# ROCm 7.0.2 release notes The release notes provide a summary of notable changes since the previous ROCm release. - [Release highlights](#release-highlights) - [Supported hardware, operating system, and virtualization changes](#supported-hardware-operating-system-hardware-and-virtualization-changes) - [User space, driver, and firmware dependent changes](#user-space-driver-and-firmware-dependent-changes) - [ROCm components versioning](#rocm-components) - [Detailed component changes](#detailed-component-changes) - [ROCm known issues](#rocm-known-issues) - [ROCm upcoming changes](#rocm-upcoming-changes) ```{note} If you’re using AMD Radeon GPUs or Ryzen APUs in a workstation setting with a display connected, see the [Use ROCm on Radeon and Ryzen](https://rocm.docs.amd.com/projects/radeon-ryzen/en/latest/index.html) documentation to verify compatibility and system requirements. ``` ## Release highlights The following are notable new features and improvements in ROCm 7.0.2. For changes to individual components, see [Detailed component changes](#detailed-component-changes). ### Supported hardware, operating system, and virtualization changes ROCm 7.0.2 adds support for the RDNA4 architecture-based [AMD Radeon RX 9060](https://www.amd.com/en/products/graphics/desktops/radeon/9000-series/amd-radeon-rx-9060.html). For more information about supported AMD hardware, see [Supported GPUs (Linux)](https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/reference/system-requirements.html#supported-gpus). ROCm 7.0.2 adds support for the following operating systems and kernel versions: * Debian 13 (kernel: 6.12) * Oracle Linux 10 (kernel: 6.12.0 [UEK]) * RHEL 10.0 (kernel: 6.12.0-55) For more information about supported operating systems, see [Supported operating systems](https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/reference/system-requirements.html#supported-operating-systems) and [install instructions](https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/). #### Virtualization support Virtualization support remains unchanged in this release. For more information, see [Virtualization Support](https://rocm.docs.amd.com/projects/install-on-linux-internal/en/latest/reference/system-requirements.html#virtualization-support). ### User space, driver, and firmware dependent changes The software for AMD Datacenter GPU products requires maintaining a hardware and software stack with interdependencies between the GPU and baseboard firmware, AMD GPU drivers, and the ROCm user space software.
|
ROCm Version |
GPU |
PLDM Bundle (Firmware) |
AMD GPU Driver (amdgpu) |
AMD GPU |
|---|---|---|---|---|
| ROCm 7.0.2 | MI355X |
01.25.15.02 (or later) 01.25.13.09 |
30.10.2 30.10.1 30.10 |
8.4.1.K |
| MI350X |
01.25.15.02 (or later) 01.25.13.09 |
30.10.2 30.10.1 30.10 |
||
| MI325X |
01.25.04.02 (or later) 01.25.03.03 |
30.10.2 30.10.1 30.10 6.4.z where z (0-3) 6.3.y where y (1-3) |
||
| MI300X | 01.25.05.00 (or later)[1] 01.25.03.12 |
30.10.2 30.10.1 30.10 6.4.z where z (0–3) 6.3.y where y (0–3) 6.2.x where x (1–4) |
8.4.1.K | |
| MI300A | BKC 26 (or later) BKC 25 |
Not Applicable | ||
| MI250X | IFWI 47 (or later) | |||
| MI250 | MU5 w/ IFWI 75 (or later) | |||
| MI210 | MU5 w/ IFWI 75 (or later) | 8.4.0.K | ||
| MI100 | VBIOS D3430401-037 | Not Applicable |
[1]: PLDM bundle 01.25.05.00 will be available by October 31, 2025.
#### AMD Instinct MI300X GPU resiliency improvement Multimedia Engine Reset has been added to support finer-grain GPU Resiliency on AMD Instinct MI300X GPUs. It allows recovery from VCN/JPEG kernel queue hang cases without requiring a full GPU reset, improving system stability and fault tolerance. To support this feature, the AMD Instinct MI300X GPU requires PLDM bundle 01.25.05.00 (or later) firmware and AMD GPU Driver (amdgpu) 30.10.2. #### New OS support in ROCm dependent on AMD GPU Driver ROCm support for RHEL 10.0 and Oracle 10 requires AMD GPU Driver 30.10.2 or later. ### RAG AI support enabled for ROCm In September 2025, Retrieval-Augmented Generation (RAG) was added to the ROCm platform. Use RAG to build and deploy end-to-end AI pipelines on AMD GPUs. It enhances the accuracy and reliability of a large language model (LLM) by exposing it to up-to-date, relevant information. When queried, RAG retrieves relevant data from its knowledge base and uses it in conjunction with the query to generate accurate and informed responses. This approach minimizes hallucinations (the creation of false information) while also enabling the model to access current information not present in its original training data. For more information, see the [ROCm-RAG documentation](https://rocm.docs.amd.com/projects/rocm-rag-internal/en/docs/index.html). ### gsplat support enabled for ROCm [Gaussian splatting (gsplat)](https://rocm.docs.amd.com/projects/gsplat/en/latest/index.html) is an open-source library for GPU-accelerated differentiable rasterization of 3D Gaussians with Python bindings. This ROCm-enabled release of gsplat, introduced in September 2025, is built on top of [AMD PyTorch for ROCm](https://rocm.docs.amd.com/projects/install-on-linux/en/docs-6.4.3/install/3rd-party/pytorch-install.html). It enables innovators in computer graphics, machine learning, and 3D vision to leverage GPU acceleration with AMD Instinct™ GPUs. With gsplat, you can build, research, and innovate with Gaussian splatting. To install gsplat, see [gsplat on ROCm installation](https://rocm.docs.amd.com/projects/gsplat/en/latest/install/gsplat-install.html). ### Introducing ROCm Life Science (ROCm-LS) toolkit The ROCm Life Science (ROCm-LS) toolkit is an open-source software collection for high-performance life science and healthcare applications built on the core ROCm platform. It helps you accelerate life science processing and analyze workloads on AMD GPUs. ROCm-LS is in an early access state. Running production workloads is not recommended. For more information, see the [AMD ROCm-LS documentation](https://rocm.docs.amd.com/projects/rocm-ls-docs-internal/en/latest/). ROCm-LS provides the following tools to build a complete workflow for life science acceleration on AMD GPUs: * The hipCIM library provides powerful support for GPU-accelerated I/O operations, coupled with an array of computer vision and image processing primitives designed for N-dimensional image data in fields such as biomedical imaging. For more information, see the [hipCIM documentation](https://rocm.docs.amd.com/projects/hipCIM/en/latest/). * MONAI for AMD ROCm, a ROCm-enabled version of [MONAI](https://monai.io/), is built on top of [PyTorch for AMD ROCm](https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package/), helping healthcare and life science innovators to leverage GPU acceleration with AMD Instinct GPUs for high-performance inference and training of medical AI applications. For more information, see the [MONAI for AMD ROCm documentation](https://rocm.docs.amd.com/projects/monai-internal/en/latest/). ### Deep learning and AI framework updates ROCm provides a comprehensive ecosystem for deep learning development. For more information, see [Deep learning frameworks for ROCm](https://rocm.docs.amd.com/en/latest/how-to/deep-learning-rocm.html) and the [Compatibility matrix](../../docs/compatibility/compatibility-matrix.rst) for the complete list of Deep learning and AI framework versions tested for compatibility with ROCm. #### Updated framework support ROCm 7.0.0 introduces several newly supported versions of Deep learning and AI frameworks: ##### PyTorch ROCm 7.0.2 enables support for PyTorch 2.8. #### New frameworks AMD ROCm has officially added support for the following Deep learning and AI frameworks: * FlashInfer is a library and kernel generator for Large Language Models (LLMs) that provides a high-performance implementation of graphics processing units (GPUs) kernels. FlashInfer focuses on LLM serving and inference, as well as advanced performance across diverse scenarios. It is supported on ROCm 6.4.1. For more information, see [FlashInfer compatibility](https://rocm.docs.amd.com/en/latest/compatibility/ml-compatibility/flashinfer-compatibility.html). * llama.cpp is an open-source framework for Large Language Model (LLM) inference that runs on both central processing units (CPUs) and graphics processing units (GPUs). It is written in plain C/C++, providing a simple, dependency-free setup. It is now supported on ROCm 7.0.0 and 6.4.x. For more information, see [llama.cpp compatibility](https://rocm.docs.amd.com/en/docs-7.0.0/compatibility/ml-compatibility/llama-cpp-compatibility.html). ### ROCm Offline Installer Creator updates The ROCm Offline Installer Creator 7.0.2 includes the following features and improvements: * Added support for RHEL 10.0, Oracle Linux 10, and Debian 13. * Added support for creating an offline installer for Debian 12 when the kernel version of the target operating system differs from the operating system of the host creating the installer. * Removed the restriction requiring the kernels for the host and target systems to match when creating a ROCm-only (no AMD GPU Driver) offline installer. See [ROCm Offline Installer Creator](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-offline-installer.html) for more information. ### ROCm Runfile Installer updates The ROCm Runfile Installer 7.0.2 adds the following features and improvements: * Added support for RHEL 10.0, Oracle Linux 10, and Debian 13. * Minor fixes for the `untar` mode. For more information, see [ROCm Runfile Installer](https://rocm.docs.amd.com/projects/install-on-linux/en/latest/install/rocm-runfile-installer.html). ### ROCm documentation updates ROCm documentation continues to be updated to provide clearer and more comprehensive guidance for a wider variety of user needs and use cases. * [Tutorials for AI developers](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/) have been expanded with the following two new inference tutorials: * [Accelerating DeepSeek-V3 inference using multi-token prediction in SGLang](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/inference/mtp.html) * [Multi-agents with Google ADK and A2A protocol](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/notebooks/inference/power-Google-ADK-on-AMD-platform-and-local-LLMs.html) For more information about the changes, see the [Changelog for the AI Developer Hub](https://rocm.docs.amd.com/projects/ai-developer-hub/en/latest/changelog.html). * ROCm components support a wide range of environment variables that can be used for testing, logging, debugging, experimental features, and more. The [rocBLAS](https://rocm.docs.amd.com/projects/rocBLAS/en/develop/reference/env-variables.html) and [RCCL](https://rocm.docs.amd.com/projects/rccl/en/develop/api-reference/env-variables.html) components have been updated with new environment variable content. ## ROCm components The following table lists the versions of ROCm components for ROCm 7.0.2, including any version changes from 7.0.1 to 7.0.2. Click the component's updated version to go to a list of its changes. Click {fab}`github` to go to the component's source code on GitHub.| Category | Group | Name | Version | |
|---|---|---|---|---|
| Libraries | Machine learning and computer vision | Composable Kernel | 1.1.0 | |
| MIGraphX | 2.13.0 | |||
| MIOpen | 3.5.0 | |||
| MIVisionX | 3.3.0 | |||
| rocAL | 2.3.0 | |||
| rocDecode | 1.0.0 | |||
| rocJPEG | 1.1.0 | |||
| rocPyDecode | 0.6.0 | |||
| RPP | 2.0.0 | |||
| Communication | RCCL | 2.26.6 ⇒ 2.26.6 | ||
| rocSHMEM | 3.0.0 | |||
| Math | hipBLAS | 3.0.0 ⇒ 3.0.2 | ||
| hipBLASLt | 1.0.0 | |||
| hipFFT | 1.0.20 | |||
| hipfort | 0.7.0 | |||
| hipRAND | 3.0.0 | |||
| hipSOLVER | 3.0.0 | |||
| hipSPARSE | 4.0.1 | |||
| hipSPARSELt | 0.2.4 | |||
| rocALUTION | 4.0.0 | |||
| rocBLAS | 5.0.0 ⇒ 5.0.2 | |||
| rocFFT | 1.0.34 | |||
| rocRAND | 4.0.0 | |||
| rocSOLVER | 3.30.0 ⇒ 3.30.1 | |||
| rocSPARSE | 4.0.2 ⇒ 4.0.3 | |||
| rocWMMA | 2.0.0 | |||
| Tensile | 4.44.0 | |||
| Primitives | hipCUB | 4.0.0 | ||
| hipTensor | 2.0.0 | |||
| rocPRIM | 4.0.0 ⇒ 4.0.1 | |||
| rocThrust | 4.0.0 | |||
| Tools | System management | AMD SMI | 26.0.0 ⇒ 26.0.1 | |
| ROCm Data Center Tool | 1.1.0 | |||
| rocminfo | 1.0.0 | |||
| ROCm SMI | 7.8.0 | |||
| ROCm Validation Suite | 1.2.0 | |||
| Performance | ROCm Bandwidth Test | 2.6.0 | ||
| ROCm Compute Profiler | 3.2.3 | |||
| ROCm Systems Profiler | 1.1.0 ⇒ 1.1.1 | |||
| ROCProfiler | 2.0.0 | |||
| ROCprofiler-SDK | 1.0.0 | |||
| ROCTracer | 4.1.0 | |||
| Development | HIPIFY | 20.0.0 | ||
| ROCdbgapi | 0.77.3 ⇒ 0.77.4 | |||
| ROCm CMake | 0.14.0 | |||
| ROCm Debugger (ROCgdb) | 16.3 | |||
| ROCr Debug Agent | 2.1.0 | |||
| Compilers | HIPCC | 1.1.1 | ||
| llvm-project | 20.0.0 | |||
| Runtimes | HIP | 7.0.0 ⇒ 7.0.2 | ||
| ROCr Runtime | 1.18.0 | |||