7.0.0 Taichi removed (#5800 )

Add JAX Plugin-PJRT support table 7.0.0 (#5672 )
* Add JAX Plugin-PJRT support table * Update wordlist.txt for JAX comp table
2026-01-09 22:58:17 -05:00 · 2025-12-19 15:43:53 -05:00 · 2025-11-20 17:42:03 +01:00 · 2025-11-13 12:09:24 -06:00 · 2025-10-28 14:13:43 +01:00 · 2025-10-17 17:33:34 -04:00
33 changed files with 1957 additions and 612 deletions
--- a/.wordlist.txt
+++ b/.wordlist.txt
@@ -72,6 +72,7 @@ CU
 CUDA
 CUs
 CXX
+CX
 Cavium
 CentOS
 ChatGPT
@@ -118,6 +119,8 @@ Dependabot
 Deprecations
 DevCap
 DirectX
+Disaggregated
+disaggregated
 Dockerfile
 Dockerized
 Doxygen
@@ -127,6 +130,7 @@ ENDPGM
 EPYC
 ESXi
 EoS
+etcd
 fas
 FBGEMM
 FIFOs
@@ -142,6 +146,8 @@ Filesystem
 FindDb
 Flang
 FlashAttention
+FlashInfer’s
+FlashInfer
 FluxBenchmark
 Fortran
 Fuyu
@@ -178,6 +184,7 @@ GPUs
 Graphbolt
 GraphSage
 GRBM
+GRE
 GenAI
 GenZ
 GitHub
@@ -235,6 +242,7 @@ Intersphinx
 Intra
 Ioffe
 JAX's
+JAXLIB
 Jinja
 JSON
 Jupyter
@@ -301,6 +309,7 @@ MirroredStrategy
 Mixtral
 MosaicML
 MoEs
+Mooncake
 Mpops
 Multicore
 Multithreaded
@@ -365,6 +374,7 @@ perf
 PEQT
 PIL
 PILImage
+PJRT
 POR
 PRNG
 PRs
@@ -445,6 +455,7 @@ SKU
 SKUs
 SLES
 SLURM
+Slurm
 SMEM
 SMFMA
 SMI
@@ -473,6 +484,7 @@ TCI
 TCIU
 TCP
 TCR
+TVM
 THREADGROUPS
 threadgroups
 TensorRT
@@ -484,8 +496,6 @@ TPS
 TPU
 TPUs
 TSME
-Taichi
-Taichi's
 Tagram
 TensileLite
 TensorBoard
@@ -615,6 +625,7 @@ coalescable
 codename
 collater
 comgr
+compat
 completers
 composable
 concretization
@@ -776,6 +787,7 @@ lossy
 macOS
 matchers
 maxtext
+megablocks
 megatron
 microarchitecture
 migraphx
@@ -934,6 +946,7 @@ softmax
 spack
 spmm
 src
+stanford
 stochastically
 strided
 subcommand
@@ -953,6 +966,7 @@ tabindex
 targetContainer
 td
 tensorfloat
+tf
 th
 tokenization
 tokenize
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,7 +6,7 @@ different versions of the ROCm software stack and its components.

 ## ROCm 7.0.0

-See the [ROCm 7.0.0 release notes](https://rocm-stg.amd.com/en/latest/about/release-notes.html#rocm-7-0-0-release-notes)
+See the [ROCm 7.0.0 release notes](https://rocm.docs.amd.com/en/docs-7.0.0/about/release-notes.html#rocm-7-0-0-release-notes)
 for a complete overview of this release.

 ### **AMD SMI** (26.0.0)
@@ -653,8 +653,8 @@ HIP runtime has the following functional improvements which improves runtime per

 #### Upcoming changes

-* `__AMDGCN_WAVEFRONT_SIZE__` macro and HIP’s `warpSize` variable as `constexpr` are deprecated and will be disabled in a future release. Users are encouraged to update their code if needed to ensure future compatibility. For more information, see [AMDGCN_WAVEFRONT_SIZE deprecation](#amdgpu-wavefront-size-compiler-macro-deprecation).
-* The `roc-obj-ls` and `roc-obj-extract` tools are  deprecated. To extract all Clang offload bundles into separate code objects use `llvm-objdump --offloading <file>`. For more information, see [Changes to ROCm Object Tooling](#changes-to-rocm-object-tooling). 
+* `__AMDGCN_WAVEFRONT_SIZE__` macro and HIP’s `warpSize` variable as `constexpr` are deprecated and will be disabled in a future release. Users are encouraged to update their code if needed to ensure future compatibility. For more information, see [AMDGCN_WAVEFRONT_SIZE deprecation](https://rocm.docs.amd.com/en/docs-7.0.0/about/release-notes.html#amdgpu-wavefront-size-compiler-macro-deprecation).
+* The `roc-obj-ls` and `roc-obj-extract` tools are  deprecated. To extract all Clang offload bundles into separate code objects use `llvm-objdump --offloading <file>`. For more information, see [Changes to ROCm Object Tooling](https://rocm.docs.amd.com/en/docs-7.0.0/about/release-notes.html#changes-to-rocm-object-tooling).

 ### **MIGraphX** (2.13.0)

--- a/RELEASE.md
+++ b/RELEASE.md
@@ -61,7 +61,7 @@ for more information about operating system and hardware compatibility.

 ROCm 7.0.0 introduces support for KVM Passthrough for AMD Instinct MI350X and MI355X GPUs.

-All KVM-based SR-IOV supported configurations require the GIM SR-IOV driver version 8.4.0.K. Refer to [GIM Release note](https://github.com/amd/MxGPU-Virtualization/releases) for more details. In addition, support for VMware ESXi 8 has been introduced for AMD Instinct MI300X GPUs. For more information, see  [Virtualization Support](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html#virtualization-support).
+All KVM-based SR-IOV supported configurations require the GIM SR-IOV driver version [8.4.0.K](https://github.com/amd/MxGPU-Virtualization/releases/tag/mainline%2F8.4.0.K). In addition, support for VMware ESXi 8 has been introduced for AMD Instinct MI300X GPUs. For more information, see  [Virtualization Support](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/system-requirements.html#virtualization-support).

 ### Deep learning and AI framework updates

@@ -98,7 +98,11 @@ Megatron-LM for ROCm now supports:

 ##### TensorFlow

-ROCm 7.0.0 enables support for TensorFlow 2.19.1.
+ROCm 7.0.0 enables the following TensorFlow features: 
+
+* Support for TensorFlow 2.19.1.
+
+* Triton autotuner support.

 ##### ONNX Runtime

@@ -124,7 +128,7 @@ AMD ROCm has officially added support for the following Deep learning and AI fra

 ### AMD GPU Driver/ROCm packaging separation

-The AMD GPU Driver (amdgpu) is now distributed separately from the ROCm software stack and is stored under in its own location ``/amdgpu/`` in the package repository at [repo.radeon.com](https://repo.radeon.com/amdgpu/). The first release is designated as AMD GPU Driver (amdgpu) version 30.10. See the [User and kernel-space support matrix](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html) for more information.
+The AMD GPU Driver (amdgpu) is now distributed separately from the ROCm software stack and is stored under in its own location ``/amdgpu/`` in the package repository at [repo.radeon.com](https://repo.radeon.com/amdgpu/). The first release is designated as [AMD GPU Driver (amdgpu) version 30.10](https://instinct.docs.amd.com/projects/amdgpu-docs/en/docs-30.10/documentation/release-notes.html#amd-gpu-driver-amdgpu-30-10-release-notes). See the [User and kernel-space support matrix](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html) for more information.

 [AMD SMI](https://github.com/ROCm/amdsmi) continues to stay with the ROCm software stack under the ROCm organization repository.

@@ -172,7 +176,7 @@ Key compiler enhancements include:
 * Comgr:
    * Added support for an in-memory virtual file system (VFS) for storing temporary files generated during intermediate compilation steps. This is designed to improve performance by reducing on-disk file I/O. Currently, VFS is supported only for the device library link step, with plans for expanded support in future releases.
 * SPIR-V:
-    * Improved [target-specific extensions](https://github.com/ROCm/llvm-project/blob/c2535466c6e40acd5ecf6ba1676a4e069c6245cc/clang/docs/LanguageExtensions.rst):
+    * Improved [target-specific extensions](https://github.com/ROCm/llvm-project/blob/c2535466c6e40acd5ecf6ba1676a4e069c6245cc/clang/docs/LanguageExtensions.rst#target-specific-extensions):
        * Added a new target-specific builtin ``__builtin_amdgcn_processor_is`` for late or deferred queries of the current target processor.
        * Added a new target-specific builtin ``__builtin_amdgcn_is_invocable``, enabling fine-grained, per-builtin feature availability.
 * The compiler driver now uses parallel code generation by default when compiling using full LTO (including when using the `-fgpu-rdc` option) for HIP. This divides the optimized LLVM IR module into roughly equal partitions before instruction selection and lowering, which can help improve build times. 
@@ -222,9 +226,9 @@ For more information about hipBLASLt changes, see the [hipBLASLt changelog](#hip

 For more information about MIGraphX changes, see the [MIGraphX changelog](migraphx-2-13-0) below.

-#### rocSHMEM Reverse Offload conduit inter-node support
+#### rocSHMEM supports Reverse Offload inter-node communication backend

-The rocSHMEM communications library has added the RO (Reverse Offload) inter-node communication backend which enables communication between GPUs on different nodes through a NIC, using a host-based CPU proxy to forward communication orders to and from the GPU.  Inter-node communication requires MPI, and is tested with Open MPI and CX7 IB NICs. For more information, see [available network backends](https://rocm.docs.amd.com/projects/rocSHMEM/en/develop/install.html#available-network-backends) for installting rocSHMEM.
+The rocSHMEM communications library has added the RO (Reverse Offload) inter-node communication backend which enables communication between GPUs on different nodes through a NIC, using a host-based CPU proxy to forward communication orders to and from the GPU.  Inter-node communication requires MPI, and is tested with Open MPI and CX7 IB NICs. For more information, see [available network backends](https://rocm.docs.amd.com/projects/rocSHMEM/en/docs-7.0.0/install.html#available-network-backends) for installing rocSHMEM.

 See the [rocSHMEM changelog](#rocshmem-3-0-0) for more details.

@@ -279,7 +283,7 @@ See the [ROCm Validation Suite changelog](#rocm-validation-suite-1-2-0) for more

 #### ROCprofiler-SDK

-##### Core SDK enhancements
+##### SDK enhancements
 
 * ROCprofiler-SDK is now compatible with the HIP 7.0.0 API.
 * ROCprofiler-SDK adds support for AMD Instinct MI350X and MI355X GPUs.
@@ -292,8 +296,7 @@ which facilitates profiling wavefronts at the instruction timing level.
 ##### rocpd
 
 The ROCm Profiling Data (``rocpd``) is now the default output format for ``rocprofv3``.
-A subproject of the ROCprofiler-SDK, ``rocpd`` enables saving profiling results to a SQLite3 database, providing a structured and 
-efficient foundation for analysis and post-processing.
+As a subcomponent of the ROCprofiler-SDK, ``rocpd`` enables storing the profiling results in a SQLite3 database, providing a structured and efficient foundation for analysis and post-processing. For details, see [Using rocpd Output Format](https://rocm.docs.amd.com/projects/rocprofiler-sdk/en/docs-7.0.0/how-to/using-rocpd-output-format.html#using-rocpd-output-format).
 
 ##### rocprofv3 CLI tool enhancements
 
@@ -335,8 +338,9 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
  benchmarking guides have been updated with expanded model coverage and 
  optimized Docker environments. Highlights include:

-  * The [Training a model with Primus and Megatron](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.html) benchmarking guide
-    now leverages the unified AMD Primus framework with the Megatron backend. See [Primus: A Lightweight, Unified Training Framework for Large Models on AMD
+  * The [Training a model with Primus and Megatron](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.html) 
+    and [Training a model with Primus and PyTorch](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.html) benchmarking guides
+    now leverage the unified AMD Primus framework with the Megatron and torchtitan backends. See [Primus: A Lightweight, Unified Training Framework for Large Models on AMD
    GPUs](https://rocm.blogs.amd.com/software-tools-optimization/primus/README.html) for an introduction to Primus.

  * The [Training a model with PyTorch](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html) benchmarking guide
@@ -345,6 +349,9 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
  * The [Training a model with JAX MaxText](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.html) benchmarking guide
    now supports [MAD](https://github.com/ROCm/MAD)-integrated benchmarking. The MaxText training environment now uses JAX 0.6.0 or 0.5.0. FP8 quantized training is supported with JAX 0.5.0.

+  * The [SGLang distributed inference](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.html?model=llama-3.1-8b-instruct) guide
+    provides a recipe to get started with disaggregated prefill/decode inference.
+
  * The [vLLM inference performance testing](https://rocm.docs.amd.com/en/latest/how-to/rocm-for-ai/inference/benchmark-docker/vllm.html) documentation
    now features clearer serving and throughput benchmarking commands -- for improved transparency of model benchmarking configurations. The vLLM inference
    environment now uses vLLM 0.10.1 and includes improved default configurations.
@@ -364,14 +371,14 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
  :margin: auto 0 auto auto
  :::{grid}
  :margin: auto 0 auto auto
-  * [hipBLAS](https://rocm.docs.amd.com/projects/hipBLAS/en/develop/reference/data-type-support.html)
-  * [hipBLASLt](https://rocm.docs.amd.com/projects/hipBLASLt/en/develop/reference/data-type-support.html)
-  * [hipSPARSE](https://rocm.docs.amd.com/projects/hipSPARSE/en/develop/reference/precision.html)
+  * [hipBLAS](https://rocm.docs.amd.com/projects/hipBLAS/en/docs-7.0.0/reference/data-type-support.html)
+  * [hipBLASLt](https://rocm.docs.amd.com/projects/hipBLASLt/en/docs-7.0.0/reference/data-type-support.html)
+  * [hipSPARSE](https://rocm.docs.amd.com/projects/hipSPARSE/en/docs-7.0.0/reference/precision.html)
  :::
  :::{grid}
  :margin: auto 0 auto auto
-  * [rocSPARSE](https://rocm.docs.amd.com/projects/rocSPARSE/en/develop/reference/precision.html)
-  * [Tensile](https://rocm.docs.amd.com/projects/Tensile/en/develop/src/reference/precision-support.html#precision-support)
+  * [rocSPARSE](https://rocm.docs.amd.com/projects/rocSPARSE/en/docs-7.0.0/reference/precision.html)
+  * [Tensile](https://rocm.docs.amd.com/projects/Tensile/en/docs-7.0.0/src/reference/precision-support.html#precision-support)
  :::
  ::::

@@ -392,15 +399,15 @@ ROCm documentation continues to be updated to provide clearer and more comprehen
  :margin: auto 0 auto auto
  :::{grid-item}
  :margin: auto 0 auto auto
-  * [hipBLASLt](https://rocm.docs.amd.com/projects/hipBLASLt/en/develop/reference/env-variables.html)
-  * [hipSPARSELt](https://rocm.docs.amd.com/projects/hipSPARSELt/en/develop/reference/env-variables.html)
-  * [ROCm Performance Primitives (RPP)](https://rocm.docs.amd.com/projects/rpp/en/develop/reference/rpp-env-variables.html)
+  * [hipBLASLt](https://rocm.docs.amd.com/projects/hipBLASLt/en/docs-7.0.0/reference/env-variables.html)
+  * [hipSPARSELt](https://rocm.docs.amd.com/projects/hipSPARSELt/en/docs-7.0.0/reference/env-variables.html)
+  * [ROCm Performance Primitives (RPP)](https://rocm.docs.amd.com/projects/rpp/en/docs-7.0.0/reference/rpp-env-variables.html)
  :::
  :::{grid-item}
  :margin: auto 0 auto auto
-  * [rocSOLVER](https://rocm.docs.amd.com/projects/rocSOLVER/en/develop/reference/env_variables.html)
-  * [rocSPARSE](https://rocm.docs.amd.com/projects/rocSPARSE/en/develop/reference/env_variables.html)
-  * [Tensile](https://rocm.docs.amd.com/projects/Tensile/en/develop/src/reference/environment-variables.html)
+  * [rocSOLVER](https://rocm.docs.amd.com/projects/rocSOLVER/en/docs-7.0.0/reference/env_variables.html)
+  * [rocSPARSE](https://rocm.docs.amd.com/projects/rocSPARSE/en/docs-7.0.0/reference/env_variables.html)
+  * [Tensile](https://rocm.docs.amd.com/projects/Tensile/en/docs-7.0.0/src/reference/environment-variables.html)
  :::
  ::::

@@ -408,9 +415,18 @@ ROCm documentation continues to be updated to provide clearer and more comprehen

 ## User space, driver, and firmware dependent changes

-GPU Software for AMD datacenter GPU products requires you to maintain a hardware and software stack with interdependencies between the GPU and baseboard firmware, AMD GPU drivers, and the ROCm user space software. Starting ROCm 7.0.0 release, we are publicly documenting these interdependencies. Note that while AMD publishes drivers and ROCm user space, your server or infrastructure provider publishes the GPU and baseboard firmware by bundling AMD’s firmware releases via AMD's Platform Level Data Model (PLDM) bundle (Firmware), which includes Integrated Firmware Image (IFWI).
+Running GPU software on AMD data center GPUs requires maintaining a coordinated
+hardware and software stack. This stack has interdependencies between the GPU
+and baseboard firmware, AMD GPU drivers, and the ROCm user-space software.
+As of the ROCm 7.0.0 release, these interdependencies are publicly documented.
+While AMD publishes drivers and ROCm user space components, your server or
+infrastructure provider publishes the GPU and baseboard firmware by bundling
+AMD’s firmware releases via AMD’s Platform Level Data Model (PLDM) bundle,
+which includes Integrated Firmware Image (IFWI).

-The GPU and baseboard firmware releases numbering may vary by GPU family. Note that, ROCm 7.0.0 release is the first release where the AMD GPU Driver (amdgpu) is versioned independently of ROCm.
+GPU and baseboard firmware versioning might differ across GPU families. Note that with the
+ROCm 7.0.0 release, the AMD GPU driver (amdgpu) is now versioned separately
+from ROCm. See [AMD GPU Driver/ROCm packaging separation](#amd-gpu-driver-rocm-packaging-separation).

 <div class="pst-scrollable-table-container">
  <table class="table" align="left" valign="middle">
@@ -443,7 +459,7 @@ The GPU and baseboard firmware releases numbering may vary by GPU family. Note t
          <td rowspan="9" style="vertical-align: middle;">ROCm 7.0.0</td>
          <td>MI355X</td>
          <td>
-              01.25.13.04 (or later)<br>
+              01.25.13.09 (or later)<br>
              01.25.11.02
          </td>
          <td>30.10</td>
@@ -452,7 +468,7 @@ The GPU and baseboard firmware releases numbering may vary by GPU family. Note t
      <tr>
          <td>MI350X</td>
          <td>
-              01.25.13.04 (or later)<br>
+              01.25.13.09 (or later)<br>
              01.25.11.02
          </td>
          <td>30.10</td>
@@ -460,7 +476,7 @@ The GPU and baseboard firmware releases numbering may vary by GPU family. Note t
      <tr>
          <td>MI325X</td>
          <td>
-              01.25.04.00 (or later)<br>
+              01.25.04.02 (or later)<br>
              01.25.03.03
          </td>
          <td>
@@ -491,11 +507,11 @@ The GPU and baseboard firmware releases numbering may vary by GPU family. Note t
      </tr>
      <tr>
          <td>MI250</td>
-          <td>MU5 w/ IFWI 75 (or later)</td>
+          <td>MU3 w/ IFWI 73</td>
      </tr>
      <tr>
          <td>MI210</td>
-          <td>MU5 w/ IFWI 75</td>
+          <td>MU3 w/ IFWI 73</td>
          <td>8.4.0.K</td>
      </tr>
      <tr>
@@ -512,11 +528,11 @@ The GPU and baseboard firmware releases numbering may vary by GPU family. Note t

 New APIs introduced in AMD SMI for ROCm 7.0.0 provide additional data for the AMD Instinct products. To support these features, the following firmware for each GPUs are required:

-* AMD Instinct MI355X - PLDM bundle 01.25.13.04
+* AMD Instinct MI355X - PLDM bundle 01.25.13.09

-* AMD Instinct MI350X - PLDM bundle 01.25.13.04
+* AMD Instinct MI350X - PLDM bundle 01.25.13.09

-* AMD Instinct MI325X - PLDM bundle 01.25.04.00
+* AMD Instinct MI325X - PLDM bundle 01.25.04.02

 * AMD Instinct MI300X - PLDM bundle 01.25.03.12

@@ -524,7 +540,7 @@ If ROCm 7.0.0 is applied on system with prior version of PLDM bundles (firmware)

 #### Enhanced temperature telemetry introduced in AMD SMI for MI355X and MI350X GPUs

-AMD SMI in ROCm 7.0.0 provides support for enhanced temperature metrics and temperature anomaly detection for AMD Instinct MI350X and MI355X GPUs when paired with: PLDM bundle 01.25.13.04.
+AMD SMI in ROCm 7.0.0 provides support for enhanced temperature metrics and temperature anomaly detection for AMD Instinct MI350X and MI355X GPUs when paired with: PLDM bundle 01.25.13.09.

 For more information on these features, see [AMD SMI changelog](https://github.com/ROCm/amdsmi/blob/release/rocm-rel-7.0/CHANGELOG.md).

@@ -534,7 +550,7 @@ KVM SR-IOV support for all Instinct GPUs require the open source AMD GPU Virtual

 #### GPU partitioning support for AMD Instinct MI355X and MI350X GPUs

-NPS2 and DPX partitioning on bare metal is enabled on AMD Instinct MI355X and MI350X GPUs on ROCm 7.0.0 when paired with: PLDM bundle 01.25.13.04.
+NPS2 and DPX partitioning on bare metal is enabled on AMD Instinct MI355X and MI350X GPUs on ROCm 7.0.0 when paired with: PLDM bundle 01.25.13.09.

 ## ROCm components

@@ -2542,31 +2558,31 @@ issues related to individual components, review the [Detailed component changes]

 ### A memory error in the kernel might lead to applications using the ROCr library becoming unresponsive

-Applications using the ROCr library might become unresponsive if a memory error occurs in the launched kernel when the queue from which it was launched is destroyed. The application is unable to receive further signal, resulting in the stall condition. The issue will be fixed in a future ROCm release.
+Applications using the ROCr library might become unresponsive if a memory error occurs in the launched kernel when the queue from which it was launched is destroyed. The application is unable to receive further signal, resulting in the stall condition. The issue will be fixed in a future ROCm release. See [GitHub issue #5334](https://github.com/ROCm/ROCm/issues/5334).

 ### Applications using stream capture APIs might fail during stream capture

-Applications using ``hipLaunchHostFunc`` with stream capture APIs might fail to capture graphs during stream capture, and return `hipErrorStreamCaptureUnsupported`. This issue resulted from an update in ``hipStreamAddCallback``. This issue will be fixed in a future ROCm release.
+Applications using ``hipLaunchHostFunc`` with stream capture APIs might fail to capture graphs during stream capture, and return `hipErrorStreamCaptureUnsupported`. This issue resulted from an update in ``hipStreamAddCallback``. This issue will be fixed in a future ROCm release. See [GitHub issue #5337](https://github.com/ROCm/ROCm/issues/5337).

 ### Compilation failure via hipRTC when compiling with std=c++11

-Applications compiling kernels using `hipRTC` might fail while passing the `std=c++11` compiler option. This issue will be fixed in a future ROCm release. 
+Applications compiling kernels using `hipRTC` might fail while passing the `std=c++11` compiler option. This issue will be fixed in a future ROCm release. See [GitHub issue #5341](https://github.com/ROCm/ROCm/issues/5341).

 ### Compilation failure when referencing std::array if _GLIBCXX_ASSERTIONS is defined

-Compiling from a device kernel or function results in failure when attempting to reference `std::array` if `_GLIBCXX_ASSERTIONS` is defined. The issue occurs because there's no device definition for `std::__glibcxx_asert_fail()`. This issue will be resolved in a future ROCm release with the implementation of `std::__glibcxx_assert_fail()`.
+Compiling from a device kernel or function results in failure when attempting to reference `std::array` if `_GLIBCXX_ASSERTIONS` is defined. The issue occurs because there's no device definition for `std::__glibcxx_asert_fail()`. This issue will be resolved in a future ROCm release with the implementation of `std::__glibcxx_assert_fail()`. See [GitHub issue #5342](https://github.com/ROCm/ROCm/issues/5342).

 ### Segmentation fault in ROCprofiler-SDK due to ABI mismatch affecting std::regex

-Starting with GCC 5.1, GNU `libstdc++` introduced a dual Application Binary Interface (ABI) to adopt `C++11`, primarily affecting the `std::string` and its dependencies, including `std::regex`. If your code is compiled against headers expecting one ABI but linked or run with the other, it can cause problems with `std::string` and `std::regex`, leading to a segmentation fault in ROCprofiler-SDK, which uses `std::regex`. This issue is resolved in the [ROCm Systems `develop` branch](https://github.com/ROCm/rocm-systems) and will be part of a future ROCm release.
+Starting with GCC 5.1, GNU `libstdc++` introduced a dual Application Binary Interface (ABI) to adopt `C++11`, primarily affecting the `std::string` and its dependencies, including `std::regex`. If your code is compiled against headers expecting one ABI but linked or run with the other, it can cause problems with `std::string` and `std::regex`, leading to a segmentation fault in ROCprofiler-SDK, which uses `std::regex`. This issue is resolved in the [ROCm Systems `develop` branch](https://github.com/ROCm/rocm-systems) and will be part of a future ROCm release. See [GitHub issue #5343](https://github.com/ROCm/ROCm/issues/5343).

 ### Decline in performance of batched GEMM operation for applications using hipBLASLT kernels

-Default batched General Matrix Multiplications (GEMM) operations for rocBLAS and hipBLAS on gfx1200 and gfx1201 may have a decline in performance in comparison with non-batched and strided_batched GEMM operations. By default, the batched GEMM uses hipBLASLT kernels, and switching to the Tensile kernel resolves the performance decline issue. The issue will be fixed in a future ROCm release. As a workaround, you can set the environment variable `ROCBLAS_USE_HIPBLASLT=0` before the batched GEMM operation is performed on gfx1200 and gfx1201. After completing the batched operation, reset the variable to `ROCBLAS_USE_HIPBLASLT=1` before calling non-batched or strided_batched operations.
+Default batched General Matrix Multiplications (GEMM) operations for rocBLAS and hipBLAS on gfx1200 and gfx1201 may have a decline in performance in comparison with non-batched and strided_batched GEMM operations. By default, the batched GEMM uses hipBLASLT kernels, and switching to the Tensile kernel resolves the performance decline issue. The issue will be fixed in a future ROCm release. As a workaround, you can set the environment variable `ROCBLAS_USE_HIPBLASLT=0` before the batched GEMM operation is performed on gfx1200 and gfx1201. After completing the batched operation, reset the variable to `ROCBLAS_USE_HIPBLASLT=1` before calling non-batched or strided_batched operations. See [GitHub issue #5344](https://github.com/ROCm/ROCm/issues/5344).

 ### Failure to declare out-of-bound CPERs for bad memory page

-Exceeding bad memory page threshold fails to declare Out-Of-Band Common Platform Error Records (CPERs). This issue affects all AMD Instinct MI300 Series and MI350 Series GPUs, and will be fixed in a future AMD GPU Driver release.
+Exceeding bad memory page threshold fails to declare Out-Of-Band Common Platform Error Records (CPERs). This issue affects all AMD Instinct MI300 Series and MI350 Series GPUs, and will be fixed in a future AMD GPU Driver release. See [GitHub issue #5345](https://github.com/ROCm/ROCm/issues/5345).

 ## ROCm resolved issues

@@ -2611,7 +2627,7 @@ It's anticipated that ROCTracer, ROCProfiler, `rocprof`, and `rocprofv2` will re
 ### AMDGPU wavefront size compiler macro deprecation

 Access to the wavefront size as a compile-time constant via the `__AMDGCN_WAVEFRONT_SIZE`
-and `__AMDGCN_WAVEFRONT_SIZE__` macros are deprecated and will be disabled in a future release. In ROCm 7.0.0 `warpSize` is only available as a non-`constextpr` variable. You're encouraged to update your code if needed to ensure future compatibility.
+and `__AMDGCN_WAVEFRONT_SIZE__` macros are deprecated and will be disabled in a future release. In ROCm 7.0.0 `warpSize` is only available as a non-`constexpr` variable. You're encouraged to update your code if needed to ensure future compatibility.

 * The `__AMDGCN_WAVEFRONT_SIZE__` macro and `__AMDGCN_WAVEFRONT_SIZE` alias will be removed in an upcoming release.
  It is recommended to remove any use of this macro. For more information, see
--- a/docs/compatibility/compatibility-matrix-historical-6.0.csv
+++ b/docs/compatibility/compatibility-matrix-historical-6.0.csv
@@ -3,13 +3,13 @@ ROCm Version,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
      ,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"
      ,"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, 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, 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"
+      ,RHEL 8.10 [#rhel-700-past-60]_,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-db-700-past-60]_,"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
      ,"Oracle Linux 9, 8 [#ol-700-mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_","Oracle Linux 9, 8 [#mi300x-past-60]_",Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.10 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,Oracle Linux 8.9 [#mi300x-past-60]_,,,
-      ,Debian 12,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,,,,,,,,,,,
+      ,Debian 12 [#sles-db-700-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,Debian 12 [#single-node-past-60]_,,,,,,,,,,,
      ,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-past-60]_,Azure Linux 3.0 [#az-mi300x-630-past-60]_,Azure Linux 3.0 [#az-mi300x-630-past-60]_,,,,,,,,,,,,
-,Rocky Linux 9,,,,,,,,,,,,,,,,,,
+,Rocky Linux 9 [#rl-700-past-60]_,,,,,,,,,,,,,,,,,,
      ,.. _architecture-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,
      :doc:`Architecture <rocm-install-on-linux:reference/system-requirements>`,CDNA4,,,,,,,,,,,,,,,,,,
 ,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3,CDNA3
@@ -19,25 +19,27 @@ ROCm Version,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
      ,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
      ,.. _gpu-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950,,,,,,,,,,,,,,,,,,
-,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
-      ,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
-      ,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
-      ,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
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950 [#mi350x-os-past-60]_,,,,,,,,,,,,,,,,,,
+,gfx1201 [#RDNA-OS-700-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,gfx1201 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
+      ,gfx1200 [#RDNA-OS-700-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,gfx1200 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
+,gfx1101 [#RDNA-OS-700-past-60]_ [#rd-v710-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_ [#7700XT-OS-past-60]_,gfx1101 [#RDNA-OS-past-60]_,,,,,,,,,,,,,,,
+      ,gfx1100 [#RDNA-OS-700-past-60]_,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100,gfx1100
+      ,gfx1030 [#RDNA-OS-700-past-60]_ [#rd-v620-past-60]_,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030,gfx1030
+      ,gfx942 [#mi325x-os-past-60]_ [#mi300x-os-past-60]_ [#mi300A-os-past-60]_,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942,gfx942 [#mi300_624-past-60]_,gfx942 [#mi300_622-past-60]_,gfx942 [#mi300_621-past-60]_,gfx942 [#mi300_620-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_612-past-60]_, gfx942 [#mi300_611-past-60]_, gfx942 [#mi300_610-past-60]_, gfx942 [#mi300_602-past-60]_, gfx942 [#mi300_600-past-60]_
+      ,gfx90a [#mi200x-os-past-60]_,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a,gfx90a
+      ,gfx908 [#mi100-os-past-60]_,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:,,,,,,,,,,,,,,,,,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.7, 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","2.6, 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>`,"2.19.1, 2.18.1","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:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"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>`,"2.19.1, 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>`,0.6.0,0.4.35,0.4.35,0.4.35,0.4.35,0.4.31,0.4.31,0.4.31,0.4.31,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26,0.4.26
-      :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat]_,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,0.3.0.post0,N/A,N/A,N/A,N/A,N/A,
-      :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`,N/A,N/A,N/A,N/A,N/A,85f95ae,85f95ae,85f95ae,85f95ae,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]_,N/A,N/A,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,
-      :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>`,N/A,N/A,N/A,N/A,N/A,0.7.0,0.7.0,0.7.0,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,
-      :doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat]_,N/A,N/A,N/A,N/A,N/A,N/A,1.8.0b1,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,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
+      :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,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,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,0.7.0,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A,N/A
+      :doc:`Ray <../compatibility/ml-compatibility/ray-compatibility>` [#ray_compat-past-60]_,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
+      :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat-past-60]_,b6356,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
+      :doc:`FlashInfer <../compatibility/ml-compatibility/flashinfer-compatibility>` [#flashinfer_compat-past-60]_,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>`_,1.22.0,1.20.0,1.20.0,1.20.0,1.20.0,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.17.3,1.14.1,1.14.1
 ,,,,,,,,,,,,,,,,,,,
      ,,,,,,,,,,,,,,,,,,,
@@ -49,8 +51,8 @@ ROCm Version,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
      Thrust,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,2.6.0,2.5.0,2.5.0,2.5.0,2.5.0,2.3.2,2.3.2,2.3.2,2.3.2,2.2.0,2.2.0,2.2.0,2.2.0,2.1.0,2.1.0,2.1.0,2.1.0,2.0.1,2.0.1
 ,,,,,,,,,,,,,,,,,,,
-      KMD & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,
-      :doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"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"
+      DRIVER & USER SPACE [#kfd_support-past-60]_,.. _kfd-userspace-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,
+      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"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"
      ,,,,,,,,,,,,,,,,,,,
      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix-past-60:,,,,,,,,,,,,,,,,,,
      :doc:`Composable Kernel <composable_kernel:index>`,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
--- a/docs/compatibility/compatibility-matrix.rst
+++ b/docs/compatibility/compatibility-matrix.rst
@@ -29,10 +29,10 @@ compatibility and system requirements.
      :ref:`Operating systems & kernels <OS-kernel-versions>`,Ubuntu 24.04.3,Ubuntu 24.04.2,Ubuntu 24.04.2
      ,Ubuntu 22.04.5,Ubuntu 22.04.5,Ubuntu 22.04.5
      ,"RHEL 9.6, 9.4","RHEL 9.6, 9.4","RHEL 9.5, 9.4"
-      ,RHEL 8.10 [#rhel-700]_,RHEL 8.10 [#rhel-700],RHEL 8.10 [#rhel-700]
-      ,SLES 15 SP7,"SLES 15 SP7, SP6","SLES 15 SP6, SP5"
+      ,RHEL 8.10 [#rhel-700]_,RHEL 8.10,RHEL 8.10
+      ,SLES 15 SP7 [#sles-db-700]_,"SLES 15 SP7, SP6","SLES 15 SP6, SP5"
      ,"Oracle Linux 9, 8 [#ol-700-mi300x]_","Oracle Linux 9, 8 [#ol-mi300x]_",Oracle Linux 8.10 [#ol-mi300x]_
-      ,Debian 12,Debian 12 [#single-node]_,
+      ,Debian 12 [#sles-db-700]_,Debian 12 [#single-node]_,
      ,Azure Linux 3.0 [#az-mi300x]_,Azure Linux 3.0 [#az-mi300x]_,
      ,Rocky Linux 9 [#rl-700]_,,
      ,.. _architecture-support-compatibility-matrix:,,
@@ -44,25 +44,23 @@ compatibility and system requirements.
      ,RDNA3,RDNA3,RDNA3
      ,RDNA2,RDNA2,RDNA2
      ,.. _gpu-support-compatibility-matrix:,,
-      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950,,
-      ,gfx1201 [#RDNA-OS]_,gfx1201 [#RDNA-OS]_,
-      ,gfx1200 [#RDNA-OS]_,gfx1200 [#RDNA-OS]_,
-      ,gfx1101 [#RDNA-OS]_ [#7700XT-OS]_,gfx1101 [#RDNA-OS]_ [#7700XT-OS]_,
-      ,gfx1100,gfx1100,gfx1100
-      ,gfx1030,gfx1030,gfx1030
-      ,gfx942,gfx942,gfx942
-      ,gfx90a,gfx90a,gfx90a
-      ,gfx908,gfx908,gfx908
+      :doc:`GPU / LLVM target <rocm-install-on-linux:reference/system-requirements>`,gfx950 [#mi350x-os]_,,
+      ,gfx1201 [#RDNA-OS-700]_,gfx1201 [#RDNA-OS]_,
+      ,gfx1200 [#RDNA-OS-700]_,gfx1200 [#RDNA-OS]_,
+      ,gfx1101 [#RDNA-OS-700]_ [#rd-v710]_,gfx1101 [#RDNA-OS]_ [#7700XT-OS]_,
+      ,gfx1100 [#RDNA-OS-700]_,gfx1100,gfx1100
+      ,gfx1030 [#RDNA-OS-700]_ [#rd-v620]_,gfx1030,gfx1030
+      ,gfx942 [#mi325x-os]_ [#mi300x-os]_ [#mi300A-os]_,gfx942,gfx942
+      ,gfx90a [#mi200x-os]_,gfx90a,gfx90a
+      ,gfx908 [#mi100-os]_,gfx908,gfx908
      ,,,
      FRAMEWORK SUPPORT,.. _framework-support-compatibility-matrix:,,
-      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.7, 2.6, 2.5, 2.4, 2.3","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 2.1, 2.0, 1.13"
-      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.19.1, 2.18.1","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1"
+      :doc:`PyTorch <../compatibility/ml-compatibility/pytorch-compatibility>`,"2.7, 2.6, 2.5","2.6, 2.5, 2.4, 2.3","2.4, 2.3, 2.2, 2.1, 2.0, 1.13"
+      :doc:`TensorFlow <../compatibility/ml-compatibility/tensorflow-compatibility>`,"2.19.1, 2.18.1, 2.17.1 [#tf-mi350]_","2.18.1, 2.17.1, 2.16.2","2.17.0, 2.16.2, 2.15.1"
      :doc:`JAX <../compatibility/ml-compatibility/jax-compatibility>`,0.6.0,0.4.35,0.4.31
-      :doc:`verl <../compatibility/ml-compatibility/verl-compatibility>` [#verl_compat]_,N/A,N/A,N/A
-      :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>`,N/A,N/A,85f95ae
-      :doc:`DGL <../compatibility/ml-compatibility/dgl-compatibility>` [#dgl_compat]_,N/A,N/A,N/A
-      :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>`,N/A,N/A,0.7.0
-      :doc:`Taichi <../compatibility/ml-compatibility/taichi-compatibility>` [#taichi_compat]_,N/A,N/A,N/A
+      :doc:`Stanford Megatron-LM <../compatibility/ml-compatibility/stanford-megatron-lm-compatibility>` [#stanford-megatron-lm_compat]_,N/A,N/A,85f95ae
+      :doc:`Megablocks <../compatibility/ml-compatibility/megablocks-compatibility>` [#megablocks_compat]_,N/A,N/A,0.7.0
+      :doc:`llama.cpp <../compatibility/ml-compatibility/llama-cpp-compatibility>` [#llama-cpp_compat]_,b6356,b6356,N/A
      `ONNX Runtime <https://onnxruntime.ai/docs/build/eps.html#amd-migraphx>`_,1.22.0,1.20.0,1.17.3
      ,,,
      THIRD PARTY COMMS,.. _thirdpartycomms-support-compatibility-matrix:,,
@@ -73,8 +71,8 @@ compatibility and system requirements.
      Thrust,2.6.0,2.5.0,2.3.2
      CUB,2.6.0,2.5.0,2.3.2
      ,,,
-      KMD & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
-      :doc:`KMD versions <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"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"
+      DRIVER & USER SPACE [#kfd_support]_,.. _kfd-userspace-support-compatibility-matrix:,,
+      :doc:`AMD GPU Driver <rocm-install-on-linux:reference/user-kernel-space-compat-matrix>`,"30.10, 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"
      ,,,
      ML & COMPUTER VISION,.. _mllibs-support-compatibility-matrix:,,
      :doc:`Composable Kernel <composable_kernel:index>`,1.1.0,1.1.0,1.1.0
@@ -159,16 +157,28 @@ compatibility and system requirements.
 .. rubric:: Footnotes

 .. [#rhel-700] RHEL 8.10 is only supported on AMD Instinct MI300X, MI300A, MI250X, MI250, MI210, and MI100 GPUs.
-.. [#ol-700-mi300x] **For ROCm 7.0** - Oracle Linux 9 is supported only on AMD Instinct MI300X, MI350X, and MI355X. Oracle Linux 8 is only supported on AMD Instinct MI300X.
-.. [#ol-mi300x] **Prior ROCm 7.0** - Oracle Linux is supported only on AMD Instinct MI300X.
-.. [#sles-db-700] SLES 15 SP7 and Debian 12 are only supported on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
+.. [#ol-700-mi300x] **For ROCm 7.0.0** - Oracle Linux 9 is supported only on AMD Instinct MI355X, MI350X, and MI300X GPUs. Oracle Linux 8 is supported only on AMD Instinct MI300X GPUs.
+.. [#ol-mi300x] **Prior ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X GPUs.
+.. [#sles-db-700] **For ROCm 7.0.0** - SLES 15 SP7 and Debian 12 are only supported on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
 .. [#az-mi300x] Starting ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710.
 .. [#rl-700] Rocky Linux 9 is only supported on AMD Instinct MI300X and MI300A GPUs.
 .. [#single-node] **Prior to ROCm 7.0.0** - Debian 12 is supported only on AMD Instinct MI300X for single-node functionality.
-.. [#az-mi300x] Starting from ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710.
-.. [#RDNA-OS] Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
-.. [#7700XT-OS] Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
-.. [#kfd_support] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) 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 kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
+.. [#mi350x-os] AMD Instinct MI355X (gfx950) and MI350X(gfx950) GPUs are only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.4, and Oracle Linux 9.
+.. [#RDNA-OS-700] **For ROCm 7.0.0** - AMD Radeon PRO AI PRO R9700 (gfx1201), AMD Radeon RX 9070 XT (gfx1201), AMD Radeon RX 9070 GRE (gfx1201), AMD Radeon RX 9070 (gfx1201), AMD Radeon RX 9060 XT (gfx1200), AMD Radeon RX 7800 XT (gfx1101), AMD Radeon RX 7700 XT (gfx1101), AMD Radeon PRO W7700 (gfx1101), and AMD Radeon PRO W6800 (gfx1030) are only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, and RHEL 9.6.
+.. [#RDNA-OS] **Prior ROCm 7.0.0** - Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
+.. [#rd-v710] **For ROCm 7.0.0** - AMD Radeon PRO V710 (gfx1101) is only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and Azure Linux 3.0.
+.. [#rd-v620] **For ROCm 7.0.0** - AMD Radeon PRO V620 (gfx1030) is only supported on Ubuntu 24.04.3 and Ubuntu 22.04.5.
+.. [#mi325x-os] **For ROCm 7.0.0** - AMD Instinct MI325X GPU (gfx942) is only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
+.. [#mi300x-os] **For ROCm 7.0.0** - AMD Instinct MI300X GPU (gfx942) is supported on all listed :ref:`supported_distributions`.
+.. [#mi300A-os] **For ROCm 7.0.0** - AMD Instinct MI300A GPU (gfx942) is supported only on Ubuntu 24.04, Ubuntu 22.04, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, Debian 12, and Rocky Linux 9.
+.. [#mi200x-os] **For ROCm 7.0.0** - AMD Instinct MI200 Series GPUs (gfx90a) are supported only on Ubuntu 24.04, Ubuntu 22.04, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, and Debian 12.
+.. [#mi100-os] **For ROCm 7.0.0** - AMD Instinct MI100 GPU (gfx908) is only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.4, and RHEL 8.10.
+.. [#7700XT-OS] **Prior ROCm 7.0.0** - Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
+.. [#tf-mi350] 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.
+.. [#stanford-megatron-lm_compat] Stanford Megatron-LM is only supported on ROCm 6.3.0.
+.. [#megablocks_compat] Megablocks is only supported on ROCm 6.3.0.
+.. [#llama-cpp_compat] llama.cpp is only supported on ROCm 7.0.0 and 6.4.x.
+.. [#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>`_.
 .. [#ROCT-rocr] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.


@@ -240,12 +250,24 @@ Expand for full historical view of:

   .. rubric:: Footnotes

+   .. [#rhel-700-past-60] **For ROCm 7.0.0** - RHEL 8.10 is only supported on AMD Instinct MI300X, MI300A, MI250X, MI250, MI210, and MI100 GPUs.
   .. [#ol-700-mi300x-past-60] **For ROCm 7.0.0** - Oracle Linux 9 is supported only on AMD Instinct MI300X, MI350X, and MI355X. Oracle Linux 8 is only supported on AMD Instinct MI300X.
-   .. [#mi300x-past-60] **Prior to ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X.
-   .. [#single-node-past-60] **Prior to ROCm 7.0.0 ** - Debian 12 is supported only on AMD Instinct MI300X for single-node functionality.
+   .. [#mi300x-past-60] **Prior ROCm 7.0.0** - Oracle Linux is supported only on AMD Instinct MI300X.
+   .. [#sles-db-700-past-60] **For ROCm 7.0.0** - SLES 15 SP7 and Debian 12 are only supported on AMD Instinct MI300X, MI300A, MI250X, MI250, and MI210 GPUs.
+   .. [#single-node-past-60] **Prior to ROCm 7.0.0** - Debian 12 is supported only on AMD Instinct MI300X for single-node functionality.
   .. [#az-mi300x-past-60] Starting from ROCm 6.4.0, Azure Linux 3.0 is supported only on AMD Instinct MI300X and AMD Radeon PRO V710.
   .. [#az-mi300x-630-past-60] **Prior ROCm 6.4.0**- Azure Linux 3.0 is supported only on AMD Instinct MI300X.
-   .. [#RDNA-OS-past-60] Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
+   .. [#rl-700-past-60] Rocky Linux 9 is only supported on AMD Instinct MI300X and MI300A GPUs.
+   .. [#mi350x-os-past-60] AMD Instinct MI355X (gfx950) and MI350X(gfx950) GPUs are only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.4, and Oracle Linux 9.
+   .. [#RDNA-OS-700-past-60] **For ROCm 7.0.0** AMD Radeon PRO AI PRO R9700 (gfx1201), AMD Radeon RX 9070 XT (gfx1201), AMD Radeon RX 9070 GRE (gfx1201), AMD Radeon RX 9070 (gfx1201), AMD Radeon RX 9060 XT (gfx1200), AMD Radeon RX 7800 XT (gfx1101), AMD Radeon RX 7700 XT (gfx1101), AMD Radeon PRO W7700 (gfx1101), and AMD Radeon PRO W6800 (gfx1030) are only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, and RHEL 9.6.
+   .. [#RDNA-OS-past-60] **Prior ROCm 7.0.0** - Radeon AI PRO R9700, Radeon RX 9070 XT (gfx1201), Radeon RX 9060 XT (gfx1200), Radeon PRO W7700 (gfx1101), and Radeon RX 7800 XT (gfx1101) are supported only on Ubuntu 24.04.2, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
+   .. [#rd-v710-past-60] **For ROCm 7.0.0** - AMD Radeon PRO V710 (gfx1101) is only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and Azure Linux 3.0.
+   .. [#rd-v620-past-60] **For ROCm 7.0.0** - AMD Radeon PRO V620 (gfx1030) is only supported on Ubuntu 24.04.3 and Ubuntu 22.04.5.
+   .. [#mi325x-os-past-60] **For ROCm 7.0.0** - AMD Instinct MI325X GPU (gfx942) is only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, and RHEL 9.4.
+   .. [#mi300x-os-past-60] **For ROCm 7.0.0** - AMD Instinct MI300X GPU (gfx942) is supported on all listed :ref:`supported_distributions`.
+   .. [#mi300A-os-past-60] **For ROCm 7.0.0** - AMD Instinct MI300A GPU (gfx942) is supported only on Ubuntu 24.04, Ubuntu 22.04, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, Debian 12, and Rocky Linux 9.
+   .. [#mi200x-os-past-60] **For ROCm 7.0.0** - AMD Instinct MI200 Series GPUs (gfx90a) are supported only on Ubuntu 24.04, Ubuntu 22.04, RHEL 9.6, RHEL 9.4, RHEL 8.10, SLES 15 SP7, and Debian 12.
+   .. [#mi100-os-past-60] **For ROCm 7.0.0** - AMD Instinct MI100 GPU (gfx908) is only supported on Ubuntu 24.04.3, Ubuntu 22.04.5, RHEL 9.6, RHEL 9.4, and RHEL 8.10.
   .. [#7700XT-OS-past-60] Radeon RX 7700 XT (gfx1101) is supported only on Ubuntu 24.04.2 and RHEL 9.6.
   .. [#mi300_624-past-60] **For ROCm 6.2.4** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
   .. [#mi300_622-past-60] **For ROCm 6.2.2** - MI300X (gfx942) is supported on listed operating systems *except* Ubuntu 22.04.5 [6.8 HWE] and Ubuntu 22.04.4 [6.5 HWE].
@@ -256,13 +278,14 @@ Expand for full historical view of:
   .. [#mi300_610-past-60] **For ROCm 6.1.0** - MI300A (gfx942) is supported on Ubuntu 22.04.4, RHEL 9.4, RHEL 9.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.4.
   .. [#mi300_602-past-60] **For ROCm 6.0.2** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
   .. [#mi300_600-past-60] **For ROCm 6.0.0** - MI300A (gfx942) is supported on Ubuntu 22.04.3, RHEL 8.9, and SLES 15 SP5. MI300X (gfx942) is only supported on Ubuntu 22.04.3.
-   .. [#verl_compat] verl is only supported on ROCm 6.2.0.
-   .. [#stanford-megatron-lm_compat] Stanford Megatron-LM is only supported on ROCm 6.3.0.
-   .. [#dgl_compat] DGL is only supported on ROCm 6.4.0.
-   .. [#megablocks_compat] Megablocks is only supported on ROCm 6.3.0.
-   .. [#taichi_compat] Taichi is only supported on ROCm 6.3.2.
-   .. [#ray_compat] Ray is only supported on ROCm 6.4.1.
-   .. [#llama-cpp_compat] llama.cpp is only supported on ROCm 6.4.0.
-   .. [#kfd_support-past-60] As of ROCm 6.4.0, forward and backward compatibility between the AMD Kernel-mode GPU Driver (KMD) and its user space software is provided up to a year apart. For earlier ROCm releases, the compatibility is provided for +/- 2 releases. The tested 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 kernel-space support matrix <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/reference/user-kernel-space-compat-matrix.html>`_.
+   .. [#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.
+   .. [#verl_compat-past-60] verl is only supported on ROCm 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 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 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 6.4.1.
+   .. [#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>`_.
   .. [#ROCT-rocr-past-60] Starting from ROCm 6.3.0, the ROCT Thunk Interface is included as part of the ROCr runtime package.
   
--- a/docs/compatibility/ml-compatibility/flashinfer-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/flashinfer-compatibility.rst
@@ -0,0 +1,107 @@
+:orphan:
+
+.. meta::
+    :description: FlashInfer deep learning framework compatibility
+    :keywords: GPU, LLM, FlashInfer, 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 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 for the ROCm port of FlashInfer is available as follows:
+
+- ROCm support for FlashInfer is hosted in the `https://github.com/ROCm/flashinfer 
+  <https://github.com/ROCm/flashinfer>`__ repository. This location differs from the 
+  `https://github.com/flashinfer-ai/flashinfer <https://github.com/flashinfer-ai/flashinfer>`_ 
+  upstream repository.
+
+- To install FlashInfer, use the prebuilt :ref:`Docker image <flashinfer-docker-compat>`, 
+  which includes ROCm, FlashInfer, and all required dependencies.
+
+  - See the :doc:`ROCm FlashInfer installation guide <rocm-install-on-linux:install/3rd-party/flashinfer-install>` 
+    to install and get started.
+
+  - See the `Installation guide <https://docs.flashinfer.ai/installation.html>`__ 
+    in the upstream FlashInfer documentation.
+
+.. note::
+
+  Flashinfer is supported on ROCm 6.4.1.
+
+Supported devices
+================================================================================
+
+**Officially Supported**: AMD Instinct™ MI300X
+
+
+.. _flashinfer-recommendations:
+
+Use cases and recommendations
+================================================================================
+
+This release of FlashInfer on ROCm provides the decode functionality for LLM inferencing.
+In the decode phase, tokens are generated sequentially, with the model predicting each new 
+token based on the previously generated tokens and the input context.
+
+FlashInfer on ROCm brings over upstream features such as load balancing, sparse and dense 
+attention optimizations, and batching support, enabling efficient execution on AMD Instinct™ MI300X GPUs.
+
+Because large LLMs often require substantial KV caches or long context windows, FlashInfer on ROCm 
+also implements cascade attention from upstream to reduce memory usage. 
+
+For currently supported use cases and recommendations, refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`__, 
+where you can search for examples and best practices to optimize your workloads on AMD GPUs.
+
+.. _flashinfer-docker-compat:
+
+Docker image compatibility
+================================================================================
+
+.. |docker-icon| raw:: html
+
+   <i class="fab fa-docker"></i>
+
+AMD validates and publishes `ROCm FlashInfer images <https://hub.docker.com/r/rocm/flashinfer/tags>`__
+with ROCm and Pytorch backends on Docker Hub. The following Docker image tags and associated
+inventories represent the FlashInfer version from the official Docker Hub.
+The Docker images have been validated for `ROCm 6.4.1 <https://repo.radeon.com/rocm/apt/6.4.1/>`__.
+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
+
+    * - .. 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/>`__
+
+
--- a/docs/compatibility/ml-compatibility/jax-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/jax-compatibility.rst
@@ -47,6 +47,23 @@ with ROCm support:
   `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
   follow upstream JAX releases and use the latest available ROCm version.

+JAX Plugin-PJRT with JAX/JAXLIB compatibility
+================================================================================
+
+Portable JIT Runtime (PJRT) is an open, stable interface for device runtime and
+compiler. The following table details the ROCm version compatibility matrix
+between JAX Plugin–PJRT and JAX/JAXLIB.
+
+.. list-table::
+    :header-rows: 1
+
+    * - JAX Plugin-PJRT
+      - JAX/JAXLIB
+      - ROCm
+    * - 0.6.0
+      - 0.6.2, 0.6.0
+      - 7.0.0
+
 Use cases and recommendations
 ================================================================================

@@ -90,75 +107,15 @@ For more use cases and recommendations, see `ROCm JAX blog posts <https://rocm.b
 Docker image compatibility
 ================================================================================

-.. |docker-icon| raw:: html
+AMD provides preconfigured Docker images with JAX and the ROCm backend.
+These images are published on `Docker Hub <https://hub.docker.com/r/rocm/jax>`__ and are the
+recommended way to get started with deep learning with JAX on ROCm.
+For ``jax-community`` images, see `rocm/jax-community
+<https://hub.docker.com/r/rocm/jax-community/tags>`__ on Docker Hub.

-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax>`_
-with ROCm backends on Docker Hub. The following Docker image tags and
-associated inventories represent the latest JAX version from the official Docker Hub and are validated for
-`ROCm 6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`_. Click the |docker-icon|
-icon to view the image on Docker Hub.
-
-.. list-table:: JAX Docker image components
-    :header-rows: 1
-
-    * - Docker image
-      - JAX
-      - Linux
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/jax/rocm6.4.2-jax0.4.35-py3.12/images/sha256-8918fa806a172c1a10eb2f57131eb31b5d7c8fa1656b8729fe7d3d736112de83"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
-
-      - `0.4.35 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.4.35>`_
-      - Ubuntu 24.04
-      - `3.12.10 <https://www.python.org/downloads/release/python-31210/>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/jax/rocm6.4.2-jax0.4.35-py3.10/images/sha256-a394be13c67b7fc602216abee51233afd4b6cb7adaa57ca97e688fba82f9ad79"><i class="fab fa-docker fa-lg"></i> rocm/jax</a>
-
-      - `0.4.35 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.4.35>`_
-      - Ubuntu 22.04
-      - `3.10.17 <https://www.python.org/downloads/release/python-31017/>`_
-
-AMD publishes `Community ROCm JAX Docker images <https://hub.docker.com/r/rocm/jax-community>`_
-with ROCm backends on Docker Hub. The following Docker image tags and
-associated inventories are tested for `ROCm 6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_.
-
-.. list-table:: JAX community Docker image components
-    :header-rows: 1
-
-    * - Docker image
-      - JAX
-      - Linux
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/jax-community/rocm6.3.2-jax0.5.0-py3.12.8/images/sha256-25dfaa0183e274bd0a3554a309af3249c6f16a1793226cb5373f418e39d3146a"><i class="fab fa-docker fa-lg"></i> rocm/jax-community</a>
-
-      - `0.5.0 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.5.0>`_
-      - Ubuntu 22.04
-      - `3.12.8 <https://www.python.org/downloads/release/python-3128/>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/jax-community/rocm6.3.2-jax0.5.0-py3.11.11/images/sha256-ff9baeca9067d13e6c279c911e5a9e5beed0817d24fafd424367cc3d5bd381d7"><i class="fab fa-docker fa-lg"></i> rocm/jax-community</a>
-
-      - `0.5.0 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.5.0>`_
-      - Ubuntu 22.04
-      - `3.11.11 <https://www.python.org/downloads/release/python-31111/>`_
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/jax-community/rocm6.3.2-jax0.5.0-py3.10.16/images/sha256-8bab484be1713655f74da51a191ed824bb9d03db1104fd63530a1ac3c37cf7b1"><i class="fab fa-docker fa-lg"></i> rocm/jax-community</a>
-
-      - `0.5.0 <https://github.com/ROCm/jax/releases/tag/rocm-jax-v0.5.0>`_
-      - Ubuntu 22.04
-      - `3.10.16 <https://www.python.org/downloads/release/python-31016/>`_
+To find the right image tag, see the :ref:`JAX on ROCm installation
+documentation <rocm-install-on-linux:jax-docker-support>` for a list of
+available ``rocm/jax`` images.

 .. _key_rocm_libraries:

--- a/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/llama-cpp-compatibility.rst
@@ -16,7 +16,7 @@ for Large Language Model (LLM) inference that runs on both central processing un
 a simple, dependency-free setup. 

 The framework supports multiple quantization options, from 1.5-bit to 8-bit integers, 
-to speed up inference and reduce memory usage. Originally built as a CPU-first library, 
+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. 

@@ -40,12 +40,12 @@ with ROCm support:

 .. note::

-  llama.cpp is supported on ROCm 6.4.0.
+  llama.cpp is supported on ROCm 7.0.0 and ROCm 6.4.x.

 Supported devices
 ================================================================================

-**Officially Supported**: AMD Instinct™ MI300X, MI210
+**Officially Supported**: AMD Instinct™ MI300X, MI325X, MI210


 Use cases and recommendations
@@ -70,7 +70,7 @@ llama.cpp is also used in a range of real-world applications, including:
 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>`__, 
+- 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. 
@@ -84,9 +84,9 @@ Docker image compatibility

   <i class="fab fa-docker"></i>

-AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp>`__
+AMD validates and publishes `ROCm llama.cpp Docker images <https://hub.docker.com/r/rocm/llama.cpp/tags>`__
 with ROCm backends on Docker Hub. The following Docker image tags and associated
-inventories were tested on `ROCm 6.4.0 <https://repo.radeon.com/rocm/apt/6.4/>`__.
+inventories represent the available llama.cpp versions from the official Docker Hub.
 Click |docker-icon| to view the image on Docker Hub.

 .. important::
@@ -105,8 +105,115 @@ Click |docker-icon| to view the image on Docker Hub.
      - Server Docker
      - Light Docker
      - llama.cpp
+      - ROCm
      - Ubuntu

+    * - .. raw:: html
+
+           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu24.04_full/images/sha256-a2ecd635eaa65bb289a9041330128677f3ae88bee6fee0597424b17e38d4903c"><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_rocm7.0.0_ubuntu24.04_server/images/sha256-cb46b47df415addb5ceb6e6fdf0be70bf9d7f6863bbe6e10c2441ecb84246d52"><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_rocm7.0.0_ubuntu24.04_light/images/sha256-8f8536eec4b05c0ff1c022f9fc6c527ad1c89e6c1ca0906e4d39e4de73edbde9"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
+      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
+      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
+      - 24.04
+
+    * - .. raw:: html
+
+           <a href="https://hub.docker.com/layers/rocm/llama.cpp/llama.cpp-b6356_rocm7.0.0_ubuntu22.04_full/images/sha256-f36de2a3b03ae53e81c85422cb3780368c9891e1ac7884b04403a921fe2ea45d"><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_rocm7.0.0_ubuntu22.04_server/images/sha256-df15e8ab11a6837cd3736644fec1e047465d49e37d610ab0b79df000371327df"><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_rocm7.0.0_ubuntu22.04_light/images/sha256-4ea2d5bb7964f0ee3ea9b30ba7f343edd6ddfab1b1037669ca7eafad2e3c2bd7"><i class="fab fa-docker fa-lg"></i> rocm/llama.cpp</a>
+      - `b6356 <https://github.com/ROCm/llama.cpp/tree/release/b6356>`__
+      - `7.0.0 <https://repo.radeon.com/rocm/apt/7.0/>`__
+      - 22.04
+
+    * - .. 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
+
+    * - .. 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
+
+
+    * - .. 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
+
+    * - .. 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
+
+
+    * - .. 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
+
+    * - .. 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
+
    * - .. 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>
@@ -117,40 +224,52 @@ Click |docker-icon| to view the image on Docker Hub.

           <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

+
 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.
+feature set available to developers. Ensure you have the required libraries for 
+your corresponding ROCm version.

 .. list-table::
    :header-rows: 1

    * - ROCm library
-      - Version
+      - ROCm 7.0.0 version
+      - ROCm 6.4.x version
      - Purpose
      - Usage
    * - `hipBLAS <https://github.com/ROCm/hipBLAS>`__
-      - :version-ref:`hipBLAS rocm_version`
+      - 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>`__
-      - :version-ref:`hipBLASLt rocm_version`
+      - 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>`__
-      - :version-ref:`rocWMMA rocm_version`
+      - 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.
+        the flag during compile time.
+
+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
@@ -28,7 +28,7 @@ Supported devices
 ================================================================================

 - **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
+- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210

 Supported models and features
 ================================================================================
--- a/docs/compatibility/ml-compatibility/pytorch-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/pytorch-compatibility.rst
@@ -89,141 +89,13 @@ For more use cases and recommendations, see `ROCm PyTorch blog posts <https://ro
 Docker image compatibility
 ================================================================================

-.. |docker-icon| raw:: html
+AMD provides preconfigured Docker images with PyTorch and the ROCm backend.
+These images are published on `Docker Hub <https://hub.docker.com/r/rocm/pytorch>`__ and are the
+recommended way to get started with deep learning with PyTorch on ROCm.

-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes `PyTorch images <https://hub.docker.com/r/rocm/pytorch>`__
-with ROCm backends on Docker Hub. The following Docker image tags and associated
-inventories were tested on `ROCm 6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__.
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: PyTorch Docker image components
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker
-      - PyTorch
-      - Ubuntu
-      - Python
-      - Apex
-      - torchvision
-      - TensorBoard
-      - MAGMA
-      - UCX
-      - OMPI
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.6.0/images/sha256-6a287591500b4048a9556c1ecc92bc411fd3d552f6c8233bc399f18eb803e8d6"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
-      - 24.04
-      - `3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `1.6.0 <https://github.com/ROCm/apex/tree/release/1.6.0>`__
-      - `0.21.0 <https://github.com/pytorch/vision/tree/v0.21.0>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`__
-      - `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.10_pytorch_release_2.6.0/images/sha256-06b967629ba6657709f04169832cd769a11e6b491e8b1394c361d42d7a0c8b43"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.6.0 <https://github.com/ROCm/pytorch/tree/release/2.6>`__
-      - 22.04
-      - `3.10 <https://www.python.org/downloads/release/python-31017/>`__
-      - `1.6.0 <https://github.com/ROCm/apex/tree/release/1.6.0>`__
-      - `0.21.0 <https://github.com/pytorch/vision/tree/v0.21.0>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
-      - `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.5.1/images/sha256-62022414217ef6de33ac5b1341e57db8a48e8573fa2ace12d48aa5edd4b99ef0"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
-      - 24.04
-      - `3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`__
-      - `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.10.0>`__
-      - `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.11_pytorch_release_2.5.1/images/sha256-469a7f74fc149aff31797e011ee41978f6a190adc69fa423b3c6a718a77bd985"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
-      - 22.04
-      - `3.11 <https://www.python.org/downloads/release/python-31113/>`__
-      - `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`__
-      - `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
-      - `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.10_pytorch_release_2.5.1/images/sha256-37f41a1cd94019688669a1b20d33ea74156e0c129ef6b8270076ef214a6a1a2c"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.5.1 <https://github.com/ROCm/pytorch/tree/release/2.5>`__
-      - 22.04
-      - `3.10 <https://www.python.org/downloads/release/python-31017/>`__
-      - `1.5.0 <https://github.com/ROCm/apex/tree/release/1.5.0>`__
-      - `0.20.1 <https://github.com/pytorch/vision/tree/v0.20.1>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
-      - `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.4.1/images/sha256-60824ba83dc1b9d94164925af1f81c0235c105dd555091ec04c57e05177ead1b"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
-      - 24.04
-      - `3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`__
-      - `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`__
-      - `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu22.04_py3.10_pytorch_release_2.4.1/images/sha256-fe944fe083312f901be6891ab4d3ffebf2eaf2cf4f5f0f435ef0b76ec714fabd"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.4.1 <https://github.com/ROCm/pytorch/tree/release/2.4>`__
-      - 22.04
-      - `3.10 <https://www.python.org/downloads/release/python-31017/>`__
-      - `1.4.0 <https://github.com/ROCm/apex/tree/release/1.4.0>`__
-      - `0.19.0 <https://github.com/pytorch/vision/tree/v0.19.0>`__
-      - `2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.12.1~rc2-1 <https://github.com/openucx/ucx/tree/v1.12.1>`__
-      - `4.1.2-2ubuntu1 <https://github.com/open-mpi/ompi/tree/v4.1.2>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/pytorch/rocm6.4.2_ubuntu24.04_py3.12_pytorch_release_2.3.0/images/sha256-1d59251c47170c5b8960d1172a4dbe52f5793d8966edd778f168eaf32d56661a"><i class="fab fa-docker fa-lg"></i></a>
-
-      - `2.3.0 <https://github.com/ROCm/pytorch/tree/release/2.3>`__
-      - 24.04
-      - `3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `1.3.0 <https://github.com/ROCm/apex/tree/release/1.3.0>`__
-      - `0.18.0 <https://github.com/pytorch/vision/tree/v0.18.0>`__
-      - `2.13.0 <https://github.com/tensorflow/tensorboard/tree/2.13>`__
-      - `master <https://bitbucket.org/icl/magma/src/master/>`__
-      - `1.16.0+ds-5ubuntu1 <https://github.com/openucx/ucx/tree/v1.16.0>`__
-      - `4.1.6-7ubuntu2 <https://github.com/open-mpi/ompi/tree/v4.1.6>`__
+To find the right image tag, see the :ref:`PyTorch on ROCm installation
+documentation <rocm-install-on-linux:pytorch-docker-support>` for a list of
+available ``rocm/pytorch`` images.

 Key ROCm libraries for PyTorch
 ================================================================================
@@ -570,10 +442,6 @@ Key features and enhancements for PyTorch 2.7 with ROCm 7.0
  ROCm-specific test conditions, and enhanced unit test coverage for Flash
  Attention and Memory Efficient operations.

- Build system and infrastructure improvements: Provides updated CentOS Stream 9
-  support, improved Docker configuration, migration to public MAGMA repository,
-  and enhanced QA automation scripts for PyTorch unit testing.
-
 - Composable Kernel (CK) updates: Features updated CK submodule integration with
  the latest optimizations and performance improvements for core mathematical
  operations.
--- a/docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/stanford-megatron-lm-compatibility.rst
@@ -27,7 +27,7 @@ Supported Devices
 ================================================================================

 - **Officially Supported**: AMD Instinct MI300X
- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210X
+- **Partially Supported** (functionality or performance limitations): AMD Instinct MI250X, MI210


 Supported models and features
--- a/docs/compatibility/ml-compatibility/taichi-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/taichi-compatibility.rst
@@ -1,76 +0,0 @@
-:orphan:
-
-.. meta::
-    :description: Taichi compatibility
-    :keywords: GPU, Taichi compatibility
-
-.. version-set:: rocm_version latest
-
-*******************************************************************************
-Taichi compatibility
-*******************************************************************************
-
-`Taichi <https://www.taichi-lang.org/>`_ is an open-source, imperative, and parallel 
-programming language designed for high-performance numerical computation. 
-Embedded in Python, it leverages just-in-time (JIT) compilation frameworks such as LLVM to accelerate 
-compute-intensive Python code by compiling it to native GPU or CPU instructions.
-
-Taichi is widely used across various domains, including real-time physical simulation, 
-numerical computing, augmented reality, artificial intelligence, computer vision, robotics, 
-visual effects in film and gaming, and general-purpose computing.
-
-* ROCm support for Taichi is hosted in the official `https://github.com/ROCm/taichi <https://github.com/ROCm/taichi>`_ repository.
-* Due to independent compatibility considerations, this location differs from the `https://github.com/taichi-dev <https://github.com/taichi-dev>`_ upstream repository.
-* Use the prebuilt :ref:`Docker image <taichi-docker-compat>` with ROCm, PyTorch, and Taichi preinstalled.
-* See the :doc:`ROCm Taichi installation guide <rocm-install-on-linux:install/3rd-party/taichi-install>` to install and get started.
-
-.. note::
-
-	Taichi is supported on ROCm 6.3.2.
-
-Supported devices and features
-===============================================================================
-There is support through the ROCm software stack for all Taichi GPU features on AMD Instinct MI250X and MI210X series GPUs with the exception of Taichi’s GPU rendering system, CGUI.
-AMD Instinct MI300X series GPUs will be supported by November.
-
-.. _taichi-recommendations:
-
-Use cases and recommendations
-================================================================================
-To fully leverage Taichi's performance capabilities in compute-intensive tasks, it is best to adhere to specific coding patterns and utilize Taichi decorators. 
-A collection of example use cases is available in the `https://github.com/ROCm/taichi_examples <https://github.com/ROCm/taichi_examples>`_ repository, 
-providing practical insights and foundational knowledge for working with the Taichi programming language. 
-You can also refer to the `AMD ROCm blog <https://rocm.blogs.amd.com/>`_ to search for Taichi examples and best practices to optimize your workflows on AMD GPUs.
-
-.. _taichi-docker-compat:
-
-Docker image compatibility
-================================================================================
-
-.. |docker-icon| raw:: html
-
-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `ROCm Taichi Docker images <https://hub.docker.com/r/rocm/taichi/tags>`_
-with ROCm backends on Docker Hub. The following Docker image tags and associated inventories 
-represent the latest Taichi version from the official Docker Hub.
-The Docker images have been validated for `ROCm 6.3.2 <https://rocm.docs.amd.com/en/docs-6.3.2/about/release-notes.html>`_. 
-Click |docker-icon| to view the image on Docker Hub.
-
-.. list-table:: 
-    :header-rows: 1
-    :class: docker-image-compatibility
-
-    * - Docker image
-      - ROCm
-      - Taichi
-      - Ubuntu
-      - Python
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/taichi/taichi-1.8.0b1_rocm6.3.2_ubuntu22.04_py3.10.12/images/sha256-e016964a751e6a92199032d23e70fa3a564fff8555afe85cd718f8aa63f11fc6"><i class="fab fa-docker fa-lg"></i> rocm/taichi</a>
-      - `6.3.2 <https://repo.radeon.com/rocm/apt/6.3.2/>`_
-      - `1.8.0b1 <https://github.com/taichi-dev/taichi>`_
-      - 22.04
-      - `3.10.12 <https://www.python.org/downloads/release/python-31012/>`_
--- a/docs/compatibility/ml-compatibility/tensorflow-compatibility.rst
+++ b/docs/compatibility/ml-compatibility/tensorflow-compatibility.rst
@@ -47,80 +47,15 @@ fixes, updates, and support for the latest ROCM versions.
 .. _tensorflow-docker-compat:

 Docker image compatibility
-===============================================================================
+================================================================================

-.. |docker-icon| raw:: html
+AMD provides preconfigured Docker images with TensorFlow and the ROCm backend.
+These images are published on `Docker Hub <https://hub.docker.com/r/rocm/tensorflow>`__ and are the
+recommended way to get started with deep learning with TensorFlow on ROCm.

-   <i class="fab fa-docker"></i>
-
-AMD validates and publishes ready-made `TensorFlow images
-<https://hub.docker.com/r/rocm/tensorflow>`__ with ROCm backends on
-Docker Hub. The following Docker image tags and associated inventories are
-validated for `ROCm 6.4.2 <https://repo.radeon.com/rocm/apt/6.4.2/>`__. Click
-the |docker-icon| icon to view the image on Docker Hub.
-
-.. list-table:: TensorFlow Docker image components
-    :header-rows: 1
-
-    * - Docker image
-      - TensorFlow
-      - Ubuntu
-      - Python
-      - TensorBoard
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.18-dev/images/sha256-96754ce2d30f729e19b497279915b5212ba33d5e408e7e5dd3f2304d87e3441e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.18.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - 24.04
-      - `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.18-dev/images/sha256-fa741508d383858e86985a9efac85174529127408102558ae2e3a4ac894eea1e"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.18.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.18.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
-      - 22.04
-      - `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
-      - `TensorBoard 2.18.0 <https://github.com/tensorflow/tensorboard/tree/2.18.0>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.17-dev/images/sha256-3a0aef09f2a8833c2b64b85874dd9449ffc2ad257351857338ff5b706c03a418"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.17.1-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - 24.04
-      - `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.17-dev/images/sha256-bc7341a41ebe7ab261aa100732874507c452421ef733e408ac4f05ed453b0bc5"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.17.1 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.17.1-cp310-cp310-manylinux_2_28_x86_64.whl>`__
-      - 22.04
-      - `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
-      - `TensorBoard 2.17.1 <https://github.com/tensorflow/tensorboard/tree/2.17.1>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.12-tf2.16-dev/images/sha256-4841a8df7c340dab79bf9362dad687797649a00d594e0832eb83ea6880a40d3b"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.16.2-cp312-cp312-manylinux_2_28_x86_64.whl>`__
-      - 24.04
-      - `Python 3.12 <https://www.python.org/downloads/release/python-31210/>`__
-      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
-
-    * - .. raw:: html
-
-           <a href="https://hub.docker.com/layers/rocm/tensorflow/rocm6.4.2-py3.10-tf2.16-dev/images/sha256-883fa95aba960c58a3e46fceaa18f03ede2c7df89b8e9fd603ab2d47e0852897"><i class="fab fa-docker fa-lg"></i> rocm/tensorflow</a>
-
-      - `tensorflow-rocm 2.16.2 <https://repo.radeon.com/rocm/manylinux/rocm-rel-6.4.2/tensorflow_rocm-2.16.2-cp310-cp310-manylinux_2_28_x86_64.whl>`__
-      - 22.04
-      - `Python 3.10 <https://www.python.org/downloads/release/python-31017/>`__
-      - `TensorBoard 2.16.2 <https://github.com/tensorflow/tensorboard/tree/2.16.2>`__
+To find the right image tag, see the :ref:`TensorFlow on ROCm installation
+documentation <rocm-install-on-linux:tensorflow-docker-support>` for a list of
+available ``rocm/tensorflow`` images.


 Critical ROCm libraries for TensorFlow
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -107,9 +107,9 @@ article_pages = [
    {"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/taichi-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"]},
@@ -135,6 +135,9 @@ article_pages = [
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.4", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.5", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.6", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch", "os": ["linux"]},
+    {"file": "how-to/rocm-for-ai/training/benchmark-docker/pytorch-training", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-history", "os": ["linux"]},
    {"file": "how-to/rocm-for-ai/training/benchmark-docker/previous-versions/jax-maxtext-v25.4", "os": ["linux"]},
--- a/docs/data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
@@ -0,0 +1,32 @@
+dockers:
+  - pull_tag: lmsysorg/sglang:v0.5.2rc1-rocm700-mi30x
+    docker_hub_url: https://hub.docker.com/layers/lmsysorg/sglang/v0.5.2rc1-rocm700-mi30x/images/sha256-10c4ee502ddba44dd8c13325e6e03868bfe7f43d23d0a44780a8ee8b393f4729
+    components:
+      ROCm: 7.0.0
+      SGLang: v0.5.2rc1
+      pytorch-triton-rocm: 3.4.0+rocm7.0.0.gitf9e5bf54
+model_groups:
+  - group: Dense models
+    tag: dense-models
+    models:
+      - model: Llama 3.1 8B Instruct
+        model_repo: Llama-3.1-8B-Instruct
+        url: https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
+      - model: Llama 3.1 405B FP8 KV
+        model_repo: Llama-3.1-405B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.1-405B-Instruct-FP8-KV
+      - model: Llama 3.3 70B FP8 KV
+        model_repo: amd-Llama-3.3-70B-Instruct-FP8-KV
+        url: https://huggingface.co/amd/Llama-3.3-70B-Instruct-FP8-KV
+      - model: Qwen3 32B
+        model_repo: Qwen3-32B
+        url: https://huggingface.co/Qwen/Qwen3-32B
+  - group: Small experts models
+    tag: small-experts-models
+    models:
+      - model: DeepSeek V3
+        model_repo: DeepSeek-V3
+        url: https://huggingface.co/deepseek-ai/DeepSeek-V3
+      - model: Mixtral 8x7B v0.1
+        model_repo: Mixtral-8x7B-v0.1
+        url: https://huggingface.co/mistralai/Mixtral-8x7B-v0.1
--- a/docs/data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
+++ b/docs/data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
@@ -0,0 +1,162 @@
+dockers:
+  - pull_tag: rocm/pytorch-training:v25.7
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
+    components:
+      ROCm: 6.4.2
+      PyTorch: 2.8.0a0+gitd06a406
+      Python: 3.10.18
+      Transformer Engine: 2.2.0.dev0+94e53dd8
+      Flash Attention: 3.0.0.post1
+      hipBLASLt: 1.1.0-4b9a52edfc
+      Triton: 3.3.0
+model_groups:
+  - group: Meta Llama
+    tag: llama
+    models:
+    - model: Llama 4 Scout 17B-16E
+      mad_tag: pyt_train_llama-4-scout-17b-16e
+      model_repo: Llama-4-17B_16E
+      url: https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.3 70B
+      mad_tag: pyt_train_llama-3.3-70b
+      model_repo: Llama-3.3-70B
+      url: https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 3.2 1B
+      mad_tag: pyt_train_llama-3.2-1b
+      model_repo: Llama-3.2-1B
+      url: https://huggingface.co/meta-llama/Llama-3.2-1B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 3B
+      mad_tag: pyt_train_llama-3.2-3b
+      model_repo: Llama-3.2-3B
+      url: https://huggingface.co/meta-llama/Llama-3.2-3B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3.2 Vision 11B
+      mad_tag: pyt_train_llama-3.2-vision-11b
+      model_repo: Llama-3.2-Vision-11B
+      url: https://huggingface.co/meta-llama/Llama-3.2-11B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.2 Vision 90B
+      mad_tag: pyt_train_llama-3.2-vision-90b
+      model_repo: Llama-3.2-Vision-90B
+      url: https://huggingface.co/meta-llama/Llama-3.2-90B-Vision
+      precision: BF16
+      training_modes: [finetune_fw]
+    - model: Llama 3.1 8B
+      mad_tag: pyt_train_llama-3.1-8b
+      model_repo: Llama-3.1-8B
+      url: https://huggingface.co/meta-llama/Llama-3.1-8B
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora, HF_pretrain]
+    - model: Llama 3.1 70B
+      mad_tag: pyt_train_llama-3.1-70b
+      model_repo: Llama-3.1-70B
+      url: https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct
+      precision: BF16
+      training_modes: [pretrain, finetune_fw, finetune_lora]
+    - model: Llama 3.1 405B
+      mad_tag: pyt_train_llama-3.1-405b
+      model_repo: Llama-3.1-405B
+      url: https://huggingface.co/meta-llama/Llama-3.1-405B
+      precision: BF16
+      training_modes: [finetune_qlora]
+    - model: Llama 3 8B
+      mad_tag: pyt_train_llama-3-8b
+      model_repo: Llama-3-8B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 3 70B
+      mad_tag: pyt_train_llama-3-70b
+      model_repo: Llama-3-70B
+      url: https://huggingface.co/meta-llama/Meta-Llama-3-70B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 7B
+      mad_tag: pyt_train_llama-2-7b
+      model_repo: Llama-2-7B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora, finetune_qlora]
+    - model: Llama 2 13B
+      mad_tag: pyt_train_llama-2-13b
+      model_repo: Llama-2-13B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Llama 2 70B
+      mad_tag: pyt_train_llama-2-70b
+      model_repo: Llama-2-70B
+      url: https://github.com/meta-llama/llama-models/tree/main/models/llama2
+      precision: BF16
+      training_modes: [finetune_lora, finetune_qlora]
+  - group: OpenAI
+    tag: openai
+    models:
+    - model: GPT OSS 20B
+      mad_tag: pyt_train_gpt_oss_20b
+      model_repo: GPT-OSS-20B
+      url: https://huggingface.co/openai/gpt-oss-20b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+    - model: GPT OSS 120B
+      mad_tag: pyt_train_gpt_oss_120b
+      model_repo: GPT-OSS-120B
+      url: https://huggingface.co/openai/gpt-oss-120b
+      precision: BF16
+      training_modes: [HF_finetune_lora]
+  - group: Qwen
+    tag: qwen
+    models:
+    - model: Qwen 3 8B
+      mad_tag: pyt_train_qwen3-8b
+      model_repo: Qwen3-8B
+      url: https://huggingface.co/Qwen/Qwen3-8B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 3 32B
+      mad_tag: pyt_train_qwen3-32b
+      model_repo: Qwen3-32
+      url: https://huggingface.co/Qwen/Qwen3-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 32B
+      mad_tag: pyt_train_qwen2.5-32b
+      model_repo: Qwen2.5-32B
+      url: https://huggingface.co/Qwen/Qwen2.5-32B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2.5 72B
+      mad_tag: pyt_train_qwen2.5-72b
+      model_repo: Qwen2.5-72B
+      url: https://huggingface.co/Qwen/Qwen2.5-72B
+      precision: BF16
+      training_modes: [finetune_lora]
+    - model: Qwen 2 1.5B
+      mad_tag: pyt_train_qwen2-1.5b
+      model_repo: Qwen2-1.5B
+      url: https://huggingface.co/Qwen/Qwen2-1.5B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+    - model: Qwen 2 7B
+      mad_tag: pyt_train_qwen2-7b
+      model_repo: Qwen2-7B
+      url: https://huggingface.co/Qwen/Qwen2-7B
+      precision: BF16
+      training_modes: [finetune_fw, finetune_lora]
+  - group: Flux
+    tag: flux
+    models:
+    - model: FLUX.1-dev
+      mad_tag: pyt_train_flux
+      model_repo: Flux
+      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
+      precision: BF16
+      training_modes: [pretrain]
--- 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
@@ -0,0 +1,24 @@
+dockers:
+  - pull_tag: rocm/pytorch-training:v25.8
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.8/images/sha256-5082ae01d73fec6972b0d84e5dad78c0926820dcf3c19f301d6c8eb892e573c5
+    components:
+      ROCm: 6.4.3
+      PyTorch: 2.8.0a0+gitd06a406
+      Python: 3.10.18
+      Transformer Engine: 2.2.0.dev0+a1e66aae
+      Flash Attention: 3.0.0.post1
+      hipBLASLt: 1.1.0-d1b517fc7a
+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
--- 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
@@ -1,14 +1,13 @@
 dockers:
-  - pull_tag: rocm/pytorch-training:v25.7
-    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712
+  - pull_tag: rocm/pytorch-training:v25.8
+    docker_hub_url: https://hub.docker.com/layers/rocm/pytorch-training/v25.8/images/sha256-5082ae01d73fec6972b0d84e5dad78c0926820dcf3c19f301d6c8eb892e573c5
    components:
-      ROCm: 6.4.2
+      ROCm: 6.4.3
      PyTorch: 2.8.0a0+gitd06a406
      Python: 3.10.18
-      Transformer Engine: 2.2.0.dev0+94e53dd8
+      Transformer Engine: 2.2.0.dev0+a1e66aae
      Flash Attention: 3.0.0.post1
-      hipBLASLt: 1.1.0-4b9a52edfc
-      Triton: 3.3.0
+      hipBLASLt: 1.1.0-d1b517fc7a
 model_groups:
  - group: Meta Llama
    tag: llama
@@ -151,6 +150,15 @@ model_groups:
      url: https://huggingface.co/Qwen/Qwen2-7B
      precision: BF16
      training_modes: [finetune_fw, finetune_lora]
+  - group: Stable Diffusion
+    tag: sd
+    models:
+    - model: Stable Diffusion XL
+      mad_tag: pyt_huggingface_stable_diffusion_xl_2k_lora_finetuning
+      model_repo: SDXL
+      url: https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0
+      precision: BF16
+      training_modes: [finetune_lora]
  - group: Flux
    tag: flux
    models:
@@ -160,3 +168,11 @@ model_groups:
      url: https://huggingface.co/black-forest-labs/FLUX.1-dev
      precision: BF16
      training_modes: [pretrain]
+  - group: NCF
+    tag: ncf
+    models:
+    - model: NCF
+      mad_tag: pyt_ncf_training
+      model_repo:
+      url: https://github.com/NVIDIA/DeepLearningExamples/tree/master/PyTorch/Recommendation/NCF
+      precision: FP32
--- a/docs/data/reference/precision-support/precision-support.yaml
+++ b/docs/data/reference/precision-support/precision-support.yaml
@@ -32,7 +32,7 @@ library_groups:

      - name: "MIGraphX"
        tag: "migraphx"
-        doc_link: "amdmigraphx:reference/cpp"
+        doc_link: "amdmigraphx:reference/MIGraphX-cpp"
        data_types:
          - type: "int8"
            support: "⚠️"
@@ -290,7 +290,7 @@ library_groups:

      - name: "Tensile"
        tag: "tensile"
-        doc_link: "tensile:reference/precision-support"
+        doc_link: "tensile:src/reference/precision-support"
        data_types:
          - type: "int8"
            support: "✅"
--- a/docs/how-to/deep-learning-rocm.rst
+++ b/docs/how-to/deep-learning-rocm.rst
@@ -98,18 +98,6 @@ The table below summarizes information about ROCm-enabled deep learning framewor

          <a href="https://github.com/ROCm/megablocks"><i class="fab fa-github fa-lg"></i></a>

-    * - `Taichi <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/taichi-compatibility.html>`__
-      - .. raw:: html
-
-          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html"><i class="fas fa-link fa-lg"></i></a>
-      - 
-        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-prebuilt-docker-image-with-taichi-pre-installed>`__
-        - `Wheels package <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/taichi-install.html#use-a-wheels-package>`__
-
-      - .. raw:: html
-
-          <a href="https://github.com/ROCm/taichi"><i class="fab fa-github fa-lg"></i></a>
-
    * - `Ray <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/ray-compatibility.html>`__
      - .. raw:: html

@@ -128,10 +116,22 @@ The table below summarizes information about ROCm-enabled deep learning framewor
          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html"><i class="fas fa-link fa-lg"></i></a>
      - 
        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html#use-a-prebuilt-docker-image-with-llama-cpp-pre-installed>`__
+        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/llama-cpp-install.html#build-your-own-docker-image>`__
      - .. raw:: html

          <a href="https://github.com/ROCm/llama.cpp"><i class="fab fa-github fa-lg"></i></a>

+    * - `FlashInfer <https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/flashinfer-compatibility.html>`__
+      - .. raw:: html
+
+          <a href="https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html"><i class="fas fa-link fa-lg"></i></a>
+      - 
+        - `Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html#use-a-prebuilt-docker-image-with-flashinfer-pre-installed>`__
+        - `ROCm Base Docker image <https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/3rd-party/flashinfer-install.html#build-your-own-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/sglang-distributed.rst
+++ b/docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
@@ -0,0 +1,257 @@
+.. meta::
+   :description: SGLang multi-node disaggregated distributed inference using Mooncake
+   :keywords: model, sglang, mooncake, disagg, disaggregated, distributed, multi-node, docker
+
+******************************************
+SGLang distributed inference with Mooncake
+******************************************
+
+As LLM inference increasingly demands handling massive models and dynamic workloads, efficient
+distributed inference becomes essential. Traditional co-located architectures face bottlenecks due
+to tightly coupled memory and compute resources, which limits scalability and flexibility.
+Disaggregated inference refers to the process of splitting the inference of LLMs into distinct
+phases. This architecture, facilitated by libraries like Mooncake, uses high-bandwidth
+RDMA to transfer the Key-Value (KV) cache between prefill and decode nodes.
+This allows for independent resource scaling and optimization, resulting in
+improved efficiency and throughput.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
+
+   {% set docker = data.dockers[0] %}
+
+   `SGLang <https://docs.sglang.ai>`__ is a high-performance inference and
+   serving engine for large language models (LLMs) and vision models. The
+   ROCm-enabled `SGLang base Docker image <{{ docker.docker_hub_url }}>`__
+   bundles SGLang with PyTorch, which is optimized for AMD Instinct MI300X series
+   accelerators. It includes the following software components:
+
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      {% for component_name, component_version in docker.components.items() %}
+      * - {{ component_name }}
+        - {{ component_version }}
+      {% endfor %}
+
+The following guides on setting up and running SGLang and Mooncake for disaggregated
+distributed inference on a Slurm cluster using AMD Instinct MI300X series accelerators backed by
+Mellanox CX-7 NICs.
+
+Prerequisites
+=============
+
+Before starting, ensure you have:
+
+* A Slurm cluster with at least three nodes: one for the proxy, one for prefill (``xP``), and one for decode (``yD``).
+
+  ``Nodes -> xP + yD + 1``
+
+* A Dockerized environment with SGLang, Mooncake, etcd, and NIC drivers built in. See :ref:`sglang-disagg-inf-build-docker-image` for instructions.
+
+* A shared filesystem for storing models, scripts, and logs (cluster-specific).
+
+Supported models
+================
+
+The following models are supported for SGLang disaggregated prefill/decode
+inference. Some instructions, commands, and recommendations in this
+documentation might vary by selected model.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-distributed-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 type</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">Model</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.model_repo | lower }}" 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.model_repo | lower }}" data-param-group="{{ model_group.tag }}" tabindex="0">{{ model.model }}</div>
+            {% endif %}
+         {% endfor %}
+      {% endfor %}
+            </div>
+         </div>
+      </div>
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.model_repo }}
+
+      .. note::
+
+         See the `{{ model.model }} model card on Hugging Face <{{ model.url }}>`__ to learn more about this model.
+         Some models require access authorization prior to use through an external license agreement with a third party.
+
+      {% endfor %}
+   {% endfor %}
+
+.. _sglang-disagg-inf-build-docker-image:
+
+Build the Docker image
+----------------------
+
+Get the Dockerfile located in
+`<https://github.com/ROCm/MAD/blob/develop/docker/sglang_dissag_inference.ubuntu.amd.Dockerfile>`__.
+It uses `lmsysorg/sglang:v0.5.2rc1-rocm700-mi30x
+<https://hub.docker.com/layers/lmsysorg/sglang/v0.4.9.post1-rocm630/images/sha256-2f6b1748e4bcc70717875a7da76c87795fd8aa46a9646e08d38aa7232fc78538>`__
+as the base Docker image and installs the necessary components for Mooncake, etcd, and Mellanox network
+drivers.
+
+.. code-block:: shell
+
+   git clone https://github.com/ROCm/MAD.git
+   cd MAD/docker
+   docker build \
+       -t sglang_disagg_pd_image \
+       -f sglang_disagg_inference.ubuntu.amd.Dockerfile .
+
+Benchmarking
+============
+
+The `<https://github.com/ROCm/MAD/tree/develop/scripts/sglang_dissag>`__
+repository contains scripts to launch SGLang inference with prefill/decode
+disaggregation via Mooncake for supported models.
+
+* `scripts/sglang_dissag/run_xPyD_models.slurm <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/run_xPyD_models.slurm>`__
+  -- the main Slurm batch script to launch Docker containers on all nodes using ``sbatch`` or ``salloc``.
+
+* `scripts/sglang_dissag/sglang_disagg_server.sh <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/sglang_disagg_server.sh>`__
+  -- the entrypoint script that runs inside each container to start the correct service -- proxy, prefill, or decode.
+
+* `scripts/sglang_dissag/benchmark_xPyD.sh <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/benchmark_xPyD.sh>`__
+  -- the benchmark script to run the GSM8K accuracy benchmark and the SGLang benchmarking tool for performance measurement.
+
+* `scripts/sglang_dissag/benchmark_parser.py <https://github.com/ROCm/MAD/blob/develop/scripts/sglang_disagg/benchmark_parser.py>`__
+  -- the log parser script to be run on the concurrency benchmark log file to generate tabulated data.
+
+Launch the service
+------------------
+
+The service is deployed using a Slurm batch script that orchestrates the containers across the
+allocated nodes.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/inference/sglang-distributed-benchmark-models.yaml
+
+   {% set model_groups = data.model_groups %}
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+   .. container:: model-doc {{ model.model_repo }}
+
+      .. code-block:: shell
+
+         # Clone the MAD repo if you haven't already and
+         # navigate to the scripts directory
+         git clone https://github.com/ROCm/MAD.git
+         cd MAD/scripts/sglang_disagg/
+
+         # Slurm sbatch run command
+         export DOCKER_IMAGE_NAME=sglang_disagg_pd_image
+         export xP=<num_prefill_nodes>
+         export yD=<num_decode_nodes>
+         export MODEL_NAME={{ model.model_repo }}
+         # num_nodes = xP + yD + 1
+         sbatch -N <num_nodes> -n <num_nodes> --nodelist=<Nodes> run_xPyD_models.slurm
+
+      {% endfor %}
+   {% endfor %}
+
+Post-run logs and testing
+-------------------------
+
+Logs are stored in your shared filesystem in the directory specified by the ``LOG_PATH`` variable in the Slurm script.
+A new directory named after the Slurm job ID is created for each run.
+
+Inside that directory, you can access various logs:
+
+* ``pd_sglang_bench_serving.sh_NODE<...>.log`` -- the main log for each server node.
+
+* ``etcd_NODE<...>.log`` -- logs for etcd services.
+
+* ``prefill_NODE<...>.log`` -- logs for the prefill services.
+
+* ``decode_NODE<...>.log`` -- logs for the decode services.
+
+Use the benchmark parser script for concurrency logs to tabulate different data.
+
+.. code-block:: shell
+
+   python3 benchmark_parser.py <log_path/benchmark_XXX_CONCURRENCY.log>
+
+To verify the service is responsive, you can try sending a ``curl`` request to test the launched
+server from the Docker container on the proxy node. For example:
+
+.. code-block:: shell
+
+   curl -X POST http://127.0.0.1:30000/generate \
+       -H "Content-Type: application/json" \
+       -d '{ "text": "Let me tell you a story ", "sampling_params": { "temperature": 0.3 } }'
+
+Known issues
+============
+
+When running larger models, such as DeepSeek-V3 and Llama-3.1-405B-Instruct-FP8-KV, at
+higher concurrency levels (512+), the following error might occur:
+
+.. code-block:: shell-session
+
+   <TransferEncodingError: 400, message:
+    Not enough data to satisfy transfer length header.
+
+   The above exception was the direct cause of the following exception:
+
+   Traceback (most recent call last):
+   ...
+
+This leads to dropping requests and lower throughput.
+
+Further reading
+===============
+
+- To learn about Mooncake, see `Welcome to Mooncake <https://kvcache-ai.github.io/Mooncake/>`__.
+
+- To learn more about the options for latency and throughput benchmark scripts,
+  see `<https://github.com/sgl-project/sglang/tree/main/benchmark/blog_v0_2>`__.
+
+- See the base upstream Docker image on `Docker Hub <https://hub.docker.com/layers/lmsysorg/sglang/v0.5.2rc1-rocm700-mi30x/images/sha256-10c4ee502ddba44dd8c13325e6e03868bfe7f43d23d0a44780a8ee8b393f4729>`__.
+
+- To learn more about system settings and management practices to configure your system for
+  MI300X series accelerators, see `AMD Instinct MI300X system optimization <https://instinct.docs.amd.com/projects/amdgpu-docs/en/latest/system-optimization/mi300x.html>`__.
+
+- For application performance optimization strategies for HPC and AI workloads,
+  including inference with vLLM, see :doc:`/how-to/rocm-for-ai/inference-optimization/workload`.
+
+- To learn how to run community models from Hugging Face on AMD GPUs, see
+  :doc:`Running models from Hugging Face </how-to/rocm-for-ai/inference/hugging-face-models>`.
+
+- To learn how to fine-tune LLMs and optimize inference, see
+  :doc:`Fine-tuning LLMs and inference optimization </how-to/rocm-for-ai/fine-tuning/fine-tuning-and-inference>`.
+
+- 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/sglang-history` to find documentation for previous releases
+of SGLang 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
@@ -3,7 +3,7 @@
   :keywords: ROCm, AI, LLM, train, jax, torch, Llama, flux, tutorial, docker

 ******************************************
-Training a model with JAX MaxText for ROCm
+Training a model with JAX MaxText on ROCm
 ******************************************

 MaxText is a high-performance, open-source framework built on the Google JAX
@@ -406,8 +406,6 @@ benchmark results:
 Further reading
 ===============

- See the ROCm/maxtext benchmarking README at `<https://github.com/ROCm/maxtext/blob/main/benchmarks/gpu-rocm/readme.md>`__.
-
 - 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
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/megatron-lm.rst
@@ -5,15 +5,13 @@
   :keywords: ROCm, AI, LLM, train, Megatron-LM, megatron, Llama, tutorial, docker, torch

 ******************************************
-Training a model with Megatron-LM for ROCm
+Training a model with Megatron-LM on ROCm
 ******************************************

 .. caution::

-   The ROCm Megatron-LM framework now has limited support with this Docker
-   environment; it now focuses on Primus with Megatron-Core. See :doc:`primus-megatron`.
-
-   To learn how to migrate your existing workloads to Primus with Megatron-Core,
+   Primus with Megatron supersedes this ROCm Megatron-LM training workflow.
+   To learn how to migrate workloads from Megatron-LM to Primus with Megatron,
   see :doc:`previous-versions/megatron-lm-primus-migration-guide`.

 The `Megatron-LM framework for ROCm <https://github.com/ROCm/Megatron-LM>`_ is
@@ -807,9 +805,16 @@ Single node training
      AC=none \
      SEQ_LEN=4096 \
      PAD_LEN=4096 \
-      TRAIN_ITERS=50 \
+      TRAIN_ITERS=20 \
      bash examples/deepseek_v2/train_deepseekv2.sh

+   .. note::
+
+      Note that DeepSeek-V2-Lite is experiencing instability due to GPU memory access fault
+      for large iterations.
+      For stability, it's recommended to use Primus for this workload.
+      See :doc:`primus-megatron`.
+
 .. container:: model-doc pyt_megatron_lm_train_mixtral-8x7b

   To run training on a single node for Mixtral 8x7B (MoE with expert parallel),
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry.rst
@@ -3,7 +3,7 @@
   :keywords: ROCm, AI, LLM, train, PyTorch, torch, Llama, flux, tutorial, docker

 ******************************************
-Training MPT-30B with LLM Foundry and ROCm
+Training MPT-30B with LLM Foundry on ROCm
 ******************************************

 MPT-30B is a 30-billion parameter decoder-style transformer-based model from
--- 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
@@ -4,7 +4,7 @@
 PyTorch training performance testing version history
 ****************************************************

-This table lists previous versions of the ROCm Megatron-LM training Docker image for
+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/pytorch-training`` Docker image on `Docker Hub <https://hub.docker.com/r/rocm/pytorch-training/tags>`_.
@@ -16,12 +16,21 @@ previous releases of the ``ROCm/pytorch-training`` Docker image on `Docker Hub <
     - Components
     - Resources

+   * - v25.8 (latest)
+     - 
+       * ROCm 6.4.3
+       * PyTorch 2.8.0a0+gitd06a406
+     - 
+       * :doc:`Primus PyTorch Training documentation <../primus-pytorch>`
+       * :doc:`PyTorch training (legacy) documentation <../pytorch-training>`
+       * `Docker Hub <https://hub.docker.com/r/rocm/pytorch-training/tags>`__
+
   * - v25.7
     - 
       * ROCm 6.4.2
       * PyTorch 2.8.0a0+gitd06a406
     - 
-       * :doc:`Documentation <../pytorch-training>`
+       * :doc:`Documentation <pytorch-training-v25.7>`
       * `Docker Hub <https://hub.docker.com/layers/rocm/pytorch-training/v25.7/images/sha256-cc6fd840ab89cb81d926fc29eca6d075aee9875a55a522675a4b9231c9a0a712>`__

   * - v25.6
--- a/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/previous-versions/pytorch-training-v25.7.rst
@@ -0,0 +1,567 @@
+: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 PyTorch for ROCm
+**************************************
+
+.. caution::
+
+   This documentation does not reflect the latest version of ROCm vLLM
+   performance benchmark documentation. See :doc:`../pytorch-training` for the latest version.
+
+PyTorch is an open-source machine learning framework that is widely used for
+model training with GPU-optimized components for transformer-based models.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   {% set docker = dockers[0] %}
+   The `PyTorch for ROCm training Docker <{{ docker.docker_hub_url }}>`__
+   (``{{ docker.pull_tag }}``) image provides a prebuilt optimized environment for fine-tuning and pretraining a
+   model on AMD Instinct MI325X and MI300X accelerators. It includes the following software components to accelerate
+   training workloads:
+
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      {% for component_name, component_version in docker.components.items() %}
+      * - {{ component_name }}
+        - {{ component_version }}
+      {% endfor %}
+
+.. _amd-pytorch-training-model-support-v257:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
+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/pytorch-training-v25.7-benchmark-models.yaml
+
+   {% set unified_docker = data.dockers[0] %}
+   {% 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-3 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>
+
+
+   .. _amd-pytorch-training-supported-training-modes-v257:
+
+   The following table lists supported training modes per model.
+
+   .. dropdown:: Supported training modes
+
+      .. list-table::
+         :header-rows: 1
+
+         * - Model
+           - Supported training modes
+
+      {% for model_group in model_groups %}
+         {% set models = model_group.models %}
+         {% for model in models %}
+         * - {{ model.model }}
+           - ``{{ model.training_modes | join('``, ``') }}``
+
+         {% endfor %}
+      {% endfor %}
+
+      .. note::
+
+         Some model and fine-tuning combinations are not listed. This is
+         because the `upstream torchtune repository <https://github.com/pytorch/torchtune>`__
+         doesn't provide default YAML configurations for them.
+         For advanced usage, you can create a custom configuration to enable
+         unlisted fine-tuning methods by using an existing file in the
+         ``/workspace/torchtune/recipes/configs`` directory as a template.
+
+.. _amd-pytorch-training-performance-measurements-v257:
+
+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-a8deaeb413-item-21cea50186-tab>`_
+page provides reference throughput and latency measurements for training
+popular AI models.
+
+.. note::
+
+   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-a8deaeb413-item-21cea50186-tab>`_
+   should not be interpreted as the peak performance achievable by AMD
+   Instinct MI325X and MI300X accelerators 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.
+
+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.
+
+Run training
+============
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/previous-versions/pytorch-training-v25.7-benchmark-models.yaml
+
+   {% set unified_docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+
+   Once the setup is complete, choose between two options to start benchmarking training:
+
+   .. 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
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            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:: Standalone benchmarking
+
+         .. rubric:: Download the Docker image and required packages
+
+         1. Use the following command to pull the Docker image from Docker Hub.
+
+            .. code-block:: shell
+
+               docker pull {{ unified_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 $HOME/.ssh:/root/.ssh \
+                   --shm-size 64G \
+                   --name training_env \
+                   {{ unified_docker.pull_tag }}
+
+            Use these commands if you exit the ``training_env`` container and need to return to it.
+
+            .. code-block:: shell
+
+               docker start training_env
+               docker exec -it training_env bash
+
+         3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
+            repository and navigate to the benchmark scripts directory
+            ``/workspace/MAD/scripts/pytorch_train``.
+
+            .. code-block:: shell
+
+               git clone https://github.com/ROCm/MAD
+               cd MAD/scripts/pytorch_train
+
+         .. rubric:: Prepare training datasets and dependencies
+
+         1. The following benchmarking examples require downloading models and datasets
+            from Hugging Face. To ensure successful access to gated repos, set your
+            ``HF_TOKEN``.
+
+            .. code-block:: shell
+
+               export HF_TOKEN=$your_personal_hugging_face_access_token
+
+         2. Run the setup script to install libraries and datasets needed for benchmarking.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_setup.sh
+
+            .. container:: model-doc pyt_train_llama-3.1-8b
+
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 8B:
+
+               .. list-table::
+                  :header-rows: 1
+
+                  * - Library
+                    - Reference
+
+                  * - ``accelerate``
+                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+            .. container:: model-doc pyt_train_llama-3.1-70b
+
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for Llama 3.1 70B:
+
+               .. list-table::
+                  :header-rows: 1
+
+                  * - Library
+                    - Reference
+
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+                  * - ``torchdata``
+                    - `TorchData <https://pytorch.org/data/beta/index.html>`_
+
+                  * - ``tomli``
+                    - `Tomli <https://pypi.org/project/tomli/>`_
+
+                  * - ``tiktoken``
+                    - `tiktoken <https://github.com/openai/tiktoken>`_
+
+                  * - ``blobfile``
+                    - `blobfile <https://pypi.org/project/blobfile/>`_
+
+                  * - ``tabulate``
+                    - `tabulate <https://pypi.org/project/tabulate/>`_
+
+                  * - ``wandb``
+                    - `Weights & Biases <https://github.com/wandb/wandb>`_
+
+                  * - ``sentencepiece``
+                    - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+
+                  * - ``tensorboard``
+                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+
+            .. container:: model-doc pyt_train_flux
+
+               ``pytorch_benchmark_setup.sh`` installs the following libraries for FLUX:
+
+               .. list-table::
+                  :header-rows: 1
+
+                  * - Library
+                    - Reference
+
+                  * - ``accelerate``
+                    - `Hugging Face Accelerate <https://huggingface.co/docs/accelerate/en/index>`_
+
+                  * - ``datasets``
+                    - `Hugging Face Datasets <https://huggingface.co/docs/datasets/v3.2.0/en/index>`_ 3.2.0
+
+                  * - ``sentencepiece``
+                    - `SentencePiece <https://github.com/google/sentencepiece>`_ 0.2.0
+
+                  * - ``tensorboard``
+                    - `TensorBoard <https://www.tensorflow.org/tensorboard>`_ 2.18.0
+
+                  * - ``csvkit``
+                    - `csvkit <https://csvkit.readthedocs.io/en/latest/>`_ 2.0.1
+
+                  * - ``deepspeed``
+                    - `DeepSpeed <https://github.com/deepspeedai/DeepSpeed>`_ 0.16.2
+
+                  * - ``diffusers``
+                    - `Hugging Face Diffusers <https://huggingface.co/docs/diffusers/en/index>`_ 0.31.0
+
+                  * - ``GitPython``
+                    - `GitPython <https://github.com/gitpython-developers/GitPython>`_ 3.1.44
+
+                  * - ``opencv-python-headless``
+                    - `opencv-python-headless <https://pypi.org/project/opencv-python-headless/>`_ 4.10.0.84
+
+                  * - ``peft``
+                    - `PEFT <https://huggingface.co/docs/peft/en/index>`_ 0.14.0
+
+                  * - ``protobuf``
+                    - `Protocol Buffers <https://github.com/protocolbuffers/protobuf>`_ 5.29.2
+
+                  * - ``pytest``
+                    - `PyTest <https://docs.pytest.org/en/stable/>`_ 8.3.4
+
+                  * - ``python-dotenv``
+                    - `python-dotenv <https://pypi.org/project/python-dotenv/>`_ 1.0.1
+
+                  * - ``seaborn``
+                    - `Seaborn <https://seaborn.pydata.org/>`_ 0.13.2
+
+                  * - ``transformers``
+                    - `Transformers <https://huggingface.co/docs/transformers/en/index>`_ 4.47.0
+
+            ``pytorch_benchmark_setup.sh`` downloads the following datasets from Hugging Face:
+
+            * `bghira/pseudo-camera-10k <https://huggingface.co/datasets/bghira/pseudo-camera-10k>`_
+
+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+         {% set training_modes = model.training_modes %}
+         {% set training_mode_descs = {
+            "pretrain": "Benchmark pre-training.",
+            "HF_pretrain": "Llama 3.1 8B pre-training with FP8 precision."
+         } %}
+         {% set available_modes = training_modes | select("in", ["pretrain", "HF_pretrain"]) | list %}
+         {% if available_modes %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Pre-training
+
+            To start the pre-training benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t {% if available_modes | length == 1 %}{{ available_modes[0] }}{% else %}$training_mode{% endif %} \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+            {% if model.mad_tag == "pyt_train_flux" %}
+            .. container:: model-doc {{ model.mad_tag }}
+
+               .. note::
+
+                  Currently, FLUX models are not supported out-of-the-box on {{ unified_docker.pull_tag }}.
+                  To use FLUX, refer to the previous version of the ``pytorch-training`` Docker: :doc:`pytorch-training-v25.6`
+
+                  Occasionally, downloading the Flux dataset might fail. In the event of this
+                  error, manually download it from Hugging Face at
+                  `black-forest-labs/FLUX.1-dev <https://huggingface.co/black-forest-labs/FLUX.1-dev>`_
+                  and save it to `/workspace/FluxBenchmark`. This ensures that the test script can access
+                  the required dataset.
+            {% endif %}
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if model.mad_tag == "pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
+                 - Only Llama 3.1 8B supports FP8 precision.
+
+               * - ``$sequence_length``
+                 - Sequence length for the language model.
+                 - Between 2048 and 8192. 8192 by default.
+         {% endif %}
+
+         {% set training_mode_descs = {
+            "finetune_fw": "Full weight fine-tuning (BF16 and FP8 supported).",
+            "finetune_lora": "LoRA fine-tuning (BF16 supported).",
+            "finetune_qlora": "QLoRA fine-tuning (BF16 supported).",
+            "HF_finetune_lora": "LoRA fine-tuning with Hugging Face PEFT.",
+         } %}
+         {% set available_modes = training_modes | select("in", ["finetune_fw", "finetune_lora", "finetune_qlora", "HF_finetune_lora"]) | list %}
+         {% if available_modes %}
+         .. container:: model-doc {{ model.mad_tag }}
+
+            .. rubric:: Fine-tuning
+
+            To start the fine-tuning benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+            See :ref:`supported training modes <amd-pytorch-training-supported-training-modes-v257>`.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t $training_mode \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if "finetune_fw" in available_modes %} or ``FP8``{% endif %}
+                 - All models support BF16.{% if "finetune_fw" in available_modes %} FP8 is only available for full weight fine-tuning.{% endif %}
+
+               * - ``$sequence_length``
+                 - Between 2048 and 16384.
+                 - Sequence length for the language model.
+
+            {% if model.mad_tag in ["pyt_train_llama3.2-vision-11b", "pyt_train_llama-3.2-vision-90b"] %}
+            .. note::
+
+               For LoRA and QLoRA support with vision models (Llama 3.2 11B and 90B),
+               use the following torchtune commit for compatibility:
+
+               .. code-block:: shell
+
+                  git checkout 48192e23188b1fc524dd6d127725ceb2348e7f0e
+
+            {% elif model.mad_tag in ["pyt_train_llama-2-7b", "pyt_train_llama-2-13b", "pyt_train_llama-2-70b"] %}
+            .. note::
+
+               You might encounter the following error with Llama 2: ``ValueError: seq_len (16384) of
+               input tensor should be smaller than max_seq_len (4096)``.
+               This error indicates that an input sequence is longer than the model's maximum context window.
+
+               Ensure your tokenized input does not exceed the model's ``max_seq_len`` (4096
+               tokens in this case). You can resolve this by truncating the input or splitting
+               it into smaller chunks before passing it to the model.
+
+               Note on reproducibility: The results in this guide are based on
+               commit ``b4c98ac`` from the upstream
+               `<https://github.com/pytorch/torchtune>`__ repository. For the
+               latest updates, you can use the main branch.
+
+            {% endif %}
+         {% endif %}
+      {% endfor %}
+   {% endfor %}
+
+            .. rubric:: Benchmarking examples
+
+            For examples of benchmarking commands, see `<https://github.com/ROCm/MAD/tree/develop/benchmark/pytorch_train#benchmarking-examples>`__.
+
+Multi-node training
+-------------------
+
+Pre-training
+~~~~~~~~~~~~
+
+Multi-node training with torchtitan is supported. The provided SLURM script is pre-configured for Llama 3 70B.
+
+To launch the training job on a SLURM cluster for Llama 3 70B, run the following commands from the MAD repository.
+
+.. code-block:: shell
+
+   # In the MAD repository
+   cd scripts/pytorch_train
+   sbatch run_slurm_train.sh
+
+Fine-tuning
+~~~~~~~~~~~
+
+Multi-node training with torchtune is supported. The provided SLURM script is pre-configured for Llama 3.3 70B.
+
+To launch the training job on a SLURM cluster for Llama 3.3 70B, run the following commands from the MAD repository.
+
+.. code-block:: shell
+
+   huggingface-cli login # Get access to HF Llama model space
+   huggingface-cli download meta-llama/Llama-3.3-70B-Instruct --local-dir ./models/Llama-3.3-70B-Instruct # Download the Llama 3.3 model locally
+   # In the MAD repository
+   cd scripts/pytorch_train
+   sbatch Torchtune_Multinode.sh
+
+.. note::
+
+   Information regarding benchmark setup:
+
+   * By default, Llama 3.3 70B is fine-tuned using ``alpaca_dataset``.
+   * You can adjust the torchtune `YAML configuration file
+     <https://github.com/pytorch/torchtune/blob/main/recipes/configs/llama3_3/70B_full_multinode.yaml>`__
+     if you're using a different model.
+   * The number of nodes and other parameters can be tuned in the SLURM script ``Torchtune_Multinode.sh``.
+   * Set the ``mounting_paths`` inside the SLURM script.
+
+Once the run is finished, you can find the log files in the ``result_torchtune/`` directory.
+
+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 accelerators, 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/primus-pytorch.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
@@ -0,0 +1,305 @@
+.. 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
+****************************************
+
+`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 accelerators using a modular, reproducible configuration paradigm.
+Primus now supports the PyTorch torchtitan backend.
+
+.. note::
+
+   Primus with the PyTorch torchtitan backend is intended to supersede the :doc:`ROCm PyTorch training <pytorch-training>` workflow.
+   See :doc:`pytorch-training` to see steps to run workloads without Primus.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
+
+   {% set dockers = data.dockers %}
+   {% set docker = dockers[0] %}
+   For ease of use, AMD provides a ready-to-use Docker image -- ``{{
+   docker.pull_tag }}`` -- for MI300X series accelerators containing essential
+   components for Primus and PyTorch training with
+   Primus Turbo optimizations.
+
+   .. list-table::
+      :header-rows: 1
+
+      * - Software component
+        - Version
+
+      {% for component_name, component_version in docker.components.items() %}
+      * - {{ component_name }}
+        - {{ component_version }}
+      {% endfor %}
+
+.. _amd-primus-pytorch-model-support-v258:
+
+Supported models
+================
+
+The following models are pre-optimized for performance on the AMD Instinct MI325X and MI300X accelerators.
+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/primus-pytorch-benchmark-models.yaml
+
+   {% set unified_docker = data.dockers[0] %}
+   {% set model_groups = data.model_groups %}
+   .. raw:: html
+
+      <div id="vllm-benchmark-ud-params-picker" class="container-fluid">
+         <div class="row gx-0" style="display: none;">
+            <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-3 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">Model</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-v258:
+
+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.
+
+.. datatemplate:yaml:: /data/how-to/rocm-for-ai/training/primus-pytorch-benchmark-models.yaml
+
+   {% set unified_docker = data.dockers[0] %}
+
+   Pull the Docker image
+   =====================
+
+   Use the following command to pull the `Docker image <{{ unified_docker.docker_hub_url }}>`_ from Docker Hub.
+
+   .. code-block:: shell
+
+      docker pull {{ unified_docker.pull_tag }}
+
+   Run training
+   ============
+
+   {% set model_groups = data.model_groups %}
+
+   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).
+
+   .. 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-v258` 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:: Standalone 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-v258` to switch to another available model.
+
+            .. rubric:: Download the Docker image and required packages
+
+            1. Use the following command to pull the Docker image from Docker Hub.
+
+               .. code-block:: shell
+
+                  docker pull {{ unified_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 $HOME/.ssh:/root/.ssh \
+                      --shm-size 64G \
+                      --name training_env \
+                      {{ unified_docker.pull_tag }}
+
+               Use these commands if you exit the ``training_env`` container and need to return to it.
+
+               .. code-block:: shell
+
+                  docker start training_env
+                  docker exec -it training_env bash
+
+            3. In the Docker container, clone the `<https://github.com/ROCm/MAD>`__
+               repository and navigate to the benchmark scripts directory
+               ``/workspace/MAD/scripts/pytorch_train``.
+
+               .. code-block:: shell
+
+                  git clone https://github.com/ROCm/MAD
+                  cd MAD/scripts/pytorch_train
+
+            .. rubric:: Prepare training datasets and dependencies
+
+            1. The following benchmarking examples require downloading models and datasets
+               from Hugging Face. To ensure successful access to gated repos, set your
+               ``HF_TOKEN``.
+
+               .. code-block:: shell
+
+                  export HF_TOKEN=$your_personal_hugging_face_access_token
+
+            2. Run the setup script to install libraries and datasets needed for benchmarking.
+
+               .. code-block:: shell
+
+                  ./pytorch_benchmark_setup.sh
+
+            .. rubric:: Pretraining
+
+            To start the pretraining benchmark, use the following command with the
+            appropriate options. See the following list of options and their descriptions.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -t pretrain \
+                   -m {{ model.model_repo }} \
+                   -p $datatype \
+                   -s $sequence_length
+
+
+            .. list-table::
+               :header-rows: 1
+
+               * - Name
+                 - Options
+                 - Description
+
+               {% for mode in available_modes %}
+               * - {% if loop.first %}``$training_mode``{% endif %}
+                 - ``{{ mode }}``
+                 - {{ training_mode_descs[mode] }}
+               {% endfor %}
+
+               * - ``$datatype``
+                 - ``BF16``{% if model.mad_tag == "primus_pyt_train_llama-3.1-8b" %} or ``FP8``{% endif %}
+                 - Currently, only Llama 3.1 8B supports FP8 precision.
+
+               * - ``$sequence_length``
+                 - Sequence length for the language model.
+                 - Between 2048 and 8192. 8192 by default.
+
+            .. rubric:: Benchmarking examples
+
+            Use the following command to run train {{ model.model }} with BF16 precision using Primus torchtitan.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -m {{ model.model_repo }}
+
+            To train {{ model.model }} with FP8 precision, use the following command.
+
+            .. code-block:: shell
+
+               ./pytorch_benchmark_report.sh -m {{ model.model_repo }} -p FP8
+      {% 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 accelerators, 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/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/pytorch-training.rst
+++ b/docs/how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
@@ -1,11 +1,18 @@
+: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 PyTorch for ROCm
+Training a model with PyTorch on ROCm
 **************************************

+.. note::
+
+   Primus with the PyTorch torchtitan backend is intended to supersede the :doc:`ROCm PyTorch training <pytorch-training>` workflow.
+   See :doc:`primus-pytorch` for details.
+
 PyTorch is an open-source machine learning framework that is widely used for
 model training with GPU-optimized components for transformer-based models.

@@ -49,7 +56,7 @@ vary by model -- select one to get started.
            <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-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-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>
@@ -87,9 +94,11 @@ vary by model -- select one to get started.
      {% for model_group in model_groups %}
         {% set models = model_group.models %}
         {% for model in models %}
+         {% if model.training_modes %}
         * - {{ model.model }}
           - ``{{ model.training_modes | join('``, ``') }}``

+         {% endif %}
         {% endfor %}
      {% endfor %}

@@ -152,20 +161,23 @@ Run training

      .. 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
-
   {% 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-pytorch-training-model-support` 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.

@@ -187,6 +199,17 @@ Run training

      .. tab-item:: Standalone benchmarking

+   {% for model_group in model_groups %}
+      {% for model in model_group.models %}
+
+         .. container:: model-doc {{ model.mad_tag }}
+
+            The following commands are tailored to {{ model.model }}.
+            See :ref:`amd-pytorch-training-model-support` to switch to another available model.
+
+      {% endfor %}
+   {% endfor %}
+
         .. rubric:: Download the Docker image and required packages

         1. Use the following command to pull the Docker image from Docker Hub.
@@ -388,7 +411,7 @@ Run training
               .. note::

                  Currently, FLUX models are not supported out-of-the-box on {{ unified_docker.pull_tag }}.
-                  To use FLUX, refer to the previous version of the ``pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`
+                  To use FLUX, refer to ``rocm/pytorch-training`` Docker: :doc:`previous-versions/pytorch-training-v25.6`

                  Occasionally, downloading the Flux dataset might fail. In the event of this
                  error, manually download it from Hugging Face at
--- a/docs/how-to/rocm-for-hpc/index.rst
+++ b/docs/how-to/rocm-for-hpc/index.rst
@@ -76,6 +76,14 @@ Ubuntu versions.
          single node workstations, multi and many-core nodes, clusters of nodes via
          QMP, and classic vector computers.

+      * -
+        - `Grid <https://github.com/amd/InfinityHub-CI/tree/main/grid/>`_
+        - Grid is a library for lattice QCD calculations that employs a high-level data parallel
+          approach while using a number of techniques to target multiple types of parallelism.
+          The library currently supports MPI, OpenMP, and short vector parallelism. The SIMD
+          instruction sets covered include SSE, AVX, AVX2, FMA4, IMCI, and AVX512. Recent
+          releases expanded this support to include GPU offloading.
+
      * -
        - `MILC <https://github.com/amd/InfinityHub-CI/tree/main/milc/>`_
        - The MILC Code is a set of research codes developed by MIMD Lattice Computation
@@ -148,24 +156,6 @@ Ubuntu versions.
          backends ranging from general-purpose processors, CUDA and HIP enabled
          accelerators to SX-Aurora vector processors.

-      * -
-        - `nekRS <https://github.com/amd/InfinityHub-CI/tree/main/nekrs>`_
-        - nekRS is an open-source Navier Stokes solver based on the spectral element
-          method targeting classical processors and accelerators like GPUs.
-
-      * -
-        - `OpenFOAM <https://github.com/amd/InfinityHub-CI/tree/main/openfoam>`_
-        - OpenFOAM is a free, open-source computational fluid dynamics (CFD)
-          tool developed primarily by OpenCFD Ltd. It has a large user
-          base across most areas of engineering and science, from both commercial and
-          academic organizations. OpenFOAM has extensive features to solve
-          anything from complex fluid flows involving chemical reactions, turbulence, and
-          heat transfer, to acoustics, solid mechanics, and electromagnetics.
-
-      * -
-        - `PeleC <https://github.com/amd/InfinityHub-CI/tree/main/pelec>`_
-        - PeleC is an adaptive mesh refinement(AMR) solver for compressible reacting flows.
-
      * -
        - `Simcenter Star-CCM+ <https://github.com/amd/InfinityHub-CI/tree/main/siemens-star-ccm>`_
        - Simcenter Star-CCM+ is a comprehensive computational fluid dynamics (CFD) and multiphysics
@@ -199,15 +189,6 @@ Ubuntu versions.
          defined in SymPy to create and execute highly optimized Finite Difference stencil
          kernels on multiple computer platforms.

-      * -
-        - `ECHELON <https://github.com/amd/InfinityHub-CI/tree/main/srt-echelon>`_
-        - ECHELON by Stone Ridge Technology is a reservoir simulation tool. With
-          fast processing, it retains precise accuracy and preserves legacy simulator results.
-          Faster reservoir simulation enables reservoir engineers to produce many realizations,
-          address larger models, and use advanced physics. It opens new workflows based on
-          ensemble methodologies for history matching and forecasting that yield
-          increased accuracy and more predictive results.
-
      * - Benchmark
        - `rocHPL <https://github.com/amd/InfinityHub-CI/tree/main/rochpl>`_
        - HPL, or High-Performance Linpack, is a benchmark which solves a uniformly
@@ -240,6 +221,10 @@ Ubuntu versions.
        - Base container for GPU-aware MPI with ROCm for HPC applications. This
          project provides a boilerplate for building and running a Docker
          container with ROCm supporting GPU-aware MPI implementations using MPICH.
+
+      * - 
+        - `AMD ROCm with Conda Environment Container <https://github.com/amd/InfinityHub-CI/tree/main/conda-rocm-environment>`_
+        - Container recipe that uses the `base-gpu-mpi-rocm-docker` as the base and adds Conda. The container can be used as a base for applications that require conda applications.
      
      * -
        - `Kokkos <https://github.com/amd/InfinityHub-CI/tree/main/kokkos>`_
@@ -258,14 +243,6 @@ Ubuntu versions.
          range of hardware platforms via use of an in-built domain specific language derived
          from the Mako templating engine.

-      * -
-        - `PETSc <https://github.com/amd/InfinityHub-CI/tree/main/petsc>`_
-        - Portable, Extensible Toolkit for Scientific Computation (PETSc) is a suite of data structures
-          and routines for the scalable (parallel) solution of scientific applications modeled by partial
-          differential equations. It supports MPI, GPUs through CUDA, HIP, and OpenCL,
-          as well as hybrid MPI-GPU parallelism. It also supports the NEC-SX Tsubasa Vector Engine.
-          PETSc also includes the Toolkit for Advanced Optimization (TAO) library.
-
      * -
        - `RAJA <https://github.com/amd/InfinityHub-CI/tree/main/raja>`_
        - RAJA is a library of C++ software abstractions, primarily developed at Lawrence
@@ -278,4 +255,9 @@ Ubuntu versions.
          within an object-oriented software framework for the solution of large-scale,
          complex multi-physics engineering and scientific problems.

+      * -
+        - `VLLM <https://github.com/amd/InfinityHub-CI/tree/main/vllm>`_
+        - The VLLM project helps to build a Dockerfile for performance testing of the LLAMA2 applications.
+          This Dockerfile uses a base install that includes Ubuntu 20.04, ROCm 6.1.2 and Python 3.9. The container can host the LLAMA2 applications (LLMs) and requires some large input files for testing.
+
 To learn about ROCm for AI applications, see :doc:`../rocm-for-ai/index`.
--- a/docs/reference/graph-safe-support.rst
+++ b/docs/reference/graph-safe-support.rst
@@ -93,7 +93,7 @@ The following table shows whether a ROCm library is graph-safe.
      - ⚠️ (experimental)
    * 
      - `rocThrust <https://github.com/ROCm/rocThrust>`_
-      - ❌ (see :doc:`details <rocthrust:hipgraph-support>`)
+      - ❌ (see :doc:`details <rocthrust:reference/rocThrust-hipgraph-support>`)
    * 
      - `rocWMMA <https://github.com/ROCm/rocWMMA>`_
      - ❌
--- a/docs/sphinx/_toc.yml.in
+++ b/docs/sphinx/_toc.yml.in
@@ -43,12 +43,12 @@ subtrees:
        title: DGL compatibility
      - file: compatibility/ml-compatibility/megablocks-compatibility.rst
        title: Megablocks compatibility
-      - file: compatibility/ml-compatibility/taichi-compatibility.rst
-        title: Taichi 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

@@ -67,9 +67,9 @@ subtrees:
        subtrees:
        - entries:
          - file: how-to/rocm-for-ai/training/benchmark-docker/primus-megatron.rst
-            title: Train a model with Primus and Megatron-Core
-          - file: how-to/rocm-for-ai/training/benchmark-docker/pytorch-training.rst
-            title: Train a model with PyTorch
+            title: Train a model with Primus and Megatron-LM
+          - file: how-to/rocm-for-ai/training/benchmark-docker/primus-pytorch.rst
+            title: Train a model with Primus and PyTorch
          - file: how-to/rocm-for-ai/training/benchmark-docker/jax-maxtext.rst
            title: Train a model with JAX MaxText
          - file: how-to/rocm-for-ai/training/benchmark-docker/mpt-llm-foundry
@@ -106,6 +106,8 @@ subtrees:
            title: PyTorch inference performance testing
          - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang.rst
            title: SGLang inference performance testing
+          - file: how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst
+            title: SGLang distributed inference with Mooncake
          - file: how-to/rocm-for-ai/inference/deploy-your-model.rst
            title: Deploy your model

--- a/docs/sphinx/requirements.in
+++ b/docs/sphinx/requirements.in
@@ -1,4 +1,4 @@
-rocm-docs-core==1.20.1
+rocm-docs-core==1.26.0
 sphinx-reredirects
 sphinx-sitemap
 sphinxcontrib.datatemplates==0.11.0
--- a/docs/sphinx/requirements.txt
+++ b/docs/sphinx/requirements.txt
@@ -10,7 +10,7 @@ alabaster==1.0.0
    # via sphinx
 asttokens==3.0.0
    # via stack-data
-attrs==25.3.0
+attrs==25.4.0
    # via
    #   jsonschema
    #   jupyter-cache
@@ -19,34 +19,32 @@ babel==2.17.0
    # via
    #   pydata-sphinx-theme
    #   sphinx
-beautifulsoup4==4.13.4
+beautifulsoup4==4.14.2
    # via pydata-sphinx-theme
 breathe==4.36.0
    # via rocm-docs-core
-certifi==2025.4.26
+certifi==2025.10.5
    # via requests
-cffi==1.17.1
+cffi==2.0.0
    # via
    #   cryptography
    #   pynacl
-charset-normalizer==3.4.2
+charset-normalizer==3.4.4
    # via requests
-click==8.2.1
+click==8.3.0
    # via
    #   jupyter-cache
    #   sphinx-external-toc
-comm==0.2.2
+comm==0.2.3
    # via ipykernel
-cryptography==45.0.3
+cryptography==46.0.2
    # via pyjwt
-debugpy==1.8.14
+debugpy==1.8.17
    # via ipykernel
 decorator==5.2.1
    # via ipython
 defusedxml==0.7.1
    # via sphinxcontrib-datatemplates
-deprecated==1.2.18
-    # via pygithub
 docutils==0.21.2
    # via
    #   myst-parser
@@ -54,19 +52,19 @@ docutils==0.21.2
    #   sphinx
 exceptiongroup==1.3.0
    # via ipython
-executing==2.2.0
+executing==2.2.1
    # via stack-data
-fastjsonschema==2.21.1
+fastjsonschema==2.21.2
    # via
    #   nbformat
    #   rocm-docs-core
 gitdb==4.0.12
    # via gitpython
-gitpython==3.1.44
+gitpython==3.1.45
    # via rocm-docs-core
-greenlet==3.2.3
+greenlet==3.2.4
    # via sqlalchemy
-idna==3.10
+idna==3.11
    # via requests
 imagesize==1.4.1
    # via sphinx
@@ -74,7 +72,7 @@ importlib-metadata==8.7.0
    # via
    #   jupyter-cache
    #   myst-nb
-ipykernel==6.29.5
+ipykernel==7.0.0
    # via myst-nb
 ipython==8.37.0
    # via
@@ -86,9 +84,9 @@ jinja2==3.1.6
    # via
    #   myst-parser
    #   sphinx
-jsonschema==4.24.0
+jsonschema==4.25.1
    # via nbformat
-jsonschema-specifications==2025.4.1
+jsonschema-specifications==2025.9.1
    # via jsonschema
 jupyter-cache==1.0.1
    # via myst-nb
@@ -106,17 +104,17 @@ markdown-it-py==3.0.0
    # via
    #   mdit-py-plugins
    #   myst-parser
-markupsafe==3.0.2
+markupsafe==3.0.3
    # via jinja2
 matplotlib-inline==0.1.7
    # via
    #   ipykernel
    #   ipython
-mdit-py-plugins==0.4.2
+mdit-py-plugins==0.5.0
    # via myst-parser
 mdurl==0.1.2
    # via markdown-it-py
-myst-nb==1.2.0
+myst-nb==1.3.0
    # via rocm-docs-core
 myst-parser==4.0.1
    # via myst-nb
@@ -134,31 +132,30 @@ nest-asyncio==1.6.0
 packaging==25.0
    # via
    #   ipykernel
-    #   pydata-sphinx-theme
    #   sphinx
-parso==0.8.4
+parso==0.8.5
    # via jedi
 pexpect==4.9.0
    # via ipython
-platformdirs==4.3.8
+platformdirs==4.5.0
    # via jupyter-core
-prompt-toolkit==3.0.51
+prompt-toolkit==3.0.52
    # via ipython
-psutil==7.0.0
+psutil==7.1.0
    # via ipykernel
 ptyprocess==0.7.0
    # via pexpect
 pure-eval==0.2.3
    # via stack-data
-pycparser==2.22
+pycparser==2.23
    # via cffi
-pydata-sphinx-theme==0.15.4
+pydata-sphinx-theme==0.16.1
    # via
    #   rocm-docs-core
    #   sphinx-book-theme
-pygithub==2.6.1
+pygithub==2.8.1
    # via rocm-docs-core
-pygments==2.19.1
+pygments==2.19.2
    # via
    #   accessible-pygments
    #   ipython
@@ -166,11 +163,11 @@ pygments==2.19.1
    #   sphinx
 pyjwt[crypto]==2.10.1
    # via pygithub
-pynacl==1.5.0
+pynacl==1.6.0
    # via pygithub
 python-dateutil==2.9.0.post0
    # via jupyter-client
-pyyaml==6.0.2
+pyyaml==6.0.3
    # via
    #   jupyter-cache
    #   myst-nb
@@ -178,21 +175,21 @@ pyyaml==6.0.2
    #   rocm-docs-core
    #   sphinx-external-toc
    #   sphinxcontrib-datatemplates
-pyzmq==26.4.0
+pyzmq==27.1.0
    # via
    #   ipykernel
    #   jupyter-client
-referencing==0.36.2
+referencing==0.37.0
    # via
    #   jsonschema
    #   jsonschema-specifications
-requests==2.32.4
+requests==2.32.5
    # via
    #   pygithub
    #   sphinx
-rocm-docs-core==1.20.1
+rocm-docs-core==1.26.0
    # via -r requirements.in
-rpds-py==0.25.1
+rpds-py==0.27.1
    # via
    #   jsonschema
    #   referencing
@@ -202,7 +199,7 @@ smmap==5.0.2
    # via gitdb
 snowballstemmer==3.0.1
    # via sphinx
-soupsieve==2.7
+soupsieve==2.8
    # via beautifulsoup4
 sphinx==8.1.3
    # via
@@ -220,7 +217,7 @@ sphinx==8.1.3
    #   sphinx-reredirects
    #   sphinxcontrib-datatemplates
    #   sphinxcontrib-runcmd
-sphinx-book-theme==1.1.4
+sphinx-book-theme==1.1.3
    # via rocm-docs-core
 sphinx-copybutton==0.5.2
    # via rocm-docs-core
@@ -234,7 +231,7 @@ sphinx-notfound-page==1.1.0
    # via rocm-docs-core
 sphinx-reredirects==0.1.6
    # via -r requirements.in
-sphinx-sitemap==2.8.0
+sphinx-sitemap==2.9.0
    # via -r requirements.in
 sphinxcontrib-applehelp==2.0.0
    # via sphinx
@@ -252,21 +249,20 @@ sphinxcontrib-runcmd==0.2.0
    # via sphinxcontrib-datatemplates
 sphinxcontrib-serializinghtml==2.0.0
    # via sphinx
-sqlalchemy==2.0.41
+sqlalchemy==2.0.44
    # via jupyter-cache
 stack-data==0.6.3
    # via ipython
 tabulate==0.9.0
    # via jupyter-cache
-tomli==2.2.1
+tomli==2.3.0
    # via sphinx
-tornado==6.5.1
+tornado==6.5.2
    # via
    #   ipykernel
    #   jupyter-client
 traitlets==5.14.3
    # via
-    #   comm
    #   ipykernel
    #   ipython
    #   jupyter-client
@@ -274,9 +270,10 @@ traitlets==5.14.3
    #   matplotlib-inline
    #   nbclient
    #   nbformat
-typing-extensions==4.14.0
+typing-extensions==4.15.0
    # via
    #   beautifulsoup4
+    #   cryptography
    #   exceptiongroup
    #   ipython
    #   myst-nb
@@ -288,9 +285,7 @@ urllib3==2.5.0
    # via
    #   pygithub
    #   requests
-wcwidth==0.2.13
+wcwidth==0.2.14
    # via prompt-toolkit
-wrapt==1.17.2
-    # via deprecated
 zipp==3.23.0
    # via importlib-metadata
Author	SHA1	Message	Date
Pratik Basyal	518af31092	7.0.0 Taichi removed (#5800 )	2025-12-19 15:43:53 -05:00
Istvan Kiss	d289732d7a	Add JAX Plugin-PJRT support table 7.0.0 (#5672 ) * Add JAX Plugin-PJRT support table * Update wordlist.txt for JAX comp table	2025-11-20 17:42:03 +01:00
Pratik Basyal	79b26dd47a	AMDGPU link update post version branching (#5662 )	2025-11-13 12:09:24 -06:00
Istvan Kiss	33a62e63d2	Remove CentOS Stream mention from PyTorch release notes. (#5585 )	2025-10-28 14:13:43 +01:00
Pratik Basyal	efb82287f2	PLDM update for MI250 and MI210 (#5540 )	2025-10-17 17:33:34 -04:00
Alex Xu	b0e1d68cb7	bump rocm-docs-core version to 1.26.0	2025-10-14 11:33:10 -04:00
Pratik Basyal	68071aaa18	Merge conflict from cherry-pick fixed (#5506 ) (#5507 )	2025-10-10 22:44:40 -04:00
Pratik Basyal	754ed7fda8	700 framework changelog update (#5485 ) * Framework and changelog updated (#5483) * Framework and chaneglog updated * Wordlist updated * 701 change from cherry-pick removed	2025-10-08 15:16:48 -04:00
anisha-amd	7b86e29541	Docs: frameworks 25.09 - compatibility - FlashInfer and llama.cpp (#5462 ) (#5464 )	2025-10-02 14:28:49 -04:00
Pratik Basyal	10cd5ced57	PLDM version updated for docs/7.0.0 branch (#5413 )	2025-09-22 11:14:15 -04:00
Pratik Basyal	7147bf17e8	rocSHMEM and ROCprofiler-SDK release highlight udpated (#5410 )	2025-09-22 10:26:06 -04:00
Pratik Basyal	f9986589f6	ROCPD highlight and KMD renamed to AMD GPU Driver (#5392 ) * ROCPD highlight and KMD renamed to AMD GPU Driver * Minor change	2025-09-18 18:50:13 -04:00
Peter Park	80f5cd2f71	Remove duplicate ML FW docker image support table (#5389 ) (#5391 ) (cherry picked from commit `27d53cf082`)	2025-09-18 17:11:13 -04:00
Pratik Basyal	04e4e8a2f1	Compatibility updated 700 (#5359 ) * Compatibility updated (#5355) * TF update	2025-09-16 18:59:09 -04:00
Peter Park	8724f5a8c4	Fix PyTorch training benchmark doc template (#5357 ) (#5358 ) * fix template * update wordlist (cherry picked from commit `e8d104124f`)	2025-09-16 17:26:10 -04:00
Peter Park	01ab9d6199	Add Stable Diffusion XL to PyT training benchmark doc and fix paths in SGLang Disagg Inference doc (#5282 ) (#5356 ) * add sdxl to pytorch-training * fix sphinx warnings fix links * fix paths in cmds and links in sglang disagg * fix col width * update release highlights * fix quickfix (cherry picked from commit `26f708da87`)	2025-09-16 17:02:36 -04:00
randyh62	bc4daaf2de	Revert "Update RELEASE.md" (#5354 ) * Revert "Update RELEASE.md (#5330)" This reverts commit `9f703e27bb`. * Update RELEASE.md * Update .wordlist.txt fix spelling	2025-09-16 13:33:45 -07:00
Peter Park	24dec07aef	Add NCF to pytorch training benchmark doc (#5352 ) (#5353 ) * add previous version (25.6) * fix template * Formatting and wording fixes * add caveats * update yaml * add note to pytorch-training * fix template * make model name shorter (cherry picked from commit `bab853a0d3`)	2025-09-16 13:33:07 -04:00
Pratik Basyal	9e1871a01b	Github Issue Links updated (#5350 ) (#5351 ) * 7.0.0 compatibility updated * GIM link updated	2025-09-16 13:04:24 -04:00
Peter Park	b0fdab6c8c	fix pldm note (#5346 ) (#5348 ) (cherry picked from commit `8c40d14d7e`)	2025-09-16 11:14:43 -05:00
Peter Park	4e45bf7838	Merge develop into docs/7.0.0 (#5340 ) * Post GA fixes develop (#5329) * Develop link updated * Release notes and compatibilty update * Compatibilitbity updated * RPP link updated * Compatibility updated for 7.0.0 (#5332) * Compatibility udpated * Minor fix * docs(PyTorch training v25.8): Add Primus and update PyTorch training benchmark docs (#5331) * pyt: update previous versions list update conf.py * pyt: update yaml and rst update update toc * update headings and anchors * pyt: update doc * update docker hub urls * docs: Add SGLang disaggregated P/D inference w/ Mooncake guide (#5335) * add main content * Update content and format add clarification update update data * fix fix fix * fix: deepseek v3 * add ki * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> * Update docs/how-to/rocm-for-ai/inference/benchmark-docker/sglang-distributed.rst Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> --------- Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com> --------- Co-authored-by: Pratik Basyal <prbasyal@amd.com> Co-authored-by: Leo Paoletti <164940351+lpaoletti@users.noreply.github.com>	2025-09-16 10:42:08 -05:00
Pratik Basyal	ef75f43c5e	700 compatibility matrix fix (#5333 ) * Post GA fixes develop (#5329) * Develop link updated * Release notes and compatibilty update * Compatibilitbity updated * RPP link updated * Compatibility updated for 7.0.0 (#5332) * Compatibility udpated * Minor fix	2025-09-16 10:18:35 -05:00
randyh62	9f703e27bb	Update RELEASE.md (#5330 ) update llvm-project link URL	2025-09-16 07:32:03 -07:00
anisha-amd	1214bd84ed	Docs: deep learning table fix	2025-09-16 09:26:45 -04:00
Parag Bhandari	f8cb05fd07	Merge branch 'roc-7.0.x' into docs/7.0.0	2025-09-16 08:20:03 -04:00
Pratik Basyal	22a9ab4626	700 reset link [Develop] (#5325 ) (#5327 ) * TOC link update and manifest removed * Link reset * Changelog synced	2025-09-16 08:10:42 -04:00
Parag Bhandari	63d8f852da	Merge branch 'roc-7.0.x' into docs/7.0.0	2025-09-16 07:37:31 -04:00
Pratik Basyal	72127d21d3	700 update pre GA batch1 (#5322 ) (#5324 ) * Fix PLDM note for ROCm 7.0 (#5320) * fix pdlm for mi300x * update debian 12 support note * 7.0.0 Release notes update Batch 9 (#559) * Changelog synced * Compatibilty updated * Compatibilty update * Compiler highlight updated * wordlist updated --------- Co-authored-by: Peter Park <peter.park@amd.com>	2025-09-16 06:31:27 -05:00
Parag Bhandari	ecbcc9b11f	Merge branch 'develop' into docs/7.0.0	2025-09-16 06:09:57 -04:00
pbhandar-amd	76571df432	Sync develop into docs/7.0.0	2025-09-15 21:44:26 -04:00
pbhandar-amd	40ffdeb995	Sync develop into docs/7.0.0	2025-09-15 12:14:07 -04:00
pbhandar-amd	681f31fbb2	Sync develop into docs/7.0.0	2025-09-11 17:55:27 -04:00
pbhandar-amd	ceae5bc124	Update documentation requirements for ROCm	2025-09-11 15:27:33 -04:00
anisha-amd	5f516799fe	Docs: adding ray and llama.cpp live blog links (#5290 ) (#5292 )	2025-09-10 15:15:41 -04:00
anisha-amd	d6e4bb6ff6	Docs: frameworks compatibility- ray and llama.cpp (#5273 ) (#5275 )	2025-09-09 12:36:25 -04:00
pbhandar-amd	25ec3eec87	Sync develop into docs/7.0.0	2025-08-28 17:44:53 -04:00
pbhandar-amd	6048413d0d	Update documentation requirements	2025-08-28 17:09:16 -04:00
pbhandar-amd	94a4e655a7	Update requirements.in	2025-08-28 16:48:02 -04:00