find documentation for previous releases of xDiT diffusion inference
 performance testing.
--- a/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.1.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.1.rst
@@ -1,322 +0,0 @@
 :orphan:
 .. meta::
   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
                 prebuilt and optimized docker images.
   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
 ************************
 xDiT diffusion inference
 ************************
 .. caution::
   This documentation does not reflect the latest version of the xDiT diffusion
   inference performance documentation. See
   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
   version.
 .. _xdit-video-diffusion-v261-v261:
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
   and includes the following components:
   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
      .. list-table::
         :header-rows: 1
         * - Software component
           - Version
         {% for component_name, component_data in docker.components.items() %}
         * - `{{ component_name }} <{{ component_data.url }}>`_
           - {{ component_data.version }}
         {% endfor %}
 Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
 For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
 What's new
 ==========
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   {% for item in docker.whats_new %}
   * {{ item }}
   {% endfor %}
 .. _xdit-video-diffusion-supported-models-v261:
 Supported models
 ================
 The following models are supported for inference performance benchmarking.
 Some instructions, commands, and recommendations in this documentation might
 vary by model -- select one to get started.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   .. raw:: html
      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
          <div class="row gx-0">
              <div class="col-2 me-1 px-2 model-param-head">Model</div>
              <div class="row col-10 pe-0">
        {% for model_group in docker.supported_models %}
               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
        {% endfor %}
              </div>
          </div>
          <div class="row gx-0 pt-1">
              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
              <div class="row col-10 pe-0">
        {% for model_group in docker.supported_models %}
            {% set models = model_group.models %}
            {% for model in models %}
                {% if models|length % 3 == 0 %}
                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
                {% else %}
                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
                {% endif %}
            {% endfor %}
        {% endfor %}
              </div>
          </div>
      </div>
   {% for model_group in docker.supported_models %}
       {% for model in model_group.models %}
   .. container:: model-doc {{ model.js_tag }}
      .. note::
         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
         external license agreement through a third party.
       {% endfor %}
   {% endfor %}
 System validation
 =================
 Before running AI workloads, it's important to validate that your AMD hardware is configured
 correctly and performing optimally.
 If you have already validated your system settings, including aspects like NUMA auto-balancing, you
 can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
 optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting.
 To test for optimal performance, consult the recommended :ref:`System health benchmarks
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 Pull the Docker image
 =====================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
   Pull the image using the following command:
   .. code-block:: shell
      docker pull {{ docker.pull_tag }}
 Validate and benchmark
 ======================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   Once the image has been downloaded you can follow these steps to
   run benchmarks and generate outputs.
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{model.js_tag}}
      The following commands are written for {{ model.model }}.
      See :ref:`xdit-video-diffusion-supported-models-v261` to switch to another available model.
     {% endfor %}
   {% endfor %}
 Choose your setup method
 ------------------------
 You can either use an existing Hugging Face cache or download the model fresh inside the container.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{model.js_tag}}
      .. tab-set::
         .. tab-item:: Option 1: Use existing Hugging Face cache
            If you already have models downloaded on your host system, you can mount your existing cache.
            1. Set your Hugging Face cache location.
               .. code-block:: shell
                  export HF_HOME=/your/hf_cache/location
            2. Download the model (if not already cached).
               .. code-block:: shell
                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
            3. Launch the container with mounted cache.
               .. code-block:: shell
                  docker run \
                      -it --rm \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --user root \
                      --device=/dev/kfd \
                      --device=/dev/dri \
                      --group-add video \
                      --ipc=host \
                      --network host \
                      --privileged \
                      --shm-size 128G \
                      --name pytorch-xdit \
                      -e HSA_NO_SCRATCH_RECLAIM=1 \
                      -e OMP_NUM_THREADS=16 \
                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
                      -e HF_HOME=/app/huggingface_models \
                      -v $HF_HOME:/app/huggingface_models \
                      {{ docker.pull_tag }}
         .. tab-item:: Option 2: Download inside container
            If you prefer to keep the container self-contained or don't have an existing cache.
            1. Launch the container
               .. code-block:: shell
                  docker run \
                      -it --rm \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --user root \
                      --device=/dev/kfd \
                      --device=/dev/dri \
                      --group-add video \
                      --ipc=host \
                      --network host \
                      --privileged \
                      --shm-size 128G \
                      --name pytorch-xdit \
                      -e HSA_NO_SCRATCH_RECLAIM=1 \
                      -e OMP_NUM_THREADS=16 \
                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
                      {{ docker.pull_tag }}
            2. Inside the container, set the Hugging Face cache location and download the model.
               .. code-block:: shell
                  export HF_HOME=/app/huggingface_models
                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
               .. warning::
                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
     {% endfor %}
   {% endfor %}
 Run inference
 =============
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.1-inference-models.yaml
   {% set docker = data.docker %}
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{ model.js_tag }}
      .. tab-set::
         .. tab-item:: MAD-integrated benchmarking
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. On the host machine, use this command to run the performance benchmark test on
               the `{{model.model}} <{{ model.url }}>`_ model using one node.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{model.mad_tag}} \
                      --keep-model-dir \
                      --live-output
            MAD launches a Docker container with the name
            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
            and ``{{ model.mad_tag }}_serving.csv``.
         .. tab-item:: Standalone benchmarking
            To run the benchmarks for {{ model.model }}, use the following command:
            .. code-block:: shell
               {{ model.benchmark_command
                  | map('replace', '{model_repo}', model.model_repo)
                  | map('trim')
                  | join('\n               ') }}
            The generated video will be stored under the results directory.
            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
      {% endfor %}
    {% endfor %}
 Previous versions
 =================
 See
 :doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
 to find documentation for previous releases of xDiT diffusion inference
 performance testing.
--- a/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.2.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-26.2.rst
@@ -1,320 +0,0 @@
 :orphan:
 .. meta::
   :description: Learn to validate diffusion model video generation on MI300X, MI350X and MI355X accelerators using
                 prebuilt and optimized docker images.
   :keywords: xDiT, diffusion, video, video generation, image, image generation, validate, benchmark
 ************************
 xDiT diffusion inference
 ************************
 .. caution::
   This documentation does not reflect the latest version of the xDiT diffusion
   inference performance documentation. See
   :doc:`/how-to/rocm-for-ai/inference/xdit-diffusion-inference` for the latest
   version.
 .. _xdit-video-diffusion-262:
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
   and includes the following components:
   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
      .. list-table::
         :header-rows: 1
         * - Software component
           - Version
         {% for component_name, component_data in docker.components.items() %}
         * - `{{ component_name }} <{{ component_data.url }}>`_
           - {{ component_data.version }}
         {% endfor %}
 Follow this guide to pull the required image, spin up a container, download the model, and run a benchmark.
 For preview and development releases, see `amdsiloai/pytorch-xdit <https://hub.docker.com/r/amdsiloai/pytorch-xdit>`_.
 What's new
 ==========
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   {% for item in docker.whats_new %}
   * {{ item }}
   {% endfor %}
 .. _xdit-video-diffusion-262-supported-models:
 Supported models
 ================
 The following models are supported for inference performance benchmarking.
 Some instructions, commands, and recommendations in this documentation might
 vary by model -- select one to get started.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   .. raw:: html
      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
          <div class="row gx-0">
              <div class="col-2 me-1 px-2 model-param-head">Model</div>
              <div class="row col-10 pe-0">
        {% for model_group in docker.supported_models %}
               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.js_tag }}" tabindex="0">{{ model_group.group }}</div>
        {% endfor %}
              </div>
          </div>
          <div class="row gx-0 pt-1">
              <div class="col-2 me-1 px-2 model-param-head">Variant</div>
              <div class="row col-10 pe-0">
        {% for model_group in docker.supported_models %}
            {% set models = model_group.models %}
            {% for model in models %}
                {% if models|length % 3 == 0 %}
                <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
                {% else %}
                <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.js_tag }}" data-param-group="{{ model_group.js_tag }}" tabindex="0">{{ model.model }}</div>
                {% endif %}
            {% endfor %}
        {% endfor %}
              </div>
          </div>
      </div>
   {% for model_group in docker.supported_models %}
       {% for model in model_group.models %}
   .. container:: model-doc {{ model.js_tag }}
      .. note::
         To learn more about your specific model see the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_
         or visit the `GitHub page <{{ model.github }}>`__. Note that some models require access authorization before use via an
         external license agreement through a third party.
       {% endfor %}
   {% endfor %}
 System validation
 =================
 Before running AI workloads, it's important to validate that your AMD hardware is configured
 correctly and performing optimally.
 If you have already validated your system settings, including aspects like NUMA auto-balancing, you
 can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
 optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting.
 To test for optimal performance, consult the recommended :ref:`System health benchmarks
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 Pull the Docker image
 =====================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   For this tutorial, it's recommended to use the latest ``{{ docker.pull_tag }}`` Docker image.
   Pull the image using the following command:
   .. code-block:: shell
      docker pull {{ docker.pull_tag }}
 Validate and benchmark
 ======================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   Once the image has been downloaded you can follow these steps to
   run benchmarks and generate outputs.
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{model.js_tag}}
      The following commands are written for {{ model.model }}.
      See :ref:`xdit-video-diffusion-262-supported-models` to switch to another available model.
     {% endfor %}
   {% endfor %}
 Choose your setup method
 ------------------------
 You can either use an existing Hugging Face cache or download the model fresh inside the container.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{model.js_tag}}
      .. tab-set::
         .. tab-item:: Option 1: Use existing Hugging Face cache
            If you already have models downloaded on your host system, you can mount your existing cache.
            1. Set your Hugging Face cache location.
               .. code-block:: shell
                  export HF_HOME=/your/hf_cache/location
            2. Download the model (if not already cached).
               .. code-block:: shell
                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
            3. Launch the container with mounted cache.
               .. code-block:: shell
                  docker run \
                      -it --rm \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --user root \
                      --device=/dev/kfd \
                      --device=/dev/dri \
                      --group-add video \
                      --ipc=host \
                      --network host \
                      --privileged \
                      --shm-size 128G \
                      --name pytorch-xdit \
                      -e HSA_NO_SCRATCH_RECLAIM=1 \
                      -e OMP_NUM_THREADS=16 \
                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
                      -e HF_HOME=/app/huggingface_models \
                      -v $HF_HOME:/app/huggingface_models \
                      {{ docker.pull_tag }}
         .. tab-item:: Option 2: Download inside container
            If you prefer to keep the container self-contained or don't have an existing cache.
            1. Launch the container
               .. code-block:: shell
                  docker run \
                      -it --rm \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --user root \
                      --device=/dev/kfd \
                      --device=/dev/dri \
                      --group-add video \
                      --ipc=host \
                      --network host \
                      --privileged \
                      --shm-size 128G \
                      --name pytorch-xdit \
                      -e HSA_NO_SCRATCH_RECLAIM=1 \
                      -e OMP_NUM_THREADS=16 \
                      -e CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
                      {{ docker.pull_tag }}
            2. Inside the container, set the Hugging Face cache location and download the model.
               .. code-block:: shell
                  export HF_HOME=/app/huggingface_models
                  huggingface-cli download {{ model.model_repo }} {% if model.revision %} --revision {{ model.revision }} {% endif %}
               .. warning::
                  Models will be downloaded to the container's filesystem and will be lost when the container is removed unless you persist the data with a volume.
     {% endfor %}
   {% endfor %}
 Run inference
 =============
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/previous-versions/xdit_26.2-inference-models.yaml
   {% set docker = data.docker %}
   {% for model_group in docker.supported_models %}
     {% for model in model_group.models %}
   .. container:: model-doc {{ model.js_tag }}
      .. tab-set::
         .. tab-item:: MAD-integrated benchmarking
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. On the host machine, use this command to run the performance benchmark test on
               the `{{model.model}} <{{ model.url }}>`_ model using one node.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{model.mad_tag}} \
                      --keep-model-dir \
                      --live-output
            MAD launches a Docker container with the name
            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
            and ``{{ model.mad_tag }}_serving.csv``.
         .. tab-item:: Standalone benchmarking
            To run the benchmarks for {{ model.model }}, use the following command:
            .. code-block:: shell
               {{ model.benchmark_command
                  | map('replace', '{model_repo}', model.model_repo)
                  | map('trim')
                  | join('\n               ') }}
            The generated content and timing information will be stored under the results directory.
      {% endfor %}
    {% endfor %}
 Previous versions
 =================
 See
 :doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
 to find documentation for previous releases of xDiT diffusion inference
 performance testing.
--- a/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history.rst
@@ -15,35 +15,11 @@ benchmarking, see the version-specific documentation.
     - Components
     - Resources
-   * - ``rocm/pytorch-xdit:v26.3`` (latest)
+   * - ``rocm/pytorch-xdit:v25.13`` (latest)
     - 
       * TheRock e40a6da
     -
       * :doc:`Documentation </how-to/rocm-for-ai/inference/xdit-diffusion-inference>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v26.3/images/sha256-ac78a03d2911bf1b49c001d3be2e8bd745c1bc455cb49ae972825a7986880902>`__
   * - ``rocm/pytorch-xdit:v26.2``
     -
       * `ROCm 7.11.0 preview <https://rocm.docs.amd.com/en/7.11.0-preview/about/release-notes.html>`__
       * TheRock 1728a81
     - 
-       * :doc:`Documentation <xdit-26.2>`
+       * :doc:`Documentation <../../xdit-diffusion-inference>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v26.2/images/sha256-e2c504af438bb9cf60e3869c499baa5102b3d3f62141b99c49743e755ae44008>`__
   * - ``rocm/pytorch-xdit:v26.1``
     -
       * `ROCm 7.11.0 preview <https://rocm.docs.amd.com/en/7.11.0-preview/about/release-notes.html>`__
       * TheRock 1728a81
     -
       * :doc:`Documentation <xdit-26.1>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v26.1/images/sha256-4e35ebcad47042a41389b992ecb3489b3b0a922e4c34c7a0dd1098733a3db513>`__
   * - ``rocm/pytorch-xdit:v25.13``
     -
       * `ROCm 7.11.0 preview <https://rocm.docs.amd.com/en/7.11.0-preview/about/release-notes.html>`__
       * TheRock 1728a81
     -
       * :doc:`Documentation <xdit-25.13>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-xdit/v25.13/images/sha256-81954713070d67bde08595e03f62110c8a3dd66a9ae17a77d611e01f83f0f4ef>`__
   * - ``rocm/pytorch-xdit:v25.12``
--- a/docs/how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
@@ -2,65 +2,547 @@
   :description: Learn how to validate LLM inference performance on MI300X GPUs using AMD MAD and the ROCm vLLM Docker image.
   :keywords: model, MAD, automation, dashboarding, validate
-**************
+**********************************
-vLLM inference
+vLLM inference performance testing
-**************
+**********************************
-The `ROCm-enabled vLLM Docker image
+.. _vllm-benchmark-unified-docker-1210:
 <https://hub.docker.com/r/vllm/vllm-openai-rocm/tags>`__ offers a prebuilt,
 optimized environment for large language model (LLM) inference on AMD Instinct
 MI355X, MI350X, MI325X and MI300X GPUs. This ROCm vLLM Docker image integrates
 vLLM and PyTorch tailored specifically for AMD Instinct data center GPUs.
-This container integrates ROCm, PyTorch, and vLLM with optimizations tailored
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-for AMD Instinct data center GPUs, enabling consistent and reproducible
+
-inference deployments.
+   {% set docker = data.dockers[0] %}
   The `ROCm vLLM Docker <{{ docker.docker_hub_url }}>`_ image offers a
   prebuilt, optimized environment for validating large language model (LLM)
   inference performance on AMD Instinct™ MI355X, MI350X, MI325X and MI300X
   GPUs. This ROCm vLLM Docker image integrates vLLM and PyTorch tailored
   specifically for AMD data center GPUs and includes the following components:
   .. tab-set::
      .. tab-item:: {{ docker.pull_tag }}
         .. list-table::
            :header-rows: 1
            * - Software component
              - Version
            {% for component_name, component_version in docker.components.items() %}
            * - {{ component_name }}
              - {{ component_version }}
            {% endfor %}
 With this Docker image, you can quickly test the :ref:`expected
 inference performance numbers <vllm-benchmark-performance-measurements-1210>` for
 AMD Instinct GPUs.
 What's new
 ==========
- For vLLM release notes on model support, hardware and performance improvements,
+The following is summary of notable changes since the :doc:`previous ROCm/vLLM
-  and other highlights, see the `vLLM Releases page
+Docker release <previous-versions/vllm-history>`.
  <https://github.com/vllm-project/vllm/releases>`__ on GitHub.
- It's now recommended to use the upstream vLLM documentation at `docs.vllm.ai
+- Improved performance on Llama 3 MXFP4 through AITER optimizations and improved kernel fusion.
  <https://docs.vllm.ai>`__ for the latest inference and deployment guides.
-Get started
+.. _vllm-benchmark-supported-models-1210:
 ===========
-For a consistent and portable inference environment, it's recommended to use Docker. vLLM
+Supported models
-offers a Docker image `vllm/vllm-openai-rocm
+================
 <https://hub.docker.com/r/vllm/vllm-openai-rocm/tags>`__ for deployment on AMD
 GPUs. Use the following command to pull the latest Docker image from Docker Hub.
-.. code-block:: shell
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
-   docker pull vllm/vllm-openai-rocm:latest
+   {% set docker = data.dockers[0] %}
   {% set model_groups = data.model_groups %}
-After pulling the Docker image, follow the vLLM usage documentation: `Using
+   .. _vllm-benchmark-available-models-1210:
-vLLM <https://docs.vllm.ai/en/latest/usage/>`__.
+
   The following models are supported for inference performance benchmarking
   with vLLM and ROCm. Some instructions, commands, and recommendations in this
   documentation might vary by model -- select one to get started. MXFP4 models
   are only supported on MI355X and MI350X GPUs.
   .. raw:: html
      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
         <div class="row gx-0">
            <div class="col-2 me-1 px-2 model-param-head">Model</div>
            <div class="row col-10 pe-0">
      {% for model_group in model_groups %}
               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
      {% endfor %}
            </div>
         </div>
         <div class="row gx-0 pt-1">
            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
            <div class="row col-10 pe-0">
      {% for model_group in model_groups %}
         {% set models = model_group.models %}
         {% for model in models %}
            {% if models|length % 3 == 0 %}
               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
            {% else %}
               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
            {% endif %}
         {% endfor %}
      {% endfor %}
            </div>
         </div>
      </div>
   .. _vllm-benchmark-vllm-1210:
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
   .. container:: model-doc {{ model.mad_tag }}
      {% if model.precision == "float4" %}
      .. important::
         MXFP4 is supported only on MI355X and MI350X GPUs.
      {% endif %}
      {% if model.mad_tag in ["pyt_vllm_mixtral-8x7b", "pyt_vllm_mixtral-8x7b_fp8", "pyt_vllm_mixtral-8x22b", "pyt_vllm_mixtral-8x22b_fp8", "pyt_vllm_deepseek-r1"] %}
      .. caution::
         There is a known regression with AITER for MoE models such as Mixtral and
         DeepSeek-R1. Consider using the :doc:`previous release
         <previous-versions/vllm-0.11.1-20251103>`
         ``rocm/vllm:rocm7.0.0_vllm_0.11.1_20251103`` for better performance.
      {% endif %}
      .. note::
         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`_ to learn more about your selected model.
         Some models require access authorization prior to use via an external license agreement through a third party.
      {% if model.precision == "float8" and model.model_repo.startswith("amd") %}
         This model uses FP8 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
      {% endif %}
      {% if model.precision == "float4" and model.model_repo.startswith("amd") %}
         This model uses FP4 quantization via `AMD Quark <https://quark.docs.amd.com/latest/>`__ for efficient inference on AMD GPUs.
      {% endif %}
      {% endfor %}
   {% endfor %}
 .. _vllm-benchmark-performance-measurements-1210:
 Performance measurements
 ========================
 To evaluate performance, the
 `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
 page provides reference throughput and serving measurements for inferencing popular AI models.
 .. important::
   The performance data presented in
   `Performance results with AMD ROCm software <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html>`_
   only reflects the latest version of this inference benchmarking environment.
   The listed measurements should not be interpreted as the peak performance achievable by AMD Instinct GPUs or ROCm software.
 System validation
 =================
 Before running AI workloads, it's important to validate that your AMD hardware is configured
 correctly and performing optimally.
 If you have already validated your system settings, including aspects like NUMA auto-balancing, you
 can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
 optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 To test for optimal performance, consult the recommended :ref:`System health benchmarks
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 Pull the Docker image
 =====================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
   {% set docker = data.dockers[0] %}
   Download the `ROCm vLLM Docker image <{{ docker.docker_hub_url }}>`_.
   Use the following command to pull the Docker image from Docker Hub.
   .. code-block:: shell
      docker pull {{ docker.pull_tag }}
 Benchmarking
 ============
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
   {% set docker = data.dockers[0] %}
   {% set model_groups = data.model_groups %}
   Once the setup is complete, choose between two options to reproduce the
   benchmark results:
   .. _vllm-benchmark-mad-1210:
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
      {% set serv_config = model.config.serving %}
      {% set acc_config = model.config.accuracy %}
      {% set ex_config = model.config.ex %}
   .. container:: model-doc {{model.mad_tag}}
      .. tab-set::
         .. tab-item:: MAD-integrated benchmarking
            The following run command is tailored to {{ model.model }}.
            See :ref:`vllm-benchmark-supported-models-1210` to switch to another available model.
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. On the host machine, use this command to run the performance benchmark test on
               the `{{model.model}} <{{ model.url }}>`_ model using one node with the
               :literal:`{{model.precision}}` data type.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{model.mad_tag}} \
                      --keep-model-dir \
                      --live-output
            MAD launches a Docker container with the name
            ``container_ci-{{model.mad_tag}}``. The throughput and serving reports of the
            model are collected in the following paths: ``{{ model.mad_tag }}_throughput.csv``
            and ``{{ model.mad_tag }}_serving.csv``.
            Although the :ref:`available models
            <vllm-benchmark-available-models-1210>` are preconfigured to collect
            offline throughput and online serving performance data, you can
            also change the benchmarking parameters. See the standalone
            benchmarking tab for more information.
            {% if model.tunableop %}
            .. note::
               For improved performance, consider enabling :ref:`PyTorch TunableOp <mi300x-tunableop>`.
               TunableOp automatically explores different implementations and configurations of certain PyTorch
               operators to find the fastest one for your hardware.
               By default, ``{{model.mad_tag}}`` runs with TunableOp disabled (see
               `<https://github.com/ROCm/MAD/blob/develop/models.json>`__). To enable it, include
               the ``--tunableop on`` argument in your run.
               Enabling TunableOp triggers a two-pass run -- a warm-up followed by the
               performance-collection run.
            {% endif %}
         .. tab-item:: Standalone benchmarking
            The following commands are optimized for {{ model.model }}.
            See :ref:`vllm-benchmark-supported-models-1210` to switch to another available model.
            .. seealso::
               For more information on configuration, see the `config files
               <https://github.com/ROCm/MAD/tree/develop/scripts/vllm/configs>`__
               in the MAD repository. Refer to the `vLLM engine <https://docs.vllm.ai/en/latest/configuration/engine_args.html#engineargs>`__
               for descriptions of available configuration options
               and `Benchmarking vLLM <https://github.com/vllm-project/vllm/blob/main/benchmarks/README.md>`__ for
               additional benchmarking information.
            .. rubric:: Launch the container
            You can run the vLLM benchmark tool independently by starting the
            `Docker container <{{ docker.docker_hub_url }}>`_ as shown
            in the following snippet.
            .. code-block:: shell
               docker pull {{ docker.pull_tag }}
               docker run -it \
                   --device=/dev/kfd \
                   --device=/dev/dri \
                   --group-add video \
                   --shm-size 16G \
                   --security-opt seccomp=unconfined \
                   --security-opt apparmor=unconfined \
                   --cap-add=SYS_PTRACE \
                   -v $(pwd):/workspace \
                   --env HUGGINGFACE_HUB_CACHE=/workspace \
                   --name test \
                   {{ docker.pull_tag }}
            .. rubric:: Run the inference benchmarks
            .. tab-set::
               .. tab-item:: Latency command
                  Use the following command to start the latency benchmark.
                  .. code-block:: shell
                     model={{ model.model_repo }}
                     tp={{ serv_config.tp }}
                     batch_size=16
                     in={{ serv_config.inp | default(1024) }}
                     out={{ serv_config.out | default(1024) }}
                     dtype={{ serv_config.dtype | default("auto") }}
                     kv_cache_dtype={{ ex_config.kv_cache_dtype | default("auto") }}
                     max_num_seqs={{ ex_config.max_num_seqs | default(1024) }}
                     max_num_batched_tokens={{ ex_config.max_num_batched_tokens }}
                     max_model_len={{ ex_config.max_model_len }}
                     vllm bench latency --model $model \
                         -tp $tp \
                         --batch-size $batch_size \
                         --input-len $in \
                         --output-len $out \
                         --dtype $dtype \
                         --kv-cache-dtype $kv_cache_dtype \
                         --max-num-seqs $max_num_seqs \
                         --max-num-batched-tokens $max_num_batched_tokens \
                         --max-model-len $max_model_len \
                         --output-json ${model}_throughput.json \
               .. tab-item:: Throughput command
                  Use the following command to start the throughput benchmark.
                  .. code-block:: shell
                     model={{ model.model_repo }}
                     tp={{ serv_config.tp }}
                     num_prompts={{ model.config.num_prompts | default(1024) }}
                     in={{ serv_config.inp | default(1024) }}
                     out={{ serv_config.out | default(1024) }}
                     dtype={{ serv_config.dtype | default("auto") }}
                     kv_cache_dtype={{ ex_config.kv_cache_dtype | default("auto") }}
                     max_num_seqs={{ ex_config.max_num_seqs | default(1024) }}
                     max_num_batched_tokens={{ ex_config.max_num_batched_tokens }}
                     max_model_len={{ ex_config.max_model_len }}
                     vllm bench throughput --model $model \
                         -tp $tp \
                         --num-prompts $num_prompts \
                         --input-len $in \
                         --output-len $out \
                         --dtype $dtype \
                         --kv-cache-dtype $kv_cache_dtype \
                         --max-num-seqs $max_num_seqs \
                         --max-num-batched-tokens $max_num_batched_tokens \
                         --max-model-len $max_model_len \
                         --trust-remote-code \
                         --output-json ${model}_throughput.json \
                         --gpu-memory-utilization {{ model.config.gpu_memory_utilization | default(0.9) }}
               .. tab-item:: Serving command
                  1. Start the server using the following command:
                     .. code-block:: shell
                        model={{ model.model_repo }}
                        tp={{ serv_config.tp }}
                        dtype={{ serv_config.dtype }}
                        kv_cache_dtype={{ ex_config.kv_cache_dtype }}
                        max_num_seqs=1024
                        max_num_batched_tokens={{ ex_config.max_num_batched_tokens }}
                        max_model_len={{ ex_config.max_model_len }}
                        vllm serve $model \
                            -tp $tp \
                            --dtype $dtype \
                            --kv-cache-dtype $kv_cache_dtype \
                            --max-num-seqs $max_num_seqs \
                            --max-num-batched-tokens $max_num_batched_tokens \
                            --max-model-len $max_model_len \
                            --no-enable-prefix-caching \
                            --swap-space 16 \
                            --disable-log-requests
                     Wait until the model has loaded and the server is ready to accept requests.
                  2. On another terminal on the same machine, run the benchmark:
                     .. code-block:: shell
                        # Connect to the container
                        docker exec -it test bash
                        # Wait for the server to start
                        until curl -s http://localhost:8000/v1/models; do sleep 30; done
                        # Run the benchmark
                        model={{ model.model_repo }}
                        max_concurrency=1
                        num_prompts=10
                        in={{ serv_config.inp | default("1024") }}
                        out={{ serv_config.out | default("1024") }}
                        vllm bench serve --model $model \
                            --percentile-metrics "ttft,tpot,itl,e2el" \
                            --dataset-name random \
                            --ignore-eos \
                            --max-concurrency $max_concurrency \
                            --num-prompts $num_prompts \
                            --random-input-len $in \
                            --random-output-len $out \
                            --trust-remote-code \
                            --save-result \
                            --result-filename ${model}_serving.json
               {% if acc_config %}
               .. tab-item:: Accuracy command
                  1. Start the server using the following command:
                     .. code-block:: shell
                        model={{ model.model_repo }}
                        tp={{ acc_config.tp }}
                        dtype={{ acc_config.dtype }}
                        kv_cache_dtype={{ ex_config.kv_cache_dtype }}
                        max_num_seqs=1024
                        max_num_batched_tokens={{ ex_config.max_num_batched_tokens }}
                        max_model_len={{ ex_config.max_model_len }}
                        vllm serve $model \
                            -tp $tp \
                            --dtype $dtype \
                            --kv-cache-dtype $kv_cache_dtype \
                            --max-num-seqs $max_num_seqs \
                            --max-num-batched-tokens $max_num_batched_tokens \
                            --max-model-len $max_model_len \
                            --no-enable-prefix-caching \
                            --swap-space 16 \
                            --disable-log-requests
                     Wait until the model has loaded and the server is ready to accept requests.
                  2. On another terminal on the same machine, run the benchmark:
                     .. code-block:: shell
                        # Connect to the container
                        docker exec -it test bash
                        # Wait for the server to start
                        until curl -s http://localhost:8000/v1/models; do sleep 30; done
                        # Install lm-eval
                        pip install lm-eval[api]
                        # Run the benchmark
                        model={{ acc_config.model }}
                        lm_eval --model local-completions \
                            --model_args model=$model,max_gen_toks=2048,num_concurrent=256,max_retries=10,base_url=http://localhost:8000/v1/completions \
                            --tasks gsm8k --limit 250 --output_path ./tmp
               {% endif %}
            .. raw:: html
               <style>
               mjx-container[jax="CHTML"][display="true"] {
                  text-align: left;
                  margin: 0;
               }
               </style>
            .. note::
               Throughput is calculated as:
               - .. math:: throughput\_tot = requests \times (\mathsf{\text{input lengths}} + \mathsf{\text{output lengths}}) / elapsed\_time
               - .. math:: throughput\_gen = requests \times \mathsf{\text{output lengths}} / elapsed\_time
      {% endfor %}
   {% endfor %}
 Advanced usage
 ==============
 For information on experimental features and known issues related to ROCm optimization efforts on vLLM,
 see the developer's guide at `<https://github.com/ROCm/vllm/blob/documentation/docs/dev-docker/README.md>`__.
 .. note::
   If you’re using this Docker image on other AMD GPUs such as the AMD Instinct MI200 Series or Radeon, add ``export VLLM_ROCM_USE_AITER=0`` to your command, since AITER is only supported on gfx942 and gfx950 architectures.
 Reproducing the Docker image
 ----------------------------
 To reproduce this ROCm-enabled vLLM Docker image release, follow these steps:
 1. Clone the `vLLM repository <https://github.com/vllm-project/vllm>`__.
   .. code-block:: shell
      git clone https://github.com/vllm-project/vllm.git
      cd vllm
 2. Use the following command to build the image directly from the specified commit.
   .. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/vllm-benchmark-models.yaml
      {% set docker = data.dockers[0] %}
      .. code-block:: shell
         docker build -f docker/Dockerfile.rocm \
             --build-arg REMOTE_VLLM=1 \
             --build-arg VLLM_REPO=https://github.com/ROCm/vllm \
             --build-arg VLLM_BRANCH="{{ docker.dockerfile.commit }}" \
             -t vllm-rocm .
   .. tip::
      Replace ``vllm-rocm`` with your desired image tag.
 Known issues
 ============
 There is a known regression with AITER for MoE models such as Mixtral and
 DeepSeek-R1. Consider using the :doc:`previous release
 <previous-versions/vllm-0.11.1-20251103>`
 (``rocm/vllm:rocm7.0.0_vllm_0.11.1_20251103``) for better performance.
 Further reading
 ===============
 - To learn more about the options for latency and throughput benchmark scripts,
  see `<https://github.com/ROCm/vllm/tree/main/benchmarks>`_.
 - To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
 - To learn more about system settings and management practices to configure your system for
  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
 - See :ref:`fine-tuning-llms-vllm` and :ref:`mi300x-vllm-optimization` for
  a brief introduction to vLLM and optimization strategies.
- For a list of other ready-made Docker images for AI with ROCm, see
+- For application performance optimization strategies for HPC and AI workloads,
-  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`__.
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
-.. _vllm-inference-previous-versions:
+- For a list of other ready-made Docker images for AI with ROCm, see
  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 Previous versions
 =================
-It's now recommended to use the upstream vLLM documentation at `docs.vllm.ai
+See :doc:`previous-versions/vllm-history` to find documentation for previous releases
-<https://docs.vllm.ai>`__ for the latest deployment guides.
+of the ``ROCm/vllm`` Docker image.
 You can find legacy versions of this documentation at
 :doc:`previous-versions/vllm-history` which provide instructions for
 inference performance testing for select models. See the `Use AMD's Docker
 images
 <https://docs.vllm.ai/en/stable/deployment/docker/#use-amds-docker-images>`__
 note in the vLLM documentation for more information.
--- a/docs/how-to/rocm-for-ai/inference/index.rst
+++ b/docs/how-to/rocm-for-ai/inference/index.rst
@@ -20,7 +20,7 @@ training, fine-tuning, and inference. It leverages popular machine learning fram
 - :doc:`LLM inference frameworks <llm-inference-frameworks>`
- :doc:`vLLM inference <benchmark-docker/vllm>`
+- :doc:`vLLM inference performance testing <benchmark-docker/vllm>`
 - :doc:`PyTorch inference performance testing <benchmark-docker/pytorch-inference>`
--- a/docs/how-to/rocm-for-ai/inference/xdit-diffusion-inference.rst
+++ b/docs/how-to/rocm-for-ai/inference/xdit-diffusion-inference.rst
@@ -13,10 +13,15 @@ xDiT diffusion inference
   {% set docker = data.docker %}
-   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers a prebuilt, optimized environment based on `xDiT <https://github.com/xdit-project/xDiT>`_ for
+   The `rocm/pytorch-xdit <{{ docker.docker_hub_url }}>`_ Docker image offers
-   benchmarking diffusion model video and image generation on gfx942 and gfx950 series (AMD Instinct™ MI300X, MI325X, MI350X, and MI355X) GPUs.
+   a prebuilt, optimized environment based on `xDiT
-   The image runs ROCm **{{docker.ROCm}}** (preview) based on `TheRock <https://github.com/ROCm/TheRock>`_
+   <https://github.com/xdit-project/xDiT>`_ for benchmarking diffusion model
-   and includes the following components:
+   video and image generation on AMD Instinct MI355X, MI350X (gfx950), MI325X,
   and MI300X (gfx942) GPUs.
   The image runs a preview version of ROCm using the new `TheRock
   <https://github.com/ROCm/TheRock>`__ build system and includes the following
   components:
   .. dropdown:: Software components - {{ docker.pull_tag.split('-')|last }}
@@ -100,6 +105,22 @@ vary by model -- select one to get started.
       {% endfor %}
   {% endfor %}
 Performance measurements
 ========================
 To evaluate performance, the `Performance results with AMD ROCm software
 <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8543b7e6d-item-9eda09e707-tab>`__
 page provides reference throughput and serving measurements for inferencing popular AI models.
 .. important::
   The performance data presented in `Performance results with AMD ROCm
   software
   <https://www.amd.com/en/developer/resources/rocm-hub/dev-ai/performance-results.html#tabs-a8543b7e6d-item-9eda09e707-tab>`__
   only reflects the latest version of this inference benchmarking environment.
   The listed measurements should not be interpreted as the peak performance
   achievable by AMD Instinct GPUs or ROCm software.
 System validation
 =================
@@ -290,13 +311,146 @@ Run inference
            To run the benchmarks for {{ model.model }}, use the following command:
            .. code-block:: shell
            {% if model.model == "Hunyuan Video" %}
               cd /app/Hunyuanvideo
               mkdir results
-               {{ model.benchmark_command
+               torchrun --nproc_per_node=8 run.py \
-                  | map('replace', '{model_repo}', model.model_repo)
+                  --model {{ model.model_repo }} \
-                  | map('trim')
+                  --prompt "In the large cage, two puppies were wagging their tails at each other." \
-                  | join('\n               ') }}
+                  --height 720 --width 1280 --num_frames 129 \
                  --num_inference_steps 50 --warmup_steps 1 --n_repeats 1 \
                  --ulysses_degree 8 \
                  --enable_tiling --enable_slicing \
                  --use_torch_compile \
                  --bench_output results
-            The generated content and timing information will be stored under the results directory.
+            {% endif %}
            {% if model.model == "Wan2.1" %}
               cd /app/Wan
               mkdir results
               torchrun --nproc_per_node=8 /app/Wan/run.py \
                  --task i2v \
                  --height 720 \
                  --width 1280 \
                  --model {{ model.model_repo }} \
                  --img_file_path /app/Wan/i2v_input.JPG \
                  --ulysses_degree 8 \
                  --seed 42 \
                  --num_frames 81 \
                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
                  --num_repetitions 1 \
                  --num_inference_steps 40 \
                  --use_torch_compile
            {% endif %}
            {% if model.model == "Wan2.2" %}
               cd /app/Wan
               mkdir results
               torchrun --nproc_per_node=8 /app/Wan/run.py \
                  --task i2v \
                  --height 720 \
                  --width 1280 \
                  --model {{ model.model_repo }} \
                  --img_file_path /app/Wan/i2v_input.JPG \
                  --ulysses_degree 8 \
                  --seed 42 \
                  --num_frames 81 \
                  --prompt "Summer beach vacation style, a white cat wearing sunglasses sits on a surfboard. The fluffy-furred feline gazes directly at the camera with a relaxed expression. Blurred beach scenery forms the background featuring crystal-clear waters, distant green hills, and a blue sky dotted with white clouds. The cat assumes a naturally relaxed posture, as if savoring the sea breeze and warm sunlight. A close-up shot highlights the feline's intricate details and the refreshing atmosphere of the seaside." \
                  --num_repetitions 1 \
                  --num_inference_steps 40 \
                  --use_torch_compile
            {% endif %}
            {% if model.model == "FLUX.1" %}
               cd /app/Flux
               mkdir results
               torchrun --nproc_per_node=8 /app/Flux/run.py \
                  --model {{ model.model_repo }} \
                  --seed 42 \
                  --prompt "A small cat" \
                  --height 1024 \
                  --width 1024 \
                  --num_inference_steps 25 \
                  --max_sequence_length 256 \
                  --warmup_steps 5 \
                  --no_use_resolution_binning \
                  --ulysses_degree 8 \
                  --use_torch_compile \
                  --num_repetitions 50
            {% endif %}
            {% if model.model == "FLUX.1 Kontext" %}
               cd /app/Flux
               mkdir results
               torchrun --nproc_per_node=8 /app/Flux/run_usp.py \
                  --model {{ model.model_repo }} \
                  --seed 42 \
                  --prompt "Add a cool hat to the cat" \
                  --height 1024 \
                  --width 1024 \
                  --num_inference_steps 30 \
                  --max_sequence_length 512 \
                  --warmup_steps 5 \
                  --no_use_resolution_binning \
                  --ulysses_degree 8 \
                  --use_torch_compile \
                  --img_file_path /app/Flux/cat.png \
                  --model_type flux_kontext \
                  --guidance_scale 2.5 \
                  --num_repetitions 25
            {% endif %}
            {% if model.model == "FLUX.2" %}
               cd /app/Flux
               mkdir results
               torchrun --nproc_per_node=8 /app/Flux/run_usp.py \
                  --model {{ model.model_repo }} \
                  --seed 42 \
                  --prompt "Add a cool hat to the cat" \
                  --height 1024 \
                  --width 1024 \
                  --num_inference_steps 50 \
                  --max_sequence_length 512 \
                  --warmup_steps 5 \
                  --no_use_resolution_binning \
                  --ulysses_degree 8 \
                  --use_torch_compile \
                  --img_file_paths /app/Flux/cat.png \
                  --model_type flux2 \
                  --guidance_scale 4.0 \
                  --num_repetitions 25
            {% endif %}
            {% if model.model == "stable-diffusion-3.5-large" %}
               cd /app/StableDiffusion3.5 
               mkdir results
               torchrun --nproc_per_node=8 /app/StableDiffusion3.5/run.py \
                  --model {{ model.model_repo }} \
                  --num_inference_steps 28 \
                  --prompt "A capybara holding a sign that reads Hello World" \
                  --use_torch_compile \
                  --pipefusion_parallel_degree 4 \
                  --use_cfg_parallel \
                  --num_repetitions 50 \
                  --dtype torch.float16 \
                  --output_path results
            {% endif %}
            The generated video will be stored under the results directory. For the actual benchmark step runtimes, see {% if model.model == "Hunyuan Video" %}stdout.{% elif model.model in ["Wan2.1", "Wan2.2"] %}results/outputs/rank0_*.json{% elif model.model in ["FLUX.1", "FLUX.1 Kontext", "FLUX.2"] %}results/timing.json{% elif model.model == "stable-diffusion-3.5-large"%}benchmark_results.csv{% endif %}
            {% if model.model == "FLUX.1" %}You may also use ``run_usp.py`` which implements USP without modifying the default diffusers pipeline. {% endif %}
      {% endfor %}
    {% endfor %}
@@ -304,7 +458,5 @@ Run inference
 Previous versions
 =================
-See
+See :doc:`benchmark-docker/previous-versions/xdit-history` to find documentation for previous releases
-:doc:`/how-to/rocm-for-ai/inference/benchmark-docker/previous-versions/xdit-history`
+of xDiT diffusion inference performance testing.
 to find documentation for previous releases of xDiT diffusion inference
 performance testing.
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
@@ -2,18 +2,13 @@
   :description: How to train a model using JAX MaxText for ROCm.
   :keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
-********************************************
+******************************************
-Training a model with Primus and JAX MaxText
+Training a model with JAX MaxText on ROCm
-********************************************
+******************************************
 The JAX MaxText for ROCm training Docker image provides a prebuilt environment
 for training on AMD Instinct MI355X, MI350X, MI325X, and MI300X GPUs, with
 essential components such as JAX, XLA, ROCm libraries, and MaxText utilities.
 The image also integrates with `Primus <https://github.com/AMD-AGI/Primus>`__,
 a high-level training framework that supports multiple backends. You can use
 the unified ``primus-cli`` to run training jobs using the JAX MaxText backend.
 The MaxText for ROCm training Docker image
 provides a prebuilt environment for training on AMD Instinct MI355X, MI350X, MI325X, and MI300X GPUs,
 including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
 It includes the following software components:
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/jax-maxtext-benchmark-models.yaml
@@ -52,7 +47,7 @@ MaxText with on ROCm provides the following key features to train large language
 - NANOO FP8 (for MI300X series GPUs) and FP8 (for MI355X and MI350X) quantization support
-.. _amd-maxtext-model-support-v26.2:
+.. _amd-maxtext-model-support-v26.1:
 Supported models
 ================
@@ -134,7 +129,7 @@ Use the following command to pull the Docker image from Docker Hub.
      docker pull {{ docker.pull_tag }}
-.. _amd-maxtext-multi-node-setup-v26.2:
+.. _amd-maxtext-multi-node-setup-v26.1:
 Multi-node configuration
 ------------------------
@@ -142,7 +137,7 @@ Multi-node configuration
 See :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your
 environment for multi-node training.
-.. _amd-maxtext-get-started-v26.2:
+.. _amd-maxtext-get-started-v26.1:
 Benchmarking
 ============
@@ -163,145 +158,11 @@ benchmark results:
      .. tab-set::
         {% if model.primus_config_name %}
         .. tab-item:: Primus benchmarking
            .. container:: model-doc {{ model.mad_tag }}
               The following run commands are tailored to {{ model.model }}.
               See :ref:`amd-maxtext-model-support-v26.2` to switch to another available model.
               .. rubric:: Download the Docker image and required packages
               1. Pull the ``{{ docker.pull_tag }}`` Docker image from Docker Hub.
                  .. code-block:: shell
                     docker pull {{ docker.pull_tag }}
               2. Run the Docker container.
                  .. code-block:: shell
                     docker run -it \
                         --device /dev/dri \
                         --device /dev/kfd \
                         --network host \
                         --ipc host \
                         --group-add video \
                         --cap-add SYS_PTRACE \
                         --security-opt seccomp=unconfined \
                         --privileged \
                         -v $HOME:$HOME \
                         -v $HOME/.ssh:/root/.ssh \
                         -v $HF_HOME:/hf_cache \
                         -e HF_HOME=/hf_cache \
                         -e MAD_SECRETS_HFTOKEN=$MAD_SECRETS_HFTOKEN
                         --shm-size 64G \
                         --name training_env \
                         {{ docker.pull_tag }}
                  Use these commands if you exit the ``training_env`` container and need to return to it.
                  .. code-block:: shell
                     docker start training_env
                     docker exec -it training_env bash
               3. Clone the Primus repository.
                  .. code-block:: shell
                     git clone https://github.com/AMD-AIG-AIMA/Primus.git
                     cd Primus
                     git checkout dev/fuyuajin/maxtext-backend-test
                     git submodule update --init third_party/maxtext/
               .. rubric:: Run the training job with primus-cli
               For detailed usage instructions for ``primus-cli``, see the
               `Primus CLI User Guide
               <https://github.com/AMD-AGI/Primus/blob/main/docs/cli/PRIMUS-CLI-GUIDE.md>`__.
               Use the following examples to run training with ``primus-cli``:
               - Direct mode: run directly on the current host or within an existing Docker container
                 .. tab-set::
                    .. tab-item:: MI355X
                       :sync: mi355x
                       .. code-block:: shell
                          ./primus-cli direct \
                            -- train pretrain \
                            --config examples/maxtext/configs/MI355X/{{ model.primus_config_name }}
                    .. tab-item:: MI300X
                       :sync: mi300x
                       .. code-block:: shell
                          ./primus-cli direct \
                            -- train pretrain \
                            --config examples/maxtext/configs/MI300X/{{ model.primus_config_name }}
               - Container mode: run in Docker containers
                 .. tab-set::
                    .. tab-item:: MI355X
                       :sync: mi355x
                       .. code-block:: shell
                          ./primus-cli container --image {{ docker.pull_tag }} \
                            -- train pretrain \
                            --config examples/maxtext/configs/MI355X/{{ model.primus_config_name }}
                    .. tab-item:: MI300X
                       :sync: mi300x
                       .. code-block:: shell
                          ./primus-cli container --image rocm/jax-training:maxtext-v26.2 \
                            -- train pretrain \
                            --config examples/maxtext/configs/MI300X/{{ model.primus_config_name }}
               - Slurm mode: run distributed training on a Slurm cluster
                 .. tab-set::
                    .. tab-item:: MI355X
                       :sync: mi355x
                       .. code-block:: shell
                          # Use a custom config file, where you can specify
                          # the Docker image and set environment variables.
                          ./primus-cli --config my_maxtext_config.yaml slurm srun -N 8 \
                            -- train pretrain \
                            --config examples/maxtext/configs/MI355X/{{ model.primus_config_name }}
                    .. tab-item:: MI300X
                       :sync: mi300x
                       .. code-block:: shell
                          # Use a custom config file, where you can specify
                          # the Docker image and set environment variables.
                          ./primus-cli --config my_maxtext_config.yaml slurm srun -N 8 \
                            -- train pretrain \
                            --config examples/maxtext/configs/MI300X/{{ model.primus_config_name }}
         {% endif %}
         {% if model.mad_tag and "single-node" in model.doc_options %}
         .. tab-item:: MAD-integrated benchmarking
            The following run command is tailored to {{ model.model }}.
-            See :ref:`amd-maxtext-model-support-v26.2` to switch to another available model.
+            See :ref:`amd-maxtext-model-support-v26.1` to switch to another available model.
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
@@ -332,7 +193,7 @@ benchmark results:
         .. tab-item:: Standalone benchmarking
            The following commands are optimized for {{ model.model }}. See
-            :ref:`amd-maxtext-model-support-v26.2` to switch to another
+            :ref:`amd-maxtext-model-support-v26.1` to switch to another
            available model. Some instructions and resources might not be
            available for all models and configurations.
@@ -452,7 +313,7 @@ benchmark results:
            [docker_image] (optional)
               The Docker image to use. If not specified, it defaults to
-               ``rocm/jax-training:maxtext-v26.2``.
+               ``rocm/jax-training:maxtext-v26.1``.
            For example, to run a multi-node training benchmark on {{ model.model }}:
@@ -477,7 +338,7 @@ benchmark results:
         {% else %}
            .. rubric:: Multi-node training
-            For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v26.2`
+            For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v26.1`
            with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/env_scripts>`__.
         {% endif %}
      {% endfor %}
@@ -490,6 +351,10 @@ Known issues
  a workaround, turn off input sequence packing (``packing=False``).
  This will be fixed in a future release.
 - Docker ``rocm/jax-training:maxtext-v26.1`` does not include `Primus
  <https://github.com/AMD-AGI/Primus/tree/main>`__. It is planned to be
  supported in a future release.
 Further reading
 ===============
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history.rst
@@ -17,22 +17,6 @@ previous releases of the ``ROCm/jax-training`` Docker image on `Docker Hub <http
     - Components
     - Resources
   * - 26.2 (latest)
     -
       * ROCm 7.1.1
       * JAX 0.8.2
     -
       * :doc:`Documentation <../jax-maxtext>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.2/images/sha256-a89643388487b1e2fc6b6ef7bd3c44378c05d217309c977a1c18c72d05ebcaeb>`__
   * - 26.1
     -
       * ROCm 7.1.1
       * JAX 0.8.2
     -
       * :doc:`Documentation <jax-maxtext-v26.1>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/jax-training/maxtext-v26.1/images/sha256-901083bde353fe6362ada3036e452c792b2c96124e5900f4e9b5946c02ff9d6a>`__
   * - 25.11
     -
       * ROCm 7.1.0
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v26.1.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v26.1.rst
@@ -1,380 +0,0 @@
 :orphan:
 .. meta::
   :description: How to train a model using JAX MaxText for ROCm.
   :keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker
 ******************************************
 Training a model with JAX MaxText on ROCm
 ******************************************
 .. caution::
   This documentation does not reflect the latest version of ROCm JAX MaxText
   training performance documentation. See :doc:`../jax-maxtext` for the latest version.
 The MaxText for ROCm training Docker image
 provides a prebuilt environment for training on AMD Instinct MI355X, MI350X, MI325X, and MI300X GPUs,
 including essential components like JAX, XLA, ROCm libraries, and MaxText utilities.
 It includes the following software components:
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v26.1-benchmark-models.yaml
   {% set dockers = data.dockers %}
   .. tab-set::
      {% for docker in dockers %}
      {% set jax_version = docker.components["JAX"] %}
      .. tab-item:: ``{{ docker.pull_tag }}``
         :sync: {{ docker.pull_tag }}
         .. list-table::
            :header-rows: 1
            * - Software component
              - Version
            {% for component_name, component_version in docker.components.items() %}
            * - {{ component_name }}
              - {{ component_version }}
            {% endfor %}
      {% endfor %}
 MaxText with on ROCm provides the following key features to train large language models efficiently:
 - Transformer Engine (TE)
 - Flash Attention (FA) 3 -- with or without sequence input packing
 - GEMM tuning
 - Multi-node support
 - NANOO FP8 (for MI300X series GPUs) and FP8 (for MI355X and MI350X) quantization support
 .. _amd-maxtext-model-support-v26.1:
 Supported models
 ================
 The following models are pre-optimized for performance on AMD Instinct
 GPUs. Some instructions, commands, and available training
 configurations in this documentation might vary by model -- select one to get
 started.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v26.1-benchmark-models.yaml
   {% set model_groups = data.model_groups %}
   .. raw:: html
      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
         <div class="row gx-0">
            <div class="col-2 me-1 px-2 model-param-head">Model</div>
            <div class="row col-10 pe-0">
      {% for model_group in model_groups %}
               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
      {% endfor %}
            </div>
         </div>
         <div class="row gx-0 pt-1">
            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
            <div class="row col-10 pe-0">
      {% for model_group in model_groups %}
         {% set models = model_group.models %}
         {% for model in models %}
            {% if models|length % 3 == 0 %}
               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
            {% else %}
               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
            {% endif %}
         {% endfor %}
      {% endfor %}
            </div>
         </div>
      </div>
 .. note::
   Some models, such as Llama 3, require an external license agreement through
   a third party (for example, Meta).
 System validation
 =================
 Before running AI workloads, it's important to validate that your AMD hardware is configured
 correctly and performing optimally.
 If you have already validated your system settings, including aspects like NUMA auto-balancing, you
 can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
 optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 To test for optimal performance, consult the recommended :ref:`System health benchmarks
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 Environment setup
 =================
 This Docker image is optimized for specific model configurations outlined
 as follows. Performance can vary for other training workloads, as AMD
 doesn’t validate configurations and run conditions outside those described.
 Pull the Docker image
 ---------------------
 Use the following command to pull the Docker image from Docker Hub.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v26.1-benchmark-models.yaml
   {% set docker = data.dockers[0] %}
   .. code-block:: shell
      docker pull {{ docker.pull_tag }}
 .. _amd-maxtext-multi-node-setup-v26.1:
 Multi-node configuration
 ------------------------
 See :doc:`/how-to/rocm-for-ai/system-setup/multi-node-setup` to configure your
 environment for multi-node training.
 .. _amd-maxtext-get-started-v26.1:
 Benchmarking
 ============
 Once the setup is complete, choose between two options to reproduce the
 benchmark results:
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/jax-maxtext-v26.1-benchmark-models.yaml
   .. _vllm-benchmark-mad:
   {% set docker = data.dockers[0] %}
   {% set model_groups = data.model_groups %}
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
   .. container:: model-doc {{model.mad_tag}}
      .. tab-set::
         {% if model.mad_tag and "single-node" in model.doc_options %}
         .. tab-item:: MAD-integrated benchmarking
            The following run command is tailored to {{ model.model }}.
            See :ref:`amd-maxtext-model-support-v26.1` to switch to another available model.
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. Use this command to run the performance benchmark test on the {{ model.model }} model
               using one GPU with the :literal:`{{model.precision}}` data type on the host machine.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{model.mad_tag}} \
                      --keep-model-dir \
                      --live-output \
                      --timeout 28800
            MAD launches a Docker container with the name
            ``container_ci-{{model.mad_tag}}``. The latency and throughput reports of the
            model are collected in the following path: ``~/MAD/perf.csv/``.
         {% endif %}
         .. tab-item:: Standalone benchmarking
            The following commands are optimized for {{ model.model }}. See
            :ref:`amd-maxtext-model-support-v26.1` to switch to another
            available model. Some instructions and resources might not be
            available for all models and configurations.
            .. rubric:: Download the Docker image and required scripts
            Run the JAX MaxText benchmark tool independently by starting the
            Docker container as shown in the following snippet.
            .. code-block:: shell
               docker pull {{ docker.pull_tag }}
            {% if model.model_repo and "single-node" in model.doc_options %}
            .. rubric:: Single node training
            1. Set up environment variables.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN=<Your Hugging Face token>
                  export HF_HOME=<Location of saved/cached Hugging Face models>
               ``MAD_SECRETS_HFTOKEN`` is your Hugging Face access token to access models, tokenizers, and data.
               See `User access tokens <https://huggingface.co/docs/hub/en/security-tokens>`__.
               ``HF_HOME`` is where ``huggingface_hub`` will store local data. See `huggingface_hub CLI <https://huggingface.co/docs/huggingface_hub/main/en/guides/cli#huggingface-cli-download>`__.
               If you already have downloaded or cached Hugging Face artifacts, set this variable to that path.
               Downloaded files typically get cached to ``~/.cache/huggingface``.
            2. Launch the Docker container.
               .. code-block:: shell
                  docker run -it \
                      --device=/dev/dri \
                      --device=/dev/kfd \
                      --network host \
                      --ipc host \
                      --group-add video \
                      --cap-add=SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --privileged \
                      -v $HOME:$HOME \
                      -v $HOME/.ssh:/root/.ssh \
                      -v $HF_HOME:/hf_cache \
                      -e HF_HOME=/hf_cache \
                      -e MAD_SECRETS_HFTOKEN=$MAD_SECRETS_HFTOKEN
                      --shm-size 64G \
                      --name training_env \
                      {{ docker.pull_tag }}
            3. In the Docker container, clone the ROCm MAD repository and navigate to the
               benchmark scripts directory at ``MAD/scripts/jax-maxtext``.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD/scripts/jax-maxtext
            4. Run the setup scripts to install libraries and datasets needed
               for benchmarking.
               .. code-block:: shell
                  ./jax-maxtext_benchmark_setup.sh -m {{ model.model_repo }}
            5. To run the training benchmark without quantization, use the following command:
               .. code-block:: shell
                  ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }}
               For quantized training, run the script with the appropriate option for your Instinct GPU.
               .. tab-set::
                  .. tab-item:: MI355X and MI350X
                     For ``fp8`` quantized training on MI355X and MI350X GPUs, use the following command:
                     .. code-block:: shell
                        ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} -q fp8
                  {% if model.model_repo not in ["Llama-3.1-70B", "Llama-3.3-70B"] %}
                  .. tab-item:: MI325X and MI300X
                     For ``nanoo_fp8`` quantized training on MI300X series GPUs, use the following command:
                     .. code-block:: shell
                        ./jax-maxtext_benchmark_report.sh -m {{ model.model_repo }} -q nanoo_fp8
                  {% endif %}
            {% endif %}
            {% if model.multinode_config and "multi-node" in model.doc_options %}
            .. rubric:: Multi-node training
            The following SLURM scripts will launch the Docker container and
            run the benchmark. Run them outside of any Docker container. The
            unified multi-node benchmark script accepts a configuration file
            that specifies the model and training parameters.
            .. code-block:: shell
               sbatch -N <NUM_NODES> jax_maxtext_multinode_benchmark.sh <config_file.yml> [docker_image]
            <NUM_NODES>
               The number of nodes to use for training (for example, 2, 4,
               8).
            <config_file.yml>
               Path to the YAML configuration file containing model and
               training parameters. Configuration files are available in the
               ``scripts/jax-maxtext/env_scripts/`` directory for different
               models and GPU architectures.
            [docker_image] (optional)
               The Docker image to use. If not specified, it defaults to
               ``rocm/jax-training:maxtext-v26.1``.
            For example, to run a multi-node training benchmark on {{ model.model }}:
            .. tab-set::
               {% if model.multinode_config.gfx950 %}
               .. tab-item:: MI355X and MI350X (gfx950)
                  .. code-block:: bash
                     sbatch -N 4 jax_maxtext_multinode_benchmark.sh {{ model.multinode_config.gfx950 }}
               {% endif %}
               {% if model.multinode_config.gfx942 %}
               .. tab-item:: MI325X and MI300X (gfx942)
                  .. code-block:: bash
                     sbatch -N 4 jax_maxtext_multinode_benchmark.sh {{ model.multinode_config.gfx942 }}
               {% endif %}
         {% else %}
            .. rubric:: Multi-node training
            For multi-node training examples, choose a model from :ref:`amd-maxtext-model-support-v26.1`
            with an available `multi-node training script <https://github.com/ROCm/MAD/tree/develop/scripts/jax-maxtext/env_scripts>`__.
         {% endif %}
      {% endfor %}
   {% endfor %}
 Known issues
 ============
 - You might see NaNs in the losses when setting ``packing=True``. As
  a workaround, turn off input sequence packing (``packing=False``).
  This will be fixed in a future release.
 - Docker ``rocm/jax-training:maxtext-v26.1`` does not include `Primus
  <https://github.com/AMD-AGI/Primus/tree/main>`__. It is planned to be
  supported in a future release.
 Further reading
 ===============
 - To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
 - To learn more about system settings and management practices to configure your system for
  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
 - For a list of other ready-made Docker images for AI with ROCm, see
  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 Previous versions
 =================
 See :doc:`previous-versions/jax-maxtext-history` to find documentation for previous releases
 of the ``ROCm/jax-training`` Docker image.
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-history.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/megatron-lm-history.rst
@@ -7,7 +7,7 @@ Megatron-LM training performance testing version history
 This table lists previous versions of the ROCm Megatron-LM training Docker image for
 inference performance testing. For detailed information about available models
 for benchmarking, see the version-specific documentation. You can find tagged
-previous releases of the ``ROCm/primus`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/megatron-lm/tags>`__.
+previous releases of the ``ROCm/megatron-lm`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/megatron-lm/tags>`__.
 .. list-table::
   :header-rows: 1
@@ -16,20 +16,13 @@ previous releases of the ``ROCm/primus`` Docker image on `Docker Hub <https://hu
     - Components
     - Resources
-   * - v26.2 (latest)
+   * - v26.1 (latest)
     -
       * ROCm 7.2.0
       * PyTorch 2.10.0+git94c6e04
     -
       * :doc:`Primus Megatron documentation <../primus-megatron>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v26.2/images/sha256-9148d1bfcd579bf92f44bd89090e0d8c958f149c134b4b34b9674ab559244585>`__
   * - v26.1
     -
       * ROCm 7.1.0
       * PyTorch 2.10.0.dev20251112+rocm7.1
     -
-       * :doc:`Primus Megatron documentation <primus-megatron-v26.1>`
+       * :doc:`Primus Megatron documentation <../primus-megatron>`
       * :doc:`Megatron-LM (legacy) documentation <../megatron-lm>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d>`__
   * - v25.11
@@ -38,7 +31,7 @@ previous releases of the ``ROCm/primus`` Docker image on `Docker Hub <https://hu
       * PyTorch 2.10.0.dev20251112+rocm7.1
     -
       * :doc:`Primus Megatron documentation <primus-megatron-v25.11>`
-       * :doc:`Megatron-LM (legacy) documentation <megatron-lm-v25.11>`
+       * :doc:`Megatron-LM (legacy) documentation <megatron-lm-v25.10>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v25.11/images/sha256-71aa65a9bfc8e9dd18bce5b68c81caff864f223e9afa75dc1b719671a1f4a3c3>`__
   * - v25.10
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v26.1.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-megatron-v26.1.rst
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v26.1.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/primus-pytorch-v26.1.rst
@@ -1,457 +0,0 @@
 :orphan:
 .. meta::
   :description: How to train a model using PyTorch for ROCm.
   :keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker
 ****************************************
 Training a model with Primus and PyTorch
 ****************************************
 .. caution::
   This documentation does not reflect the latest version of ROCm Primus PyTorch training
   performance benchmark documentation. See :doc:`../primus-pytorch` for the latest version.
 `Primus <https://github.com/AMD-AGI/Primus>`__ is a unified and flexible
 LLM training framework designed to streamline training. It streamlines LLM
 training on AMD Instinct GPUs using a modular, reproducible configuration paradigm.
 Primus now supports the PyTorch torchtitan backend.
 .. note::
   For a unified training solution on AMD GPUs with ROCm, the `rocm/pytorch-training
   <https://hub.docker.com/r/rocm/pytorch-training/>`__ Docker Hub registry will be
   deprecated soon in favor of `rocm/primus <https://hub.docker.com/r/rocm/primus>`__.
   The ``rocm/primus`` Docker containers will cover PyTorch training ecosystem frameworks,
   including torchtitan and :doc:`Megatron-LM <primus-megatron>`.
   Primus with the PyTorch torchtitan backend is designed to replace the
   :doc:`ROCm PyTorch training <pytorch-training>` workflow. See
   :doc:`pytorch-training` to see steps to run workloads without Primus.
 AMD provides a ready-to-use Docker image for MI355X, MI350X, MI325X, and
 MI300X GPUs containing essential components for Primus and PyTorch training
 with Primus Turbo optimizations.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v26.1-benchmark-models.yaml
   .. tab-set::
      .. tab-item:: {{ data.docker.pull_tag }}
         :sync: {{ data.docker.pull_tag }}
         .. list-table::
            :header-rows: 1
            * - Software component
              - Version
            {% for component_name, component_version in data.docker.components.items() %}
            * - {{ component_name }}
              - {{ component_version }}
            {% endfor %}
 .. _amd-primus-pytorch-model-support-v26.01:
 Supported models
 ================
 The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X GPUs.
 Some instructions, commands, and training recommendations in this documentation might
 vary by model -- select one to get started.
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v26.1-benchmark-models.yaml
   {% set model_groups = data.model_groups %}
   .. raw:: html
      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
         <div class="row gx-0">
            <div class="col-2 me-1 px-2 model-param-head">Model</div>
            <div class="row col-10 pe-0">
      {% for model_group in model_groups %}
               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
      {% endfor %}
            </div>
         </div>
         <div class="row gx-0 pt-1">
            <div class="col-2 me-1 px-2 model-param-head">Variant</div>
            <div class="row col-10 pe-0">
      {% for model_group in model_groups %}
         {% set models = model_group.models %}
         {% for model in models %}
            {% if models|length % 3 == 0 %}
               <div class="col-4 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
            {% else %}
               <div class="col-6 px-2 model-param" data-param-k="model" data-param-v="{{ model.mad_tag }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
            {% endif %}
         {% endfor %}
      {% endfor %}
            </div>
         </div>
      </div>
 .. seealso::
   For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
   see the documentation :doc:`pytorch-training` (without Primus)
 .. _amd-primus-pytorch-performance-measurements-v26.01:
 System validation
 =================
 Before running AI workloads, it's important to validate that your AMD hardware is configured
 correctly and performing optimally.
 If you have already validated your system settings, including aspects like NUMA auto-balancing, you
 can skip this step. Otherwise, complete the procedures in the :ref:`System validation and
 optimization <rocm-for-ai-system-optimization>` guide to properly configure your system settings
 before starting training.
 To test for optimal performance, consult the recommended :ref:`System health benchmarks
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
 This Docker image is optimized for specific model configurations outlined
 below. Performance can vary for other training workloads, as AMD
 doesn’t test configurations and run conditions outside those described.
 Pull the Docker image
 =====================
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v26.1-benchmark-models.yaml
   Use the following command to pull the Docker image from Docker Hub.
   .. code-block:: shell
      docker pull {{ data.docker.pull_tag }}
 Run training
 ============
 Once the setup is complete, choose between the following two workflows to start benchmarking training.
 For fine-tuning workloads and multi-node training examples, see :doc:`pytorch-training` (without Primus).
 For best performance on MI325X, MI350X, and MI355X GPUs, you might need to
 tweak some configurations (such as batch sizes).
 .. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/primus-pytorch-v26.1-benchmark-models.yaml
   {% set docker = data.docker %}
   {% set model_groups = data.model_groups %}
   .. tab-set::
      .. tab-item:: Primus benchmarking
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
         .. container:: model-doc {{ model.mad_tag }}
            The following run commands are tailored to {{ model.model }}.
            See :ref:`amd-primus-pytorch-model-support-v26.01` to switch to another available model.
            .. rubric:: Download the Docker image and required packages
            1. Pull the ``{{ docker.pull_tag }}`` Docker image from Docker Hub.
               .. code-block:: shell
                  docker pull {{ docker.pull_tag }}
            2. Run the Docker container.
               .. code-block:: shell
                  docker run -it \
                      --device /dev/dri \
                      --device /dev/kfd \
                      --network host \
                      --ipc host \
                      --group-add video \
                      --cap-add SYS_PTRACE \
                      --security-opt seccomp=unconfined \
                      --privileged \
                      -v $HOME:$HOME \
                      -v $HOME/.ssh:/root/.ssh \
                      --shm-size 64G \
                      --name training_env \
                      {{ docker.pull_tag }}
               Use these commands if you exit the ``training_env`` container and need to return to it.
               .. code-block:: shell
                  docker start training_env
                  docker exec -it training_env bash
               The Docker container hosts verified commit ``9c529cd4`` of the `Primus
               <https://github.com/AMD-AGI/Primus/tree/9c529cd4a934a68a880ede036c3e97b792e38167/>`__ repository.
            .. rubric:: Prepare training datasets and dependencies
            The following benchmarking examples require downloading models and datasets
            from Hugging Face. To ensure successful access to gated repos, set your
            ``HF_TOKEN``.
            .. code-block:: shell
               export HF_TOKEN=$your_personal_hugging_face_access_token
            .. rubric:: Pretraining
            To get started, navigate to the ``Primus`` directory in your container.
            .. code-block::
               cd /workspace/Primus
            Now, to start the pretraining benchmark, use the ``run_pretrain.sh`` script
            included with Primus with the appropriate options.
            .. rubric:: Benchmarking examples
            .. container:: model-doc primus_pyt_train_llama-3.1-8b
               Use the following command to run train Llama 3.1 8B with BF16 precision using Primus torchtitan.
               .. tab-set::
                  .. tab-item:: MI355X and MI350X
                     :sync: MI355X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
                          --training.local_batch_size 6
                  .. tab-item:: MI300X
                     :sync: MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml
               To train Llama 3.1 8B with FP8 precision, use the following command.
               .. tab-set::
                  .. tab-item:: MI355X and MI350X
                     :sync: MI355X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
                          --training.local_batch_size 7
                  .. tab-item:: MI300X
                     :sync: MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml
            .. container:: model-doc primus_pyt_train_llama-3.1-70b
               Use the following command to run train Llama 3.1 70B with BF16 precision using Primus torchtitan.
               .. tab-set::
                  .. tab-item:: MI355X and MI350X
                     :sync: MI355X and MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
                          --training.local_batch_size 6
                  .. tab-item:: MI300X
                     :sync: MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml
               To train Llama 3.1 70B with FP8 precision, use the following command.
               .. tab-set::
                  .. tab-item:: MI355X and MI350X
                     :sync: MI355X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
                          --training.local_batch_size 5
                  .. tab-item:: MI300X
                     :sync: MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml
            .. container:: model-doc primus_pyt_train_deepseek-v3-16b
               Use the following command to run train DeepSeek V3 16B with BF16 precision using Primus torchtitan.
               .. tab-set::
                  .. tab-item:: MI355X and MI350X
                     :sync: MI355X and MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_deepseek_v3_16b.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_deepseek_v3_16b.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
                          --training.local_batch_size 10
                  .. tab-item:: MI300X
                     :sync: MI300X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_deepseek_v3_16b.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml
      {% endfor %}
   {% endfor %}
      .. tab-item:: MAD-integrated benchmarking
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
         .. container:: model-doc {{ model.mad_tag }}
            The following run command is tailored to {{ model.model }}.
            See :ref:`amd-primus-pytorch-model-support-v26.01` to switch to another available model.
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
               using one node with the {{ model.precision }} data type on the host machine.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{ model.mad_tag }} \
                      --keep-model-dir \
                      --live-output \
                      --timeout 28800
               MAD launches a Docker container with the name
               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
               model are collected in ``~/MAD/perf.csv``.
      {% endfor %}
   {% endfor %}
 Further reading
 ===============
 - For an introduction to Primus, see `Primus: A Lightweight, Unified Training
  Framework for Large Models on AMD GPUs <https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html>`__.
 - To learn more about MAD and the ``madengine`` CLI, see the `MAD usage guide <https://github.com/ROCm/MAD?tab=readme-ov-file#usage-guide>`__.
 - To learn more about system settings and management practices to configure your system for
  AMD Instinct MI300X Series GPUs, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`_.
 - For a list of other ready-made Docker images for AI with ROCm, see
  `AMD Infinity Hub <https://www.amd.com/en/developer/resources/infinity-hub.html#f-amd_hub_category=AI%20%26%20ML%20Models>`_.
 Previous versions
 =================
 See :doc:`pytorch-training-history` to find documentation for previous releases
 of the ``ROCm/pytorch-training`` Docker image.
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-history.rst
@@ -7,7 +7,7 @@ PyTorch training performance testing version history
 This table lists previous versions of the ROCm PyTorch training Docker image for
 inference performance testing. For detailed information about available models
 for benchmarking, see the version-specific documentation. You can find tagged
-previous releases of the ``ROCm/primus`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/pytorch-training/tags>`_.
+previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/pytorch-training/tags>`_.
 .. list-table::
   :header-rows: 1
@@ -16,20 +16,13 @@ previous releases of the ``ROCm/primus`` Docker image on `Docker Hub <https://hu
     - Components
     - Resources
-   * - v26.2 (latest)
+   * - v26.1 (latest)
     -
       * ROCm 7.2.0
       * PyTorch 2.10.0+git94c6e04
     -
       * :doc:`Primus PyTorch training documentation <../primus-pytorch>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v26.2/images/sha256-9148d1bfcd579bf92f44bd89090e0d8c958f149c134b4b34b9674ab559244585>`__
   * - v26.1
     -
       * ROCm 7.1.0
       * PyTorch 2.10.0.dev20251112+rocm7.1
     -
-       * :doc:`Primus PyTorch training documentation <primus-pytorch-v26.1>`
+       * :doc:`Primus PyTorch training documentation <../primus-megatron>`
       * :doc:`PyTorch training (legacy) documentation <../megatron-lm>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/primus/v26.1/images/sha256-4fc8808bdb14117c6af7f38d79c809056e6fdbfd530c1fabbb61d097ddaf820d>`__
   * - v25.11
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
@@ -47,7 +47,7 @@ Megatron-LM.
              - {{ component_version }}
            {% endfor %}
-.. _amd-primus-megatron-lm-model-support-v26.2:
+.. _amd-primus-megatron-lm-model-support-v26.01:
 Supported models
 ================
@@ -65,21 +65,9 @@ might vary by model -- select one to get started.
         <div class="row gx-0">
            <div class="col-2 me-1 px-2 model-param-head">Model</div>
            <div class="row col-10 pe-0">
-               {% set tag = "llama" %}
+      {% for model_group in model_groups %}
-               {% set group = "Meta Llama" %}
+               <div class="col-3 px-2 model-param" data-param-k="model-group" data-param-v="{{ model_group.tag }}" tabindex="0">{{ model_group.group }}</div>
-               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ tag }}" tabindex="0">{{ group }}</div>
+      {% endfor %}
               {% set tag = "zebra-llama" %}
               {% set group = "AMD Zebra-Llama" %}
               <div class="col-6 px-2 model-param" data-param-k="model-group" data-param-v="{{ tag }}" tabindex="0">{{ group }}</div>
               {% set tag = "deepseek" %}
               {% set group = "DeepSeek" %}
               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ tag }}" tabindex="0">{{ group }}</div>
               {% set tag = "mistral" %}
               {% set group = "Mistral AI" %}
               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ tag }}" tabindex="0">{{ group }}</div>
               {% set tag = "qwen" %}
               {% set group = "Qwen" %}
               <div class="col-4 px-2 model-param" data-param-k="model-group" data-param-v="{{ tag }}" tabindex="0">{{ group }}</div>
            </div>
         </div>
@@ -120,7 +108,7 @@ To test for optimal performance, consult the recommended :ref:`System health ben
 <rocm-for-ai-system-health-bench>`. This suite of tests will help you verify and fine-tune your
 system's configuration.
-.. _mi300x-amd-primus-megatron-lm-training-v26.2:
+.. _mi300x-amd-primus-megatron-lm-training-v26.01:
 Environment setup
 =================
@@ -130,7 +118,7 @@ Environment setup
   Use the following instructions to set up the environment, configure the script to train models, and
   reproduce the benchmark results on AMD Instinct GPUs.
-.. _amd-primus-megatron-lm-requirements-v26.2:
+.. _amd-primus-megatron-lm-requirements-v26.01:
 Pull the Docker image
@@ -172,7 +160,7 @@ Pull the Docker image
 The Docker container hosts verified commit ``9c529cd4`` of the `Primus
 <https://github.com/AMD-AGI/Primus/tree/9c529cd4a934a68a880ede036c3e97b792e38167>`__ repository.
-.. _amd-primus-megatron-lm-environment-setup-v26.2:
+.. _amd-primus-megatron-lm-environment-setup-v26.01:
 Configuration
 =============
@@ -219,7 +207,7 @@ You can use either mock data or real data for training.
  Ensure that the files are accessible inside the Docker container.
-.. _amd-primus-megatron-lm-tokenizer-v26.2:
+.. _amd-primus-megatron-lm-tokenizer-v26.01:
 Tokenizer
 ---------
@@ -232,7 +220,7 @@ right permissions to access the tokenizer for each model.
   # Export your HF_TOKEN in the workspace
   export HF_TOKEN=<your_hftoken>
-.. _amd-primus-megatron-lm-run-training-v26.2:
+.. _amd-primus-megatron-lm-run-training-v26.01:
 Run training
 ============
@@ -249,12 +237,14 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
 .. code-block:: shell
   pip install -r requirements.txt
   export HSA_NO_SCRATCH_RECLAIM=1
   export NVTE_CK_USES_BWD_V3=1
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-3.3-70b
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 3.3 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run pre-training for Llama 3.3 70B BF16, run:
@@ -289,7 +279,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 3.1 8B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run pre-training for Llama 3.1 8B FP8, run:
@@ -353,7 +343,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 3.1 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run pre-training for Llama 3.1 70B BF16, run:
@@ -367,9 +357,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
            bash runner/primus-cli direct \
              --log_file /tmp/primus_llama3.1_70B.log \
              -- train pretrain \
-              --config examples/megatron/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml \
+              --config examples/megatron/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml
              --micro_batch_size 8 \
              --global_batch_size 128
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
@@ -429,7 +417,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 2 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run pre-training for Llama 2 7B FP8, run:
@@ -493,7 +481,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run pre-training for Llama 2 70B BF16, run:
@@ -528,7 +516,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to DeepSeek-V3.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run training on a single node for DeepSeek-V3 (MoE with expert parallel) BF16 with 3-layer proxy,
   use the following command:
@@ -548,9 +536,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
              --moe_layer_freq 1 \
              --train_iters 50 \
              --micro_batch_size 8 \
-              --global_batch_size 64 \
+              --global_batch_size 64
              --moe_use_fused_router_with_aux_score True \
              --moe_permute_fusion True
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
@@ -576,7 +562,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to DeepSeek-V2-Lite.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run training on a single node for DeepSeek-V2-Lite (MoE with expert parallel) BF16,
   use the following command:
@@ -591,11 +577,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
            bash runner/primus-cli direct \
              --log_file /tmp/primus_deepseek_v2_lite.log \
              -- train pretrain \
-              --config examples/megatron/configs//MI355X/deepseek_v2_lite-BF16-pretrain.yaml \
+              --config examples/megatron/configs//MI355X/deepseek_v2_lite-BF16-pretrain.yaml
              --use_turbo_grouped_mlp False \
              --moe_use_legacy_grouped_gemm True \
              --moe_use_fused_router_with_aux_score True \
              --moe_permute_fusion True
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
@@ -616,7 +598,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Mixtral 8x7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
   use the following command:
@@ -652,7 +634,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Mixtral 8x22B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run training on a single node for Mixtral 8x22B BF16 (MoE with expert parallel) 4-layer proxy,
   use the following command:
@@ -689,83 +671,11 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
              --global_batch_size 16 \
              --train_iters 50
 .. container:: model-doc primus_pyt_megatron_lm_train_qwen3-32b-lora
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to post-training Qwen 3 32B (LoRA).
   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
   To run training on a single node for Qwen 3 32B BF16 (SFT), use the following
   command:
   .. tab-set::
      .. tab-item:: MI355X and MI350X
         :sync: MI355X and MI350X
         .. code-block:: shell
            bash runner/primus-cli direct \
              --log_file /tmp/primus_qwen3_32b.log \
              -- train posttrain \
              --config examples/megatron_bridge/configs/MI355X/qwen3_32b_lora_posttrain.yaml
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
         .. code-block:: shell
            # Set the variables for better performance
            # only on MI325X and MI300X
            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
            export NVTE_CK_IS_V3_ATOMIC_FP32=1
            bash runner/primus-cli direct \
              --log_file /tmp/primus_qwen3_32b.log \
              -- train posttrain \
              --config examples/megatron_bridge/configs/MI300X/qwen3_32b_lora_posttrain.yaml
 .. container:: model-doc primus_pyt_megatron_lm_train_qwen3-32b-sft
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to post-training Qwen 3 32B (SFT).
   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
   To run training on a single node for Qwen 3 32B BF16 (SFT), use the following
   command:
   .. tab-set::
      .. tab-item:: MI355X and MI350X
         :sync: MI355X and MI350X
         .. code-block:: shell
            bash runner/primus-cli direct \
              --log_file /tmp/primus_qwen3_32b_sft.log \
              -- train posttrain \
              --config examples/megatron_bridge/configs/MI355X/qwen3_32b_sft_posttrain.yaml
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
         .. code-block:: shell
            # Set the variables for better performance
            # only on MI325X and MI300X
            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
            export NVTE_CK_IS_V3_ATOMIC_FP32=1
            bash runner/primus-cli direct \
              --log_file /tmp/primus_qwen3_32b_sft.log \
              -- train posttrain \
              --config examples/megatron_bridge/configs/MI300X/qwen3_32b_sft_posttrain.yaml
 .. container:: model-doc primus_pyt_megatron_lm_train_qwen2.5-7b
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Qwen 2.5 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run training on a single node for Qwen 2.5 7B BF16, use the following
   command:
@@ -830,7 +740,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Qwen 2.5 72B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To run the training on a single node for Qwen 2.5 72B BF16, use the following command.
@@ -861,112 +771,7 @@ To run training on a single node, navigate to ``/workspace/Primus`` and use the
              -- train pretrain \
              --config examples/megatron/configs/MI300X/qwen2.5_72B-BF16-pretrain.yaml
-.. container:: model-doc primus_pyt_megatron_lm_train_zebra-llama-1b
+.. _amd-primus-megatron-multi-node-examples-v26.01:
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Zebra-Llama 1B.
   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
   To run the training on a single node for AMD Zebra-Llama 1B BF16, use the following command.
   .. tab-set::
      .. tab-item:: MI355X and MI350X
         :sync: MI355X and MI350X
         .. code-block:: shell
            PRIMUS_TRAIN_RUNTIME=legacy bash runner/primus-cli direct \
              --log_file /tmp/primus_zebra_llama_1B.log \
              -- train pretrain \
              --config examples/megatron/configs/MI355X/zebra_llama_1B-pretrain.yaml
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
         .. code-block:: shell
            # Set the variables for better performance
            # only on MI325X and MI300X
            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
            export NVTE_CK_IS_V3_ATOMIC_FP32=1
            PRIMUS_TRAIN_RUNTIME=legacy bash runner/primus-cli direct \
              --log_file /tmp/primus_zebra_llama_1B.log \
              -- train pretrain \
              --config examples/megatron/configs/MI300X/zebra_llama_1B-pretrain.yaml
 .. container:: model-doc primus_pyt_megatron_lm_train_zebra-llama-3b
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Zebra-Llama 3B.
   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
   To run the training on a single node for AMD Zebra-Llama 3B BF16, use the following command.
   .. tab-set::
      .. tab-item:: MI355X and MI350X
         :sync: MI355X and MI350X
         .. code-block:: shell
            PRIMUS_TRAIN_RUNTIME=legacy bash runner/primus-cli direct \
              --log_file /tmp/primus_zebra_llama_3B.log \
              -- train pretrain \
              --config examples/megatron/configs/MI355X/zebra_llama_3B-pretrain.yaml
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
         .. code-block:: shell
            # Set the variables for better performance
            # only on MI325X and MI300X
            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
            export NVTE_CK_IS_V3_ATOMIC_FP32=1
            PRIMUS_TRAIN_RUNTIME=legacy bash runner/primus-cli direct \
              --log_file /tmp/primus_zebra_llama_3B.log \
              -- train pretrain \
              --config examples/megatron/configs/MI300X/zebra_llama_3B-pretrain.yaml
 .. container:: model-doc primus_pyt_megatron_lm_train_zebra-llama-8b
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Zebra Llama 8B.
   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
   To run the training on a single node for AMD Zebra-Llama 8B BF16, use the following command.
   .. tab-set::
      .. tab-item:: MI355X and MI350X
         :sync: MI355X and MI350X
         .. code-block:: shell
            PRIMUS_TRAIN_RUNTIME=legacy bash runner/primus-cli direct \
              --log_file /tmp/primus_zebra_llama_8B.log \
              -- train pretrain \
              --config examples/megatron/configs/MI355X/zebra_llama_8B-pretrain.yaml
      .. tab-item:: MI300X
         :sync: MI325X and MI300X
         .. code-block:: shell
            # Set the variables for better performance
            # only on MI325X and MI300X
            export PRIMUS_TURBO_ATTN_V3_ATOMIC_FP32=1
            export NVTE_CK_IS_V3_ATOMIC_FP32=1
            PRIMUS_TRAIN_RUNTIME=legacy bash runner/primus-cli direct \
              --log_file /tmp/primus_zebra_llama_8B.log \
              -- train pretrain \
              --config examples/megatron/configs/MI300X/zebra_llama_8B-pretrain.yaml
 .. _amd-primus-megatron-multi-node-examples-v26.2:
 Multi-node training examples
 ----------------------------
@@ -984,11 +789,14 @@ to launch the multi-node workload. Use the following steps to setup your environ
   .. code-block:: shell
      git clone --recurse-submodules https://github.com/AMD-AGI/Primus.git
-      cd Primus/
+      cd Primus
-      git checkout 44f780d
+      git checkout c4c083de64ba3e8f19ccc9629411267108931f9e
      git submodule update --init --recursive
      export DOCKER_IMAGE={{ docker.pull_tag }}
      export HF_TOKEN=<your_HF_token>
      export HSA_NO_SCRATCH_RECLAIM=1
      export NVTE_CK_USES_BWD_V3=1
      export NCCL_IB_HCA=<your_NCCL_IB_HCA> # specify which RDMA interfaces to use for communication
      export NCCL_SOCKET_IFNAME=<your_NCCL_SOCKET_IFNAME> # your Network Interface
      export GLOO_SOCKET_IFNAME=<your_GLOO_SOCKET_IFNAME> # your Network Interface
@@ -1005,13 +813,13 @@ to launch the multi-node workload. Use the following steps to setup your environ
   * If ``NCCL_IB_HCA`` and ``NCCL_SOCKET_IFNAME`` are not set, Primus will try to auto-detect. However, since NICs can vary accross different cluster, it is encouraged to explicitly export your NCCL parameters for the cluster.
   * To find your network interface, you can use ``ip a``.
   * To find RDMA interfaces, you can use ``ibv_devices`` to get the list of all the RDMA/IB  devices.
-   * Remember to set ``DOCKER_IMAGE`` and ``HF_TOKEN`` (see :ref:`amd-primus-megatron-lm-tokenizer-v26.2`) as appropriate.
+   * Remember to set ``DOCKER_IMAGE`` and ``HF_TOKEN`` (see :ref:`amd-primus-megatron-lm-tokenizer-v26.01`) as appropriate.
 .. container:: model-doc primus_pyt_megatron_lm_train_llama-3.1-8b
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 3.1 8B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Llama 3.1 8B FP8 on 8 nodes, run:
@@ -1028,7 +836,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 2 7B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Llama 2 7B FP8 on 8 nodes, run:
@@ -1045,7 +853,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 3.1 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Llama 3.1 70B FP8 on 8 nodes, run:
@@ -1075,7 +883,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Llama 2 70B FP8 on 8 nodes, run:
@@ -1105,7 +913,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 3.3 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Llama 3.3 70B FP8 on 8 nodes, run:
@@ -1135,7 +943,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Mixtral 8x7B BF16 on 8 nodes, run:
@@ -1153,7 +961,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
   Once setup is complete, run the appropriate training command.
   The following run commands are tailored to Llama 2 70B.
-   See :ref:`amd-primus-megatron-lm-model-support-v26.2` to switch to another available model.
+   See :ref:`amd-primus-megatron-lm-model-support-v26.01` to switch to another available model.
   To train Qwen2.5 72B FP8 on 8 nodes, run:
@@ -1168,7 +976,7 @@ to launch the multi-node workload. Use the following steps to setup your environ
          --global_batch_size 512 \
          --recompute_num_layers 80 \
-.. _amd-primus-megatron-lm-benchmark-test-vars-v26.2:
+.. _amd-primus-megatron-lm-benchmark-test-vars-v26.01:
 Key options
 -----------
@@ -1210,6 +1018,14 @@ recompute_granularity
 num_layers
  For using a reduced number of layers as with proxy models.
 Known issues
 ============
 DeepSeekV3 proxy model and Mixtral 8x22B proxy model may exit with an error
 due to a memory free issue. However, this does not impacts training runs. All
 iterations, in this case 50, should have been completed before the exit and
 the results should be available in the end.
 Further reading
 ===============
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
@@ -45,7 +45,7 @@ with Primus Turbo optimizations.
              - {{ component_version }}
            {% endfor %}
-.. _amd-primus-pytorch-model-support-v26.2:
+.. _amd-primus-pytorch-model-support-v26.01:
 Supported models
 ================
@@ -91,7 +91,7 @@ vary by model -- select one to get started.
   For additional workloads, including Llama 3.3, Llama 3.2, Llama 2, GPT OSS, Qwen, and Flux models,
   see the documentation :doc:`pytorch-training` (without Primus)
-.. _amd-primus-pytorch-performance-measurements-v26.2:
+.. _amd-primus-pytorch-performance-measurements-v26.01:
 System validation
 =================
@@ -138,6 +138,44 @@ tweak some configurations (such as batch sizes).
   .. tab-set::
      .. tab-item:: MAD-integrated benchmarking
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
         .. container:: model-doc {{ model.mad_tag }}
            The following run command is tailored to {{ model.model }}.
            See :ref:`amd-primus-pytorch-model-support-v26.01` to switch to another available model.
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
               using one node with the {{ model.precision }} data type on the host machine.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{ model.mad_tag }} \
                      --keep-model-dir \
                      --live-output \
                      --timeout 28800
               MAD launches a Docker container with the name
               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
               model are collected in ``~/MAD/perf.csv``.
      {% endfor %}
   {% endfor %}
      .. tab-item:: Primus benchmarking
   {% for model_group in model_groups %}
@@ -146,7 +184,7 @@ tweak some configurations (such as batch sizes).
         .. container:: model-doc {{ model.mad_tag }}
            The following run commands are tailored to {{ model.model }}.
-            See :ref:`amd-primus-pytorch-model-support-v26.2` to switch to another available model.
+            See :ref:`amd-primus-pytorch-model-support-v26.01` to switch to another available model.
            .. rubric:: Download the Docker image and required packages
@@ -224,6 +262,17 @@ tweak some configurations (such as batch sizes).
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_8B-BF16-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-BF16-pretrain.yaml \
                          --training.local_batch_size 6
                  .. tab-item:: MI300X
                     :sync: MI300X
@@ -248,6 +297,17 @@ tweak some configurations (such as batch sizes).
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_8B-FP8-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_8B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_8B-FP8-pretrain.yaml \
                          --training.local_batch_size 7
                  .. tab-item:: MI300X
                     :sync: MI300X
@@ -274,6 +334,17 @@ tweak some configurations (such as batch sizes).
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_70B-BF16-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-BF16-pretrain.yaml \
                          --training.local_batch_size 6
                  .. tab-item:: MI300X
                     :sync: MI300X
@@ -298,6 +369,17 @@ tweak some configurations (such as batch sizes).
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/llama3.1_70B-FP8-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_llama3.1_70B_fp8.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/llama3.1_70B-FP8-pretrain.yaml \
                          --training.local_batch_size 5
                  .. tab-item:: MI300X
                     :sync: MI300X
@@ -324,6 +406,17 @@ tweak some configurations (such as batch sizes).
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI355X/deepseek_v3_16b-pretrain.yaml
                  .. tab-item:: MI325X
                     :sync: MI325X
                     .. code-block:: shell
                        bash runner/primus-cli direct \
                          --log_file /tmp/primus_deepseek_v3_16b.log \
                          -- train pretrain \
                          --config examples/torchtitan/configs/MI300X/deepseek_v3_16b-pretrain.yaml \
                          --training.local_batch_size 10
                  .. tab-item:: MI300X
                     :sync: MI300X
@@ -336,44 +429,6 @@ tweak some configurations (such as batch sizes).
      {% endfor %}
   {% endfor %}
      .. tab-item:: MAD-integrated benchmarking
   {% for model_group in model_groups %}
      {% for model in model_group.models %}
         .. container:: model-doc {{ model.mad_tag }}
            The following run command is tailored to {{ model.model }}.
            See :ref:`amd-primus-pytorch-model-support-v26.2` to switch to another available model.
            1. Clone the ROCm Model Automation and Dashboarding (`<https://github.com/ROCm/MAD>`__) repository to a local
               directory and install the required packages on the host machine.
               .. code-block:: shell
                  git clone https://github.com/ROCm/MAD
                  cd MAD
                  pip install -r requirements.txt
            2. For example, use this command to run the performance benchmark test on the {{ model.model }} model
               using one node with the {{ model.precision }} data type on the host machine.
               .. code-block:: shell
                  export MAD_SECRETS_HFTOKEN="your personal Hugging Face token to access gated models"
                  madengine run \
                      --tags {{ model.mad_tag }} \
                      --keep-model-dir \
                      --live-output \
                      --timeout 28800
               MAD launches a Docker container with the name
               ``container_ci-{{ model.mad_tag }}``. The latency and throughput reports of the
               model are collected in ``~/MAD/perf.csv``.
      {% endfor %}
   {% endfor %}
 Further reading
 ===============
--- a/docs/how-to/system-optimization/index.rst
+++ b/docs/how-to/system-optimization/index.rst
@@ -14,7 +14,6 @@ optimize performance for specific types of workloads or use-cases. The contents
  .. grid-item-card:: AMD RDNA
    * :doc:`AMD RDNA3.5 system optimization <strixhalo>`
    * :doc:`AMD RDNA2 system optimization <w6000-v620>`
  .. grid-item-card:: AMD Instinct
--- a/docs/how-to/system-optimization/strixhalo.rst
+++ b/docs/how-to/system-optimization/strixhalo.rst
@@ -1,289 +0,0 @@
 .. meta::
   :description: Learn about system settings and performance tuning for AMD Strix Halo (Ryzen AI MAX/MAX+) APUs.
   :keywords: Strix Halo, Ryzen AI MAX, workstation, BIOS, installation, APU, optimization, ROCm
 .. _strix-halo-optimization:
 ==========================================
 AMD Strix Halo system optimization
 ==========================================
 This document provides guidance for optimizing systems powered by AMD Ryzen AI
 MAX and MAX+ processors (codenamed Strix Halo). These APUs combine
 high-performance CPU cores with integrated RDNA 3.5 graphics and support up to
 128GB of unified LPDDR5X-8000 memory, making them particularly well-suited for:
 * LLM development and inference systems
 * High-performance workstations
 * Virtualization hosts running multiple VMs
 * GPU compute and parallel processing
 * Gaming systems
 * Home servers and AI development platforms
 The main purpose of this document is to help users utilize Strix Halo APUs to
 their full potential through proper system configuration.
 .. _memory-settings:
 Memory settings
 ===============
 On Strix Halo GPUs (gfx1151) memory access is handled through GPU Virtual Memory
 (GPUVM), which provides per-process GPU virtual address spaces (VMIDs) rather
 than a separate, discrete VRAM pool.
 As a result, memory on Strix Halo is **mapped**, not physically partitioned.
 The terms Graphics Address Remapping Table (GART) and GTT (Graphics Translation
 Table) describe limits on how much system memory can be mapped into GPU address
 spaces and who can use it, rather than distinct types of physical memory.
 * **GART**
  Defines the amount of platform address space (system RAM or Memory-Mapped I/O)
  that can be mapped into the GPU virtual address space used by the kernel driver.
  On systems with physically shared CPU and GPU memory, such as Strix Halo, this
  mapped system memory effectively serves as VRAM for the GPU. GART is typically
  kept relatively small to limit GPU page-table size and is mainly used for
  driver-internal operations.
 * **GTT**
  Defines the amount of system RAM that can be mapped into GPU virtual address
  spaces for user processes. This is the memory pool used by applications such
  as PyTorch and other AI/compute workloads. GTT allocations are dynamic and are
  not permanently reserved, allowing the operating system to reclaim memory when
  it is not actively used by the GPU. By default, the GTT limit is set to
  approximately 50% of total system RAM.
 .. note::
  On systems with physically shared CPU and GPU memory such as Strix Halo,
  several terms are often used interchangeably in firmware menus, documentations
  and community discussions:
  * VRAM
  * Carve-out
  * GART
  * Dedicated GPU memory
  * Firmware-reserved GPU memory
  In this document, we will use VRAM from this point onward.
 If desired, you can adjust how much memory is preferentially available to the
 GPU by:
 * Increasing the VRAM in BIOS, or
 * Reducing the configured GTT size so it is smaller than the reserved amount.
 If the GTT size bigger than VRAM at that case the amdgpu driver for VRAM allocation
 using GTT (GTT-backed allocations) as you can see in
 `torvalds/linux@759e764 <https://github.com/torvalds/linux/commit/759e764f7d587283b4e0b01ff930faca64370e59>`_
 commit.
 Because memory is physically shared, there is no performance distinction
 similar to discrete GPUs where dedicated VRAM is significantly faster than
 system memory. Firmware may optionally reserve some memory exclusively for GPU
 use, but this provides little benefit for most workloads while permanently
 reducing available system memory.
 For this reason, AI frameworks typically prefer GTT-backed allocations. GTT
 allows large, flexible mappings without permanently reserving memory, resulting
 in better overall system utilization on unified memory systems.
 Configuring shared memory limits on linux
 -----------------------------------------
 The maximum amount of shared GPU-accessible memory can be increased by changing
 the kernel **Translation Table Manager (TTM)** page limit. This setting controls
 how many system memory pages may be mapped for GPU use and is exposed at:
 ::
   /sys/module/ttm/parameters/pages_limit
 The value is expressed in **pages**, not bytes or gigabytes (GB).
 .. note::
   AMD recommends keeping the dedicated VRAM reservation in BIOS small
   (for example 0.5 GB) and increasing the shared (TTM/GTT) limit instead.
 A helper utility is available to simplify configuration.
 1. Install ``pipx``:
   ::
      sudo apt install pipx
      pipx ensurepath
 2. Install the AMD debug tools:
   ::
      pipx install amd-debug-tools
 3. Query the current shared memory configuration:
   ::
      amd-ttm
 4. Set the usable shared memory (in GB):
   ::
      amd-ttm --set <NUM>
 5. Reboot for changes to take effect.
 .. note::
  The amd-ttm convert the pages to GB to help the users.
 **Example with output**
 Check the current settings:
 ::
   amd-ttm
   💻 Current TTM pages limit: 16469033 pages (62.82 GB)
   💻 Total system memory: 125.65 GB
 Change the usable shared memory:
 ::
   ❯ amd-ttm --set 100
   🐧 Successfully set TTM pages limit to 26214400 pages (100.00 GB)
   🐧 Configuration written to /etc/modprobe.d/ttm.conf
   ○ NOTE: You need to reboot for changes to take effect.
   Would you like to reboot the system now? (y/n): y
 Revert to kernel defaults:
 ::
   ❯ amd-ttm --clear
  🐧 Configuration /etc/modprobe.d/ttm.conf removed
   Would you like to reboot the system now? (y/n): y
 .. _operating-system-support:
 Operating system support
 ========================
 The ROCm compatibility tables can be found at the following links:
 - `System requirements (Linux) <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html>`_
 - `System requirements (Windows) <https://rocm.docs.amd.com/projects/install-on-windows/en/latest/reference/system-requirements.html>`_
 However, for Strix Halo there are additional kernel version requirements,
 which are described in the following section.
 Required kernel version
 -----------------------
 Support for Strix Halo requires specific fixes in the Linux kernel that
 update internal limits in the AMD KFD driver to ensure correct queue
 creation and memory availability checks. Without these updates, GPU
 compute workloads may fail to initialize or behave unpredictably. The
 necessary Linux kernel patches have been merged upstream and are
 included in Linux kernel 6.18.4 and newer releases.
 The following commits are required for Strix Halo support:
 - `gregkh/linux@7f26af7 <https://github.com/gregkh/linux/commit/7f26af7bf9b76c2c2a1a761aab5803e52be21eea>`_
 - `gregkh/linux@7445db6 <https://github.com/gregkh/linux/commit/7445db6a7d5a0242d8214582b480600b266cba9e>`_
 The table below reflects compatibility for **AMD-released pre-built ROCm
 binaries only**. Distributions that ship **native ROCm packaging** may
 provide different support levels.
 .. list-table::
   :header-rows: 0
   :widths: 10 90
   * - ❌
     - Unsupported combination
   * - ⚠️
     - Unstable / experimental combination
   * - ✅
     - Stable and supported combination
 .. list-table::
   :header-rows: 1
   :widths: 12 14 14 16 14 16 16
   * - ROCm Release
     - Ubuntu 24.04 HWE
     - Ubuntu 24.04 OEM (<= 6.14.0-1017)
     - Ubuntu 24.04 OEM (>= 6.14.0-1018)
     - Ubuntu 26.04 Generic
     - Generic Distro < 6.18.4
     - Generic Distro >= 6.18.4
   * - 7.11.0
     - ⚠️
     - ⚠️
     - ✅
     - ✅
     - ⚠️
     - ✅
   * - 7.10.0
     - ⚠️
     - ⚠️
     - ❌
     - ❌
     - ⚠️
     - ❌
   * - 7.9.0
     - ⚠️
     - ⚠️
     - ❌
     - ❌
     - ⚠️
     - ❌
   * - 7.2.1
     - ⚠️
     - ⚠️
     - ✅
     - ✅
     - ⚠️
     - ✅
   * - 7.2.0
     - ❌
     - ✅
     - ✅
     - ✅
     - ❌
     - ✅
   * - 7.1.x
     - ⚠️
     - ⚠️
     - ❌
     - ❌
     - ⚠️
     - ❌
   * - 6.4.x
     - ⚠️
     - ⚠️
     - ❌
     - ❌
     - ⚠️
     - ❌
 The following distributions include the required fixes in their
 native packaging, independent of AMD pre-built binaries:
 - Fedora 43
 - Ubuntu 26.04
 - Arch Linux
--- a/docs/index.md
+++ b/docs/index.md
@@ -63,13 +63,12 @@ ROCm documentation is organized into the following categories:
 :class-body: rocm-card-banner rocm-hue-6
 <!-- markdownlint-disable MD051 -->
 * [ROCm libraries](./reference/api-libraries.md)
-* [ROCm tools, compilers, and runtime API](./reference/rocm-tools.md)
+* [ROCm tools, compilers, and runtimes](./reference/rocm-tools.md)
 * [GPU hardware specifications](./reference/gpu-arch-specs.rst)
 * [Hardware atomics operation support](./reference/gpu-atomics-operation.rst)
 * [Environment variables](./reference/env-variables.rst)
 * [Data types and precision support](./reference/precision-support.rst)
 * [Graph safe support](./reference/graph-safe-support.rst)
 * [ROCm glossary](./reference/glossary.rst)
 <!-- markdownlint-enable MD051 -->
 :::
--- a/docs/reference/env-variables.rst
+++ b/docs/reference/env-variables.rst
@@ -74,8 +74,7 @@ Other useful variables
 ROCR-Runtime environment variables
 ==================================
-The following table lists the :doc:`ROCR-Runtime <rocr-runtime:index>`
+The following table lists the ROCR-Runtime environment variables:
 environment variables:
 .. remote-content::
   :repo: ROCm/rocm-systems
@@ -120,11 +119,8 @@ documentation.
      - Performance tuning, kernel selection, logging, and debugging for BLAS
        operations.
-    * - :doc:`rocSHMEM <rocshmem:api/env_variables>`
+    * - :doc:`rocSolver <rocsolver:reference/env_variables>`
-      - Control the behavior of rocSHMEM.
+      - Control logging of rocSolver.
    * - :doc:`rocSOLVER <rocsolver:reference/env_variables>`
      - Control logging of rocSOLVER.
    * - :doc:`rocSPARSE <rocsparse:reference/env_variables>`
      - Control logging of rocSPARSE.
--- a/docs/reference/glossary.rst
+++ b/docs/reference/glossary.rst
@@ -1,24 +0,0 @@
 .. meta::
  :description: AMD ROCm Glossary
  :keywords: AMD, ROCm,  glossary, terminology, device hardware,
    device software, host software, performance
 .. _glossary:
 ********************************************************************************
 ROCm glossary
 ********************************************************************************
 This glossary provides concise definitions of key terms and concepts in AMD ROCm
 programming. Each entry includes a brief description and a link to detailed
 documentation for in-depth information.
 The glossary is organized into four sections:
 * :doc:`glossary/device-hardware` — Hardware components (for example, Compute
  Units, cores, memory)
 * :doc:`glossary/device-software` — Software abstractions (programming model,
  ISA, thread hierarchy)
 * :doc:`glossary/host-software` — Development tools (HIP, compilers, libraries,
  profilers)
 * :doc:`glossary/performance` — Performance metrics and optimization concepts
--- a/docs/reference/glossary/device-hardware.rst
+++ b/docs/reference/glossary/device-hardware.rst
@@ -1,254 +0,0 @@
 .. meta::
  :description: Device hardware glossary for AMD GPUs
  :keywords: AMD, ROCm, GPU, device hardware, compute units, cores, MFMA,
    architecture, register file, cache, HBM
 .. _glossary-device-hardware:
 ************************
 Device hardware glossary
 ************************
 This section provides concise definitions of hardware components and architectural
 features of AMD GPUs.
 .. glossary::
    :sorted:
    AMD device architecture
        AMD device architecture is based on unified, programmable compute
        engines known as :term:`compute units (CUs) <Compute units>`. See
        :ref:`hip:hardware_implementation` for details.
    Compute units
        Compute units (CUs) are the fundamental programmable execution engines
        in AMD GPUs capable of running complex programs. See
        :ref:`hip:compute_unit` for details.
    ALU
        Arithmetic logic units (ALUs) are the primary arithmetic engines that
        execute mathematical and logical operations within
        :term:`compute units <Compute units>`. See :ref:`hip:valu` for details. 
    SALU
        Scalar :term:`ALUs <ALU>` (SALUs) operate on a single value per
        :term:`wavefront <Wavefront>` and manage all control flow.
    VALU
        Vector :term:`ALUs <ALU>` (VALUs) perform an arithmetic or logical
        operation on data for each :term:`work-item <Work-item (Thread)>` in a
        :term:`wavefront <Wavefront>`, enabling data-parallel execution.
    Special function unit
        Special function units (SFUs) accelerate transcendental and reciprocal
        mathematical functions such as ``exp``, ``log``, ``sin``, and ``cos``.
        See :ref:`hip:sfu` for details.
    Load/store unit
        Load/store units (LSUs) handle data transfer between
        :term:`compute units <Compute units>` and the GPU's memory subsystems,
        managing thousands of concurrent memory operations. See :ref:`hip:lsu`
        for details.
    Work-group (Block)
        A work-group (also called a block) is a collection of
        :term:`wavefronts <Wavefront (Warp)>` scheduled together on a single
        :term:`compute unit <Compute units>` that can coordinate through
        :term:`Local data share <Local data share>` memory. See
        :ref:`hip:inherent_thread_hierarchy_block` for work-group details.
    Work-item (Thread)
        A work-item (also called a thread) is the smallest unit of execution on
        an AMD GPU and represents a single element of work. See
        :ref:`hip:work-item` for thread hierarchy details.
    Wavefront (Warp)
        A wavefront (also called a warp) is a group of
        :term:`work-items <Work-item (Thread)>` that execute in parallel on a
        single :term:`compute unit <Compute units>`, sharing one
        instruction stream. See :ref:`hip:wavefront` for execution details.
    Wavefront scheduler
        The wavefront scheduler in each :term:`compute unit <Compute units>`
        decides which :term:`wavefront <wavefront>` to execute each clock cycle,
        enabling rapid context switching for latency hiding. See
        :ref:`hip:wave-scheduling` for details.
    Wavefront size
        The wavefront size is the number of
        :term:`work-items <Work-item (Thread)>` that execute together in a
        single :term:`wavefront <Wavefront (Warp)>`. For AMD Instinct GPUs, the
        wavefront size is 64 threads, while AMD Radeon GPUs have a wavefront
        size of 32 threads. See :ref:`hip:wavefront` for details.
    SIMD core
        SIMD cores are execution lanes that perform scalar and vector arithmetic
        operations inside each :term:`compute unit <Compute unit>`. See
        :ref:`hip:cdna_architecture` and :ref:`hip:rdna_architecture` for
        details.
    Matrix cores (MFMA units)
        Matrix cores (MFMA units) are specialized execution units that perform
        large-scale matrix operations in a single instruction, delivering high
        throughput for AI and HPC workloads. See :ref:`hip:mfma_units` for
        details.
    Data movement engine
        Data movement engines (DMEs) are specialized hardware units in AMD
        Instinct MI300 and MI350 series GPUs that accelerate multi-dimensional
        tensor data copies between global memory and on-chip memory. See
        :ref:`hip:dme` for details.
    GFX IP
        GFX IP (Graphics IP) versions are identifiers that specify which
        instruction formats, memory models, and compute features are supported
        by each AMD GPU generation. See :ref:`hip:gfx_ip` for versioning
        information.
    GFX IP major version
        The :term:`GFX IP <GFX IP>` major version represents the GPU's core
        instruction set and architecture. For example, a GFX IP `11` major
        version corresponds to the RDNA3 architecture, influencing driver
        support and available compute features. See :ref:`hip:gfx_ip` for
        versioning information.
    GFX IP minor version
        The :term:`GFX IP <GFX IP>` minor version represents specific variations
        within a :term:`GFX IP <GFX IP>` major version and affects feature sets,
        optimizations, and driver behavior. Different GPU models within the same
        major version can have unique capabilities, impacting performance and
        supported instructions. See :ref:`hip:gfx_ip` for versioning
        information.
    Compute unit versioning
        :term:`Compute units <Compute units>` are versioned with
        :term:`GFX IP <GFX IP>` identifiers that define their microarchitectural
        features and instruction set compatibility. See :ref:`hip:gfx_ip` for
        details.
    Register file
        The register file is the primary on-chip memory store in each
        :term:`compute unit <Compute units>`, holding data between arithmetic
        and memory operations. See :ref:`hip:memory_hierarchy` for details.
    SGPR file
        The :term:`SGPR <SGPR>` file is the
        :term:`register file <Register file>` that holds data used by the
        :term:`scalar ALU <SALU>`.
    VGPR file
        The :term:`VGPR <VGPR>` file is the
        :term:`register file <Register file>` that holds data used by the
        :term:`vector ALU <VALU>`. GPUs with
        :term:`matrix cores <Matrix cores (MFMA units)>` also have
        :term:`AccVGPR <AccVGPR>` files, used specifically for matrix
        instructions.
    L0 instruction cache
        On AMD Radeon GPUs, the level 0 (L0) instruction cache is local to each
        :term:`WGP <WGP>` and thus shared between the WGP's
        :term:`compute units <Compute units>`.
    L0 scalar cache
        On AMD Radeon GPUs, the level 0 (L0) scalar data cache is local to each
        :term:`WGP <WGP>` and thus shared between the WGP's
        :term:`compute units <Compute units>`. It provides the
        :term:`scalar ALU <SALU>` with fast access to recently used data.
    L0 vector cache 
        On AMD Radeon GPUs, the level 0 (L0) vector data cache is local to each
        :term:`WGP <WGP>` and thus shared between the WGP's
        :term:`compute units <Compute units>`. It provides the
        :term:`vector ALU <VALU>` with fast access to recently used data.
    L1 instruction cache
        On AMD Instinct GPUs, the level 1 (L1) instruction cache is local to
        each :term:`compute unit <Compute units>`. On AMD Radeon GPUs, the
        L1 instruction cache does not exist as a separate cache level, and
        instructions are stored in the
        :term:`L0 instruction cache <L0 instruction cache>`.
    L1 scalar cache
        On AMD Instinct GPUs, the level 1 (L1) scalar data cache is local to
        each :term:`compute unit <Compute units>`, providing the 
        :term:`scalar ALU <SALU>` with fast access to recently used data. On AMD
        Radeon GPUs, the L1 scalar cache does not exist as a separate cache
        level, and recently used scalar data is stored in the
        :term:`L0 scalar cache <L0 scalar cache>`.
    L1 vector cache
        On AMD Instinct GPUs, the level 1 (L1) vector data cache is local to
        each :term:`compute unit <Compute units>`, providing the 
        :term:`vector ALU <VALU>` with fast access to recently used data. On AMD
        Radeon GPUs, the L1 vector cache does not exist as a separate cache
        level, and recently used vector data is stored in the
        :term:`L0 vector cache <L0 vector cache>`.
    Graphics L1 cache
        On AMD Radeon GPUs, the read-only graphics level 1 (L1) cache is local
        to groups of :term:`WGPs <WGP>` called shader arrays, providing fast
        access to recently used data. AMD Instinct GPUs do not feature the
        graphics L1 cache.
    L2 cache
        On AMD Instinct MI100 series GPUs, the L2 cache is shared across the
        entire chip, while for all other AMD GPUs the L2 caches are shared by
        the :term:`compute units <Compute units>` on the same :term:`GCD <GCD>`
        or :term:`XCD <XCD>`.
    Infinity Cache (L3 cache)
        On AMD Instinct MI300 and MI350 series GPUs and AMD Radeon GPUs, the
        Infinity Cache is the last level cache of the cache hierarchy. It is
        shared by all :term:`compute units <Compute units>` and
        :term:`WGPs <WGP>` on the GPU.
    GPU RAM (VRAM)
        GPU RAM, also known as :term:`global memory <Global memory>` in the HIP
        programming model, is the large, high-capacity off-chip memory subsystem
        accessible by all :term:`compute units <Compute units>`, forming the
        foundation of the device's :ref:`memory hierarchy <hip:hbm>`.
    Local data share
        Local data share (LDS) is fast on-chip memory local to each
        :term:`compute unit <Compute units>` and shared among
        :term:`work-items <Work-item (Thread)>` in a
        :term:`work-group <Work-group (Block)>`, enabling efficient coordination
        and data reuse. In the HIP programming model, the LDS is known as shared
        memory. See :ref:`hip:lds` for LDS programming details.
    Registers
        Registers are the lowest level of the memory hierarchy, storing
        per-thread temporary variables and intermediate results. See
        :ref:`hip:memory_hierarchy` for register usage details.
    SGPR
        Scalar general-purpose :term:`registers <Registers>` (SGPRs) hold data
        produced and consumed by a :term:`compute unit <Compute units>`'s
        :term:`scalar ALU <SALU>`.
    VGPR
        Vector general-purpose :term:`registers <Registers>` (VGPRs) hold data
        produced and consumed by a :term:`compute unit <Compute units>`'s
        :term:`vector ALU <VALU>`.
    AccVGPR
        Accumulation General Purpose Vector Registers (AccVGPRs) are a special
        type of :term:`VGPRs <VGPR>` used exclusively for matrix operations.
    XCD
        On AMD Instinct MI300 and MI350 series GPUs, the Accelerator Complex Die
        (XCD) contains the GPU's computational elements and lower levels of the
        cache hierarchy. See :doc:`../../conceptual/gpu-arch/mi300` for details.
    GCD
        On AMD Instinct MI100 and MI250 series GPUs and AMD Radeon GPUs, the
        Graphics Compute Die (GCD) contains the GPU's computational elements
        and lower levels of the cache hierarchy. See
        :doc:`../../conceptual/gpu-arch/mi250` for details.
    WGP
        A Workgroup Processor (WGP) is a hardware unit on AMD Radeon GPUs that
        contains two :term:`compute units <Compute units>` and their associated
        resources, enabling efficient scheduling and execution of
        :term:`wavefronts <wavefront>`. See :ref:`hip:rdna_architecture` for
        details.
--- a/docs/reference/glossary/device-software.rst
+++ b/docs/reference/glossary/device-software.rst
@@ -1,74 +0,0 @@
 .. meta::
  :description: Device software glossary for AMD GPUs
  :keywords: AMD, ROCm, GPU, device software, programming model, AMDGPU,
    assembly, IR, GFX IP, wavefront, work-group, HIP kernel, thread hierarchy
 .. _glossary-device-software:
 ************************
 Device software glossary
 ************************
 This section provides brief definitions of software abstractions and programming
 models that run on AMD GPUs.
 .. glossary::
    :sorted:
    ROCm programming model
        The ROCm programming model defines how AMD GPUs execute massively
        parallel programs using hierarchical
        :term:`work-groups <Work-group (Block)>`, memory scopes, and barrier
        synchronization. See :ref:`hip:programming_model` for complete details.
    AMDGPU assembly
        AMDGPU assembly (GFX ISA) is the low-level assembly format for programs
        running on AMD GPUs, generated by the
        :term:`ROCm compiler toolchain <HIP compiler>`. See
        :ref:`hip:amdgpu_assembly` for instruction set details.
    AMDGPU intermediate representation
        AMDGPU IR is an intermediate representation for GPU code, serving as a
        virtual instruction set between high-level languages and
        :term:`architecture-specific assembly <AMDGPU assembly>`. See
        :ref:`hip:amdgpu_ir` for compilation details.
    LLVM target name
        The LLVM target name is a string identifier corresponding to a specific
        :term:`GFX IP <GFX IP>` version that is passed to the
        :term:`HIP compiler <HIP compiler>` toolchain to specify the target GPU
        architecture for code generation.
        See :doc:`llvm-project:reference/rocmcc` for details.
    Grid
        A grid represents the collection of all
        :term:`work-groups <Work-group (Block)>` executing a single
        :term:`kernel <HIP kernel>` across the entire GPU. See
        :ref:`hip:inherent_thread_hierarchy_grid` for grid execution details.
    HIP kernel
        A HIP kernel is the unit of GPU code that executes in parallel across
        many :term:`threads <Work-item (Thread)>`, distributed across the GPU's
        :term:`compute units <Compute units>`. See :ref:`hip:device_program` for
        kernel programming details.
    HIP thread hierarchy
        The thread hierarchy structures parallel work from individual
        :term:`threads <Work-item (Thread)>` to
        :term:`blocks <Work-group (Block)>` to :term:`grids <Grid>`, mapping
        onto hardware from :term:`SIMD lanes <SIMD core>` to
        :term:`compute units <Compute units>` to the entire GPU. See
        :ref:`hip:inherent_thread_model` for complete details.
    HIP memory hierarchy
        The memory hierarchy pairs each
        :term:`thread hierarchy <HIP thread hierarchy>` level with corresponding
        memory scopes, from :term:`private registers <Register>` to
        :term:`LDS <Local data share>` to :term:`GPU RAM <GPU RAM (VRAM)>`. See
        :ref:`hip:memory_hierarchy` for memory architecture details.
    Global memory
        Global memory is the :term:`device-wide memory <GPU RAM (VRAM)>`
        accessible to all :term:`threads <Work-item (Thread)>`, physically
        implemented as HBM or GDDR. See :ref:`hip:memory_hierarchy` for global
        memory details.
--- a/docs/reference/glossary/host-software.rst
+++ b/docs/reference/glossary/host-software.rst
@@ -1,67 +0,0 @@
 .. meta::
  :description: Host software glossary for AMD GPUs
  :keywords: AMD, ROCm, GPU, host software, HIP, compiler, runtime, libraries,
    profiler, amd-smi
 .. _glossary-host-software:
 **********************
 Host software glossary
 **********************
 This section provides brief definitions of development tools, compilers,
 libraries, and runtime environments for programming AMD GPUs.
 .. glossary::
    :sorted:
    ROCm software platform
        ROCm is AMD's GPU software stack, providing compiler
        toolchains, runtime environments, and performance libraries for HPC and
        AI applications. See :doc:`../../what-is-rocm` for a complete component
        overview.
    HIP C++ language extension
        HIP extends the C++ language with additional features designed for
        programming heterogeneous applications. These extensions mostly relate
        to the kernel language, but some can also be applied to host
        functionality. See :doc:`hip:how-to/hip_cpp_language_extensions` for
        language fundamentals.
    AMD SMI
        The ``amd-smi`` command-line utility queries, monitors, and manages
        AMD GPU state, providing hardware information and performance metrics.
        See :doc:`amdsmi:index` for detailed usage.
    HIP runtime API
        The HIP runtime API provides an interface for GPU programming, offering
        functions for memory management, kernel launches, and synchronization. See
        :ref:`hip:hip_runtime_api_how-to` for API overview.
    HIP compiler
        The HIP compiler ``amdclang++`` compiles HIP C++ programs into binaries
        that contain both host CPU and device GPU code. See
        :doc:`llvm-project:reference/rocmcc` for compiler flags and options.
    HIP runtime compiler
        The HIP Runtime Compiler (HIPRTC) compiles HIP source code at runtime
        into :term:`AMDGPU <AMDGPU assembly>` binary code objects, enabling
        just-in-time kernel generation, device-specific optimization, and
        dynamic code creation for different GPUs. See
        :ref:`hip:hip_runtime_compiler_how-to` for API details.
    ROCgdb
        ROCgdb is AMD's source-level debugger for HIP and ROCm applications,
        enabling debugging of both host CPU and GPU device code, including
        kernel breakpoints, stepping, and variable inspection. See
        :doc:`rocgdb:index` for usage and command reference.
    rocprofv3
        ``rocprofv3`` is AMD's primary performance analysis tool, providing
        profiling, tracing, and performance counter collection.
        See :ref:`rocprofiler-sdk:using-rocprofv3` for profiling workflows.
    ROCm and LLVM binary utilities
        ROCm and LLVM binary utilities are command-line tools for examining and
        manipulating GPU binaries and code objects. See
        :ref:`hip:binary_utilities` for utility details.
--- a/docs/reference/glossary/performance.rst
+++ b/docs/reference/glossary/performance.rst
@@ -1,135 +0,0 @@
 .. meta::
  :description: Performance glossary for AMD GPUs
  :keywords: AMD, ROCm, GPU, performance, optimization, roofline, bottleneck,
    occupancy, bandwidth, latency hiding, divergence
 .. _glossary-performance:
 *****************************
 Performance analysis glossary
 *****************************
 This section provides brief definitions of performance analysis concepts and
 optimization techniques.
 .. glossary::
    :sorted:
    Roofline model
        The roofline model is a visual performance model that determines whether
        a program is :term:`compute-bound <Compute-bound>` or
        :term:`memory-bound <Memory-bound>`. See :ref:`hip:roofline_model` for
        roofline analysis.
    Compute-bound
        Compute-bound kernels are limited by the
        :term:`arithmetic bandwidth <Arithmetic bandwidth>` of the GPU's
        :term:`compute units <Compute units>` rather than
        :term:`memory bandwidth <Memory bandwidth>`. See
        :ref:`hip:compute_bound` for compute-bound analysis.
    Memory-bound
        Memory-bound kernels are limited by
        :term:`memory bandwidth <Memory bandwidth>` rather than
        :term:`arithmetic bandwidth <Arithmetic bandwidth>`, typically due to
        low :term:`arithmetic intensity <Arithmetic intensity>`. See
        :ref:`hip:memory_bound` for memory-bound analysis.
    Arithmetic intensity
        Arithmetic intensity is the ratio of arithmetic operations to memory
        operations in a kernel, and determines performance characteristics. See
        :ref:`hip:arithmetic_intensity` for intensity analysis.
    Overhead
        Overhead latency is the time spent with no useful work being done, often
        due to CPU-side bottlenecks or kernel launch delays. See
        :ref:`hip:performance_bottlenecks` for details.
    Little's Law
        Little's Law relates concurrency, latency, and throughput, determining
        how much independent work must be in flight to hide latency. See
        :ref:`hip:littles_law` for latency hiding details.
    Memory bandwidth
        Memory bandwidth is the maximum rate at which data can be transferred
        between memory hierarchy levels, typically measured in bytes per
        second. See :ref:`hip:memory_bound` for details.
    Arithmetic bandwidth
        Arithmetic bandwidth is the peak rate at which arithmetic work can be
        performed, defining the compute roof in
        :term:`roofline models <Roofline model>`. See :ref:`hip:compute_bound`
        for details.
    Latency hiding
        Latency hiding masks long-latency operations by running many concurrent
        threads, keeping execution pipelines busy. See :ref:`hip:latency_hiding`
        for details.
    Wavefront execution state
        Wavefront execution states (*active*, *stalled*, *eligible*, *selected*)
        describe the scheduling status of :term:`wavefronts <Wavefront>` on AMD
        GPUs. See :ref:`hip:wavefront_execution` for state definitions.
    Active cycle
        An active cycle is a clock cycle in which a
        :term:`compute unit <Compute units>` has at least one active
        :term:`wavefront <Wavefront>` resident. See
        :ref:`hip:wavefront_execution` for details.
    Occupancy
        Occupancy is the ratio of active :term:`wavefronts <Wavefront>` to the
        maximum number of wavefronts that can be active on a
        :term:`compute unit <Compute units>`. See :ref:`hip:occupancy` for
        occupancy analysis.
    Pipe utilization
        Pipe utilization measures how effectively a kernel uses the execution
        pipelines within each :term:`compute unit <Compute units>`. See
        :ref:`hip:pipe_utilization` for utilization details.
    Peak rate
        Peak rate is the theoretical maximum throughput at which a hardware
        system can complete work under ideal conditions. See
        :ref:`hip:theoretical_performance_limits` for details.
    Issue efficiency
        Issue efficiency measures how effectively the
        :term:`wavefront scheduler <Wavefront scheduler>` keeps
        execution pipelines busy by issuing instructions. See
        :ref:`hip:issue_efficiency` for efficiency metrics.
    CU utilization
        CU utilization measures the percentage of time that
        :term:`compute units <Compute units>` are actively executing
        instructions. See :ref:`hip:cu_utilization` for utilization analysis.
    Wavefront divergence
        Wavefront divergence occurs when threads within a
        :term:`wavefront <Wavefront>` take different execution paths due to
        conditional statements. See :ref:`hip:branch_efficiency` for divergence
        handling details.
    Branch efficiency
        Branch efficiency measures how often all threads within a
        :term:`wavefront <Wavefront>` take the same execution path, quantifying
        control-flow uniformity. See :ref:`hip:branch_efficiency` for branch
        analysis.
    Memory coalescing
        Memory coalescing improves :term:`memory bandwidth <Memory bandwidth>`
        by servicing many logical loads or stores with fewer physical memory
        transactions. See :ref:`hip:memory_coalescing_theory` for coalescing
        patterns.
    Bank conflict
        A bank conflict occurs when multiple threads simultaneously access
        different addresses in the same :term:`LDS bank <Local data share>`,
        serializing accesses. See :ref:`hip:bank_conflicts_theory` for details.
    Register pressure
        Register pressure occurs when excessive register demand limits the
        number of active :term:`wavefronts <Wavefront>` per
        :term:`compute unit <Compute units>`, reducing
        :term:`occupancy <Occupancy>`. See
        :ref:`hip:register_pressure_theory` for details.
--- a/docs/reference/gpu-arch-specs.rst
+++ b/docs/reference/gpu-arch-specs.rst
@@ -9,12 +9,6 @@ The following tables provide an overview of the hardware specifications for AMD
 For more information about ROCm hardware compatibility, see the ROCm `Compatibility matrix <https://rocm.docs.amd.com/en/latest/compatibility/compatibility-matrix.html>`_.
 For a description of the terms used in the table, see the
 :ref:`ROCm glossary <glossary>`, or for more detailed information about GPU
 architecture and programming models, see the
 :ref:`specific documents and guides <gpu-arch-documentation>`, or
 :doc:`Understanding the HIP programming model<hip:understand/programming_model>`.
 .. tab-set::
  .. tab-item:: AMD Instinct GPUs
@@ -1133,3 +1127,125 @@ architecture and programming models, see the
          - 32
          - 11
          - 5
 Glossary
 ========
 For more information about the terms used, see the
 :ref:`specific documents and guides <gpu-arch-documentation>`, or
 :doc:`Understanding the HIP programming model<hip:understand/programming_model>`.
 **LLVM target name**
 Argument to pass to clang in ``--offload-arch`` to compile code for the given
 architecture.
 **VRAM**
 Amount of memory available on the GPU.
 **Compute Units**
 Number of compute units on the GPU.
 **Wavefront Size**
 Amount of work items that execute in parallel on a single compute unit. This
 is equivalent to the warp size in HIP.
 **LDS**
 The Local Data Share (LDS) is a low-latency, high-bandwidth scratch pad
 memory. It is local to the compute units, and can be shared by all work items
 in a work group. In HIP, the LDS can be used for shared memory, which is
 shared by all threads in a block.
 **L3 Cache (CDNA/GCN only)**
 Size of the level 3 cache. Shared by all compute units on the same GPU. Caches
 data and instructions. Similar to the Infinity Cache on RDNA architectures.
 **Infinity Cache (RDNA only)**
 Size of the infinity cache. Shared by all compute units on the same GPU. Caches
 data and instructions. Similar to the L3 Cache on CDNA/GCN architectures.
 **L2 Cache**
 Size of the level 2 cache. Shared by all compute units on the same GCD. Caches
 data and instructions.
 **Graphics L1 Cache (RDNA only)**
 An additional cache level that only exists in RDNA architectures. Local to a
 shader array.
 **L1 Vector Cache (CDNA/GCN only)**
 Size of the level 1 vector data cache. Local to a compute unit. This is the L0
 vector cache in RDNA architectures.
 **L1 Scalar Cache (CDNA/GCN only)**
 Size of the level 1 scalar data cache. Usually shared by several compute
 units. This is the L0 scalar cache in RDNA architectures.
 **L1 Instruction Cache (CDNA/GCN only)**
 Size of the level 1 instruction cache. Usually shared by several compute
 units. This is the L0 instruction cache in RDNA architectures.
 **L0 Vector Cache (RDNA only)**
 Size of the level 0 vector data cache. Local to a compute unit. This is the L1
 vector cache in CDNA/GCN architectures.
 **L0 Scalar Cache (RDNA only)**
 Size of the level 0 scalar data cache. Usually shared by several compute
 units. This is the L1 scalar cache in CDNA/GCN architectures.
 **L0 Instruction Cache (RDNA only)**
 Size of the level 0 instruction cache. Usually shared by several compute
 units. This is the L1 instruction cache in CDNA/GCN architectures.
 **VGPR File**
 Size of the Vector General Purpose Register (VGPR) file and. It holds data used in
 vector instructions.
 GPUs with matrix cores also have AccVGPRs, which are Accumulation General
 Purpose Vector Registers, used specifically in matrix instructions.
 **SGPR File**
 Size of the Scalar General Purpose Register (SGPR) file. Holds data used in
 scalar instructions.
 **GFXIP**
 GFXIP (Graphics IP) is a versioning system used by AMD to identify the GPU
 architecture and its instruction set. It helps categorize different generations
 of GPUs and their feature sets.
 **GFXIP major version**
 Defines the GPU's core instruction set and architecture, which determines
 compatibility with software stacks such as HIP and OpenCL. For example, a GFXIP
 11 major version corresponds to the RDNA 3 (Navi 3x) architecture, influencing
 driver support and available compute features.
 **GFXIP minor version**
 Represents specific variations within a GFXIP major version and affects feature sets,
 optimizations, and driver behavior in software stacks such as HIP and OpenCL. Different
 GPU models within the same major version can have unique capabilities, impacting
 performance and supported instructions.
 **GCD**
 Graphics Compute Die.
 **XCD**
 Accelerator Complex Die.
--- a/docs/reference/rocm-tools.md
+++ b/docs/reference/rocm-tools.md
@@ -6,7 +6,7 @@
  algebra, AMD">
 </head>
-# ROCm tools, compilers, and runtime API
+# ROCm tools, compilers, and runtimes
 ::::{grid} 1 2 2 2
 :gutter: 3
@@ -59,12 +59,14 @@
 * [FLANG](https://github.com/ROCm/flang/)
 :::
-:::{grid-item-card} Runtime API
+:::{grid-item-card} Runtimes
 :class-body: rocm-card-banner rocm-hue-12
 (runtimes)=
 * {doc}`AMD Compute Language Runtime (CLR) <hip:understand/amd_clr>`
 * {doc}`HIP <hip:index>`
 * {doc}`ROCR-Runtime <rocr-runtime:index>`
 :::
 ::::
--- a/docs/release/versions.md
+++ b/docs/release/versions.md
@@ -10,7 +10,6 @@
 | Version | Release date |
 | ------- | ------------ |
 | [7.2.1](https://rocm.docs.amd.com/en/docs-7.2.1/) | March 25, 2026 |
 | [7.2.0](https://rocm.docs.amd.com/en/docs-7.2.0/) | January 21, 2026 |
 | [7.1.1](https://rocm.docs.amd.com/en/docs-7.1.1/) | November 26, 2025 |
 | [7.1.0](https://rocm.docs.amd.com/en/docs-7.1.0/) | October 30, 2025 |
--- a/docs/sphinx/_toc.yml.in
+++ b/docs/sphinx/_toc.yml.in
@@ -35,8 +35,20 @@ subtrees:
        title: TensorFlow compatibility
      - file: compatibility/ml-compatibility/jax-compatibility.rst
        title: JAX compatibility
      - file: compatibility/ml-compatibility/verl-compatibility.rst
        title: verl compatibility
      - file: compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
        title: Stanford Megatron-LM compatibility
      - file: compatibility/ml-compatibility/dgl-compatibility.rst
        title: DGL compatibility
      - file: compatibility/ml-compatibility/megablocks-compatibility.rst
        title: Megablocks compatibility
      - file: compatibility/ml-compatibility/ray-compatibility.rst
        title: Ray compatibility
      - file: compatibility/ml-compatibility/llama-cpp-compatibility.rst
        title: llama.cpp compatibility
      - file: compatibility/ml-compatibility/flashinfer-compatibility.rst
        title: FlashInfer compatibility
  - file: how-to/build-rocm.rst
    title: Build ROCm from source
@@ -65,14 +77,14 @@ subtrees:
            title: Train a model with Primus and Megatron-LM
            entries:
            - file: how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
-              title: Train a model with Megatron-LM (legacy)
+              title: Train a model with Megatron-LM
          - file: how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
            title: Train a model with Primus and PyTorch
            entries:
            - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
-              title: Train a model with PyTorch (legacy)
+              title: Train a model with PyTorch
          - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
-            title: Train a model with Primus and JAX MaxText
+            title: Train a model with JAX MaxText
          - file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
            title: Train a model with LLM Foundry
          - file: how-to/rocm-for-ai/training/scale-model-training.rst
@@ -102,7 +114,7 @@ subtrees:
          - file: how-to/rocm-for-ai/inference/llm-inference-frameworks.rst
            title: LLM inference frameworks
          - file: how-to/rocm-for-ai/inference/benchmark-docker/vllm.rst
-            title: vLLM inference
+            title: vLLM inference performance testing
          - file: how-to/rocm-for-ai/inference/benchmark-docker/pytorch-inference.rst
            title: PyTorch inference performance testing
          - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst
@@ -211,7 +223,7 @@ subtrees:
  - file: reference/api-libraries.md
    title: ROCm libraries
  - file: reference/rocm-tools.md
-    title: ROCm tools, compilers, and runtime API
+    title: ROCm tools, compilers, and runtimes
  - file: reference/gpu-arch-specs.rst
  - file: reference/gpu-atomics-operation.rst
  - file: reference/env-variables.rst
@@ -220,18 +232,6 @@ subtrees:
    title: Data types and precision support
  - file: reference/graph-safe-support.rst
    title: Graph safe support
  - file: reference/glossary.rst
    title: ROCm glossary
    subtrees:
    - entries:
      - file: reference/glossary/device-hardware.rst
        title: Device hardware
      - file: reference/glossary/device-software.rst
        title: Device software
      - file: reference/glossary/host-software.rst
        title: Host software
      - file: reference/glossary/performance.rst
        title: Performance
 - caption: Contribute
  entries:
--- a/docs/sphinx/requirements.in
+++ b/docs/sphinx/requirements.in
@@ -1,4 +1,4 @@
-rocm-docs-core==1.33.1
+rocm-docs-core==1.31.3
 sphinx-reredirects
 sphinx-sitemap
 sphinxcontrib.datatemplates==0.11.0
--- a/docs/sphinx/requirements.txt
+++ b/docs/sphinx/requirements.txt
@@ -37,7 +37,7 @@ click==8.3.1
    #   sphinx-external-toc
 comm==0.2.3
    # via ipykernel
-cryptography==46.0.6
+cryptography==46.0.3
    # via pyjwt
 debugpy==1.8.19
    # via ipykernel
@@ -156,7 +156,7 @@ pydata-sphinx-theme==0.15.4
    #   sphinx-book-theme
 pygithub==2.8.1
    # via rocm-docs-core
-pygments==2.20.0
+pygments==2.19.2
    # via
    #   accessible-pygments
    #   ipython
@@ -184,11 +184,11 @@ referencing==0.37.0
    # via
    #   jsonschema
    #   jsonschema-specifications
-requests==2.33.0
+requests==2.32.5
    # via
    #   pygithub
    #   sphinx
-rocm-docs-core==1.33.1
+rocm-docs-core==1.31.3
    # via -r requirements.in
 rpds-py==0.30.0
    # via
--- a/docs/what-is-rocm.rst
+++ b/docs/what-is-rocm.rst
@@ -10,13 +10,13 @@ ROCm is a software stack, composed primarily of open-source software, that
 provides the tools for programming AMD Graphics Processing Units (GPUs), from
 low-level kernels to high-level end-user applications.
-.. image:: data/rocm-software-stack-7_2_1.png
+.. image:: data/rocm-software-stack-7_0_0.jpg
  :width: 800
  :alt: AMD's ROCm software stack and enabling technologies.
  :align: center
 Specifically, ROCm provides the tools for
-:doc:`HIP <hip:index>`,
+:doc:`HIP (Heterogeneous-computing Interface for Portability) <hip:index>`,
 OpenCL and OpenMP. These include compilers, libraries for high-level
 functions, debuggers, profilers and runtimes.
@@ -143,14 +143,16 @@ Compilers
 .. csv-table::
  :header: "Component", "Description"
-  ":doc:`HIPCC <hipcc:index>`", "Compiler driver utility that calls Clang and passes the appropriate include and library options for the target compiler and HIP infrastructure"
+  ":doc:`HIPCC <hipcc:index>`", "Compiler driver utility that calls Clang or NVCC and passes the appropriate include and library options for the target compiler and HIP infrastructure"
  ":doc:`ROCm compilers <llvm-project:index>`", "ROCm LLVM compiler infrastructure"
  "`FLANG <https://github.com/ROCm/flang/>`_", "An out-of-tree Fortran compiler targeting LLVM"
-Runtime API
+Runtimes
 -----------------------------------------------
 .. csv-table::
  :header: "Component", "Description"
-  ":doc:`HIP <hip:index>`", "HIP is a C++ runtime API and kernel language for AMD GPUs"
+  ":doc:`AMD Compute Language Runtime (CLR) <hip:understand/amd_clr>`", "Contains source code for AMD's compute language runtimes: HIP and OpenCL"
  ":doc:`HIP <hip:index>`", "AMD's GPU programming language extension and the GPU runtime"
  ":doc:`ROCR-Runtime <rocr-runtime:index>`", "User-mode API interfaces and libraries necessary for host applications to launch compute kernels on available HSA ROCm kernel agents"