ROCm 7.2.1 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Supported hardware, operating system, and virtualization changes

• User space, driver, and firmware dependent changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm resolved issues

• ROCm upcoming changes

• ROCm binaries

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 7.2.1. For changes to individual components, see
Detailed component changes.

Supported hardware, operating system, and virtualization changes

Hardware support remains unchanged in this release.

ROCm 7.2.1 adds support for Ubuntu 24.04.4 (kernel: 6.8 [GA], 6.17 [HWE]) and marks end of support (EoS) for Ubuntu 24.04.3. For more information, see Ubuntu installation.

For more information about:

• AMD hardware, see Supported GPUs (Linux).

• Operating systems, see Supported operating systems and ROCm installation for Linux.

Virtualization support

Virtualization support remains unchanged in this release. For more information, see Virtualization support.

User space, driver, and firmware dependent changes

The software for AMD Data Center GPU products requires maintaining a hardware
and software stack with interdependencies among the GPU and baseboard
firmware, AMD GPU drivers, and the ROCm user space software. 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 the Integrated Firmware Image (IFWI).

GPU and baseboard firmware versioning might differ across GPU families.

hipBLASLt updates

hipBLASLt has improved performance for MXFP8 and MXFP4 GEMMs.

Deep learning and AI framework updates

ROCm provides a comprehensive ecosystem for deep learning development. For more information, see Deep learning frameworks for ROCm and the Compatibility
matrix for the complete list of Deep learning and AI framework versions tested for compatibility with ROCm. AMD ROCm has officially updated support for the following Deep learning and AI frameworks:

JAX

ROCm 7.2.1 enables support for JAX 0.8.2. For more information, see JAX compatibility.

ROCm Offline Installer Creator discontinuation

The ROCm Offline Installer Creator is discontinued in ROCm 7.2.1. Equivalent installation capabilities are available through the ROCm Runfile Installer, a self-extracting installer that is not based on OS package managers. For more information, see ROCm Runfile Installer.

ROCm documentation updates

ROCm documentation continues to be updated to provide clearer and more comprehensive guidance for a wider range of user needs and use cases.

• Tutorials for AI developers have been expanded with the following two new tutorials:

  • Pretraining tutorial: SE(3)-Transformer overview
  • Fine-tuning tutorial: GRPO with slime

  For more information about the changes, see the Changelog for the AI Developer Hub.

• HIP documentation has been expanded with additional context and in-depth explanations across several core topics in the Programming Guide section. The following topics have been significantly enhanced:

  • Compilers
  • Programming model
  • Performance guidelines
  • [Performance optimi...

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Supported hardware, operating system, and virtualization changes

• User space, driver, and firmware dependent changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm resolved issues

• ROCm upcoming changes

• ROCm binaries

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 7.2.0. For changes to individual components, see
Detailed component changes.

Supported hardware, operating system, and virtualization changes

ROCm 7.2.0 adds support for RDNA4 architecture-based AMD Radeon AI PRO R9600D and AMD Radeon RX 9060 XT LP, and RDNA3 architecture-based AMD Radeon RX 7700 GPUs.

ROCm 7.2.0 extends the SLES 15 SP7 operating system support to AMD Instinct MI355X and MI350X GPUs.

For more information about:

• AMD hardware, see Supported GPUs (Linux).

• Operating systems, see Supported operating systems and ROCm installation for Linux.

Virtualization support

Virtualization support remains unchanged in this release. For more information, see Virtualization support.

User space, driver, and firmware dependent changes

The software for AMD Data Center GPU products requires maintaining a hardware
and software stack with interdependencies among the GPU and baseboard
firmware, AMD GPU drivers, and the ROCm user space software. 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 the Integrated Firmware Image (IFWI).

GPU and baseboard firmware versioning might differ across GPU families.

[1]: For AMD Instinct MI325X KVM SR-IOV users, don't use AMD GPU driver (amdgpu) 30.20.0.

[2]: For AMD Instinct MI300X KVM SR-IOV with Multi-VF (8 VF) support requires a compatible firmware BKC bundle which will be released in coming months.

Node power management for multi-GPU nodes added

Node Power Management (NPM) optimizes power allocation and GPU frequency across multiple GPUs within a node using built-in telemetry and advanced control algorithms. It dynamically scales GPU frequencies to keep total node power within limits. Use AMD SMI to verify whether NPM is enabled and to check the node’s power allocation. This feature is supported on AMD Instinct MI355X and MI350X GPUs in both bare-metal and KVM SR-IOV virtual environments when paired with PLDM bundle 01.25.17.07. See the AMD SMI changelog for details.

Model optimization for AMD Instinct MI350 Series GPUs

The following models have been optimized for AMD Instinct MI350 Series GPUs:

• Significant performance optimization has been achieved for the Llama 3.1 405B model on AMD Instinct MI355X GPUs, delivering enhanced throughput and reduced latency through kernel-level tuning and memory bandwidth improvements. These changes leverage MI355X’s advanced architecture to maximize efficiency for large-scale inference workloads.
• Optimized Llama 3.1 405B model performance on AMD Instinct MI355X GPUs.
• Optimized Llama 3 70B and Llama 2 70B model performance on AMD Instinct MI355X and MI350X GPUs.

Model optimization for AMD Instinct MI300X GPUs

The following models have been optimized for AMD Instinct MI300X GPUs:

• GEMM-level optimization for the GLM-4.6 model.
• DeepEP performance improvements.

HIP runtime performance improvements

Graph node scaling

HIP runtime now implements an optimized doorbell ring mechanism for certain graph execution topologies. It enables efficient batching of graph nodes. This enhancement provides better alignment with NVIDIA CUDA Graph optimizations.

HIP also adds a new performance test for HIP graphs with programmable topologies to measure graph performance across different structures. The test evaluates graph instantiation time, first-launch time, repeat launch times, and end-to-end execution for various graph topologies. The test implements comprehensive timing measurements, including CPU overhead and device execution time.

Back memory set (memset) optimization

HIP runtime now implements a back memory set (memset) optimization to improve how memset nodes are processed during graph execution. This enhancement specifically handles varying numbers of AQL (Architected Queue Language) packets for memset graph node due to graph node set params for AQL batch submission approach.

Async handler performance improvement

HIP runtime has removed the lock contention in async handler enqueue path. This enhancement reduces runtime overhead and maximizes GPU throughput, for asynchronous kernel execution, especially in multi-threaded applications.

HIP APIs added

To simplify cross-platform programming and improve code portability between AMD ROCm and other programming models, new HIP APIs have been added in ROCm 7.2.0.

HIP library management APIs

The following new HIP library management APIs have been added:

• hipLibraryGetKernel, gets a kernel from library.
• hipLibraryGetKernelCount, gets kernel count in library.
• hipLibraryLoadData, creates library object from code.
• hipLibraryLoadFromFile, creates library object fro...

ROCm 7.1.1 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Supported hardware, operating system, and virtualization changes

• User space, driver, and firmware dependent changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm resolved issues

• ROCm upcoming changes

• ROCm binaries

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 7.1.1. For changes to individual components, see
Detailed component changes.

Supported hardware, operating system, and virtualization changes

ROCm 7.1.1 adds support for the following operating systems and kernel versions:

• RHEL 10.1 (kernel: 6.12.0-124)

• RHEL 9.7 (kernel: 5.14.0-611)

ROCm 7.1.1 extends the Debian 13 support to AMD Instinct MI355X and MI350X GPUs.

For more information about:

• AMD hardware, see Supported GPUs (Linux).

• Operating systems, see Supported operating systems and ROCm installation for Linux.

Virtualization support

ROCm 7.1.1 adds Ubuntu 24.04 as a Guest OS in KVM SR-IOV for AMD Instinct MI300X GPUs. For more information, see Virtualization Support.

User space, driver, and firmware dependent changes

The software for AMD Data Center GPU products requires maintaining a hardware
and software stack with interdependencies among the GPU and baseboard
firmware, AMD GPU drivers, and the ROCm user space software.

[1]: For AMD Instinct MI325X KVM SR-IOV users, don't use AMD GPU Driver (amdgpu) 30.20.0.

AMD Instinct MI355X and MI350X metrics and telemetry enhancements

AMD SMI now supports per-partition metrics and monitoring on AMD Instinct MI355X and MI350X
GPUs -- depending on PLDM bundle minimum version 01.25.16.03, including
reporting for thermal throttle limits and thermal alert thresholds. For AMD SMI
on bare metal, metrics per GPU partition are available through the library API:
amdsmi_get_gpu_partition_metrics_info(). See the AMD SMI
changelog for details.

AMD Instinct MI355X GPU resiliency improvement

Multimedia Engine Reset is now supported by the AMD GPU Driver (amdgpu) 30.20.1 for
AMD Instinct MI355X GPUs. This finer-grain GPU resiliency enables recovery from
faults related to VCN or JPEG without requiring a full GPU reset, thereby
improving system stability and fault tolerance. Note that VCN queue reset
functionality requires PLDM bundle 01.25.16.03 (or later) firmware.

AMD Instinct MI325X SR-IOV Mode 1 reset issue fixed

An issue affecting AMD Instinct MI325X GPUs in SR-IOV Mode 1 has been resolved
in AMD GPU Driver (amdgpu) version 30.20.1. This fix enables seamless usage
of KVM virtualization with SR-IOV configurations and allows users to proceed
with ROCm and AMD GPU Driver updates without encountering reset-related failures.

GEMM kernel selection improvement

GEMM kernel selection efficiency has been improved using Origami. This results in improved out-of-the-box performance of GEMM functions for hipBLASLT and rocBLAS, as well as a reduced need for tuning. This improvement reduces selection time, increases selection accuracy, and adds Origami libraries for all GEMM problem types on AMD Instinct MI350X GPUs.

Performance improvement in CK/AITER fused-attn

Padding is now supported in native CK/AITER fused-attn mode, reducing the overall runtime. Previously, the Transformer Engine (TE) had to remove padding before processing and reapply it afterward as a workaround, which added runtime overhead. With this update, TE can now pass padded input directly to CK/AITER and receive padded output, eliminating the need for that workaround.

AI model support update

ROCm 7.1.1 updates the support for the following AI models:

• Hugging Face Transformers is now supported on gfx1201.
• Microsoft Phi-4-multimodal-instruct is now supported on gfx1201.
• Qwen QwQ-32B is now supported on gfx1201.
• Google Gemma 3 27B is now supported on gfx1100.

ROCm Data Science updates

ROCm Data Science Toolkit (ROCm-DS) is a comprehensive open-source software collection designed to accelerate data science and machine learning workloads on AMD GPUs. In November 2025, ROCm-DS transitioned from early access (EA) to general availability (GA).

This GA release marks a significant milestone for ROCm-DS as hipDF and hipMM transition to production status. Additionally, it introduces two new production components: hipRAFT and hipVS. For more information, see AMD ROCm-DS documentation.

Deep learning and AI framework updates

ROCm provides a comprehensive ecosystem for deep learning development. For more information, see Deep learning frameworks for ROCm and the Compatibility
matrix for the complete list of Deep learning and AI framework versions tested for compatibility with ROCm. As of November 2025, AMD ROCm has officially updated support for the following Deep learning and AI frameworks:

PyTorch

ROCm 7.1.1 enables support for PyTorch 2.9. For more information, see PyTorch compatibility.

Deep Graph Library (DGL)

Deep Graph Library (DGL) is an easy-to-use, high-performance, and scal...

ROCm 7.1.0 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Supported hardware, operating system, and virtualization changes

• User space, driver, and firmware dependent changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm resolved issues

• ROCm upcoming changes

• ROCm binaries

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 7.1.0. For changes to individual components, see
Detailed component changes.

Supported hardware, operating system, and virtualization changes

ROCm 7.1.0 extends the operating system support for the following AMD hardware:

• AMD Instinct MI325X adds support for RHEL 10.0, SLES15 SP7, Debian 13, Debian 12, Oracle Linux 10, and Oracle Linux 9.
• AMD Instinct MI100 adds support for SLES 15 SP7.

For more information about supported:

• AMD hardware, see Supported GPUs (Linux).

• Operating systems, see Supported operating systems and ROCm installation for Linux.

Virtualization support

ROCm 7.1.0 adds Guest OS support for RHEL 10.0 in KVM SR-IOV for AMD Instinct MI355X and MI350X GPUs.

For more information, see 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.

AMD SMI improvement: Set power cap

AMD Instinct MI300X now supports setting a power cap in 1VF. The system is designed to select the lowest power cap value from those provided by the host, VM, and Advanced Platform Management Link (APML). This feature provides enhanced control over power management in virtualized environments, particularly in single-VM configurations. By allowing the VM to set a power cap, you can optimize power usage and efficiency for your specific needs. This feature requires PLDM bundle 01.25.05.00 (or later) firmware.

Virtualization update for AMD Instinct MI350 Series GPUs

• Enabled SPX/NPS1 support for multi-tenant (1VM, 2VM, 4VM, and 8VM). This feature depends on PLDM bundle 01.25.15.04.

• Enabled CPX/NPS2 support (1VF/OAM). This feature depends on PLDM bundle 01.25.15.04. (Technical preview)

• Enabled DPX/NPS2 support (1VF/OAM). This feature depends on PLDM bundle 01.25.15.04.

• Enabled Guest OS support for RHEL 10 and RHEL 9.6. This feature depends on PLDM bundle 01.25.15.04.

HIP runtime compatibility improvements

ROCm 7.1.0 improves the compatibility between the HIP runtime and NVIDIA CUDA.

• New HIP APIs added for:

  • Memory management: hipMemsetD2D8, hipMemsetD2D8Async, hipMemsetD2D16, hipMemsetD2D16Async, hipMemsetD2D32, hipMemsetD2D32Async, hipMemcpyBatchAsync, hipMemcpy3DBatchAsync, hipMemcpy3DPeer, hipMemcpy3DPeerAsync, hipMemPrefetchAsync_v2, and hipMemAdvise_v2.
  • Module Management:hipModuleGetFunctionCoun and hipModuleLoadFatBinary
  • Stream Management: hipStreamSetAttribute, hipStreamGetAttribute, and hipStreamGetId
  • Device Management: hipSetValidDevices
  • Driver Entry Point Access: hipGetDriverEntryPoint

• HIP runtime now supports nested tile partitioning within cooperative groups, matching CUDA functionality.

• Improved HIP module loading latency.

For detailed enhancements and updates refer to the HIP Changelog.

hipBLASLt: Kernel optimizations and model support enhancements

hipBLASLt introduces several performance and model compatibility improvements for AMD Instinct GPUs:

• TF32 kernel optimization for AMD Instinct MI355X GPUs to enhance training and inference efficiency.
• FP32 kernel optimization for AMD Instinct MI350X GPUs, improving precision-based workloads.
• Llama 2 70B model support fix for AMD Instinct MI350X GPUs: Removed incorrect kernel to ensure accurate and stable execution.
• For AMD Instinct MI350X GPUs, added multiple high-performance kernels optimized for FP16 and BF16 data types, enhancing heuristic-based execution.
• FP8 low-precision data type operations on AMD Instinct MI350X GPUs. This update adds FP8 support for the Instinct MI350X using the hipBLASLt low-precision data type functionality.
• Mixtral-8x7b model optimization for AMD Instinct MI325X GPUs.

hipSPARSELt: SpMM performance improvements

hipSPARSELt introduces significant performance enhancements for structured sparsity matrix multiplication (SpMM) on AMD Instinct MI300X GPUs:

• New feature support -- Enabled multiple buffer single kernel execution for SpMM, improving efficiency in Split-K method scenarios.
• Kernel optimization -- Added multiple high-performance kernels optimized for FP16 and BF16 data types, enhancing heuristic-based execution.
• Tuning efficiency -- Improved the tuning process for SpMM kernels, resulting in better runtime adaptability and performance.

rocAL: Enhancements for vision transformer model training

ROCm 7.1.0 introduces new capabilities in rocAL to support training of Vision Transformer (ViT) models:

• Added support for CropResize augmentation and the CIFAR10 dataloader, commonly used in ViT training workflows.
• These updates enable seamless integration of rocAL into open-source PyTorch Vision Transformer models.

This enhancement improves preprocessing efficiency and simplifies the setup of data pipelines for ViT-based deep learning applications.

RCCL: AMD Instinct MI350 Series enhancements

• Optimized performance for select collective operations.
• Enhanced single-node performance on AMD Instinct MI350 GPUs.
• Achieved higher throughput with increased XGMI speed.
  ...

ROCm Core SDK 7.9.0 release notes

ROCm Core SDK 7.9.0 introduces a technology preview release aimed at helping
developers explore the new ROCm build and release infrastructure system called
TheRock. See ROCm Core SDK and TheRock Build System for more information.
This release focuses on foundational improvements and streamlining the development experience.

Release highlights

This technology preview of the ROCm Core SDK with TheRock introduces several
key foundational changes:

• ManyLinux_2_28 compliance: Enables single builds to support multiple Linux distributions, improving portability and simplifying deployment.
• Architecture-specific Python packages: Redesigned to target individual GPU architectures, reducing disk usage and improving modularity.
• Slimmed-down SDK: Focuses on core GPU compute capabilities with a minimal set of runtime components, libraries, and tools.

In addition to these technical updates, this release also begins the transition
to a more open and predictable development process:

• Open release process: Transition to a fully open model with public release candidates, nightly builds, and transparent pull request workflows.
• Predictable release cadence: Major and minor versions will follow a fixed 6-week release cycle.

7.9.0 compatibility notice

In terms of package compatibility, ROCm 7.9.0 diverges from the existing ROCm
7.0 stream and upcoming stable releases in that stream:

• No upgrade path from existing production releases -- including ROCm 7.0 and earlier -- as well as from upcoming stable releases. See the explanatory note.
• Not intended for production workloads -- users running production environments should continue using the ROCm 7.0 stream.
  See the explanatory note.
• Not fully featured -- this release is a stepping stone toward fully open software development.

7.9.0 support

• Hardware support: Builds are limited to AMD Instinct MI350 Series GPUs, MI300 Series GPUs and APUs, Ryzen AI Max+ PRO 300 Series APUs, and Ryzen AI Max 300 Series APUs. See Supported hardware and operating systems.
• Packaging format: RPM and Debian packages are not available in this initial release. Instead, Python wheels and tarballs are provided. See the ROCm 7.9.0 installation instructions.
• Software components: Some components of the ROCm Core SDK are not yet
  available in this release. Additional components are planned to be introduced in
  future preview releases as part of the ROCm Core SDK. Components not included in
  the future Core SDK will either:
  • Be released as standalone project-specific packages, or
  • Be grouped into ROCm Expansion SDKs.

Looking ahead

Subsequent technology preview releases will follow a 6-week cadence, gradually
filling gaps and introducing new ROCm expansions. AMD will continue to maintain
traditional ROCm releases in parallel with the 7.9+ preview stream.

Supported hardware and operating systems

ROCm 7.9.0 supports the following AMD Instinct GPUs and Ryzen AI
APUs. Each supported device is listed with its corresponding GPU architecture,
LLVM target, and supported operating systems.

Supported kernel driver and firmware bundles

ROCm depends on a coordinated stack of compatible firmware, driver, and user
space components. Maintaining version alignment between these layers ensures correct GPU
operation and performance, especially for AMD data center products.
While AMD publishes drivers and ROCm user space components, your server or
infrastructure provider publishes the GPU and baseboard firmware by bundling
AMD firmware releases through Platform Level Data Model (PLDM) bundles --
which include the Integrated Firmware Image (IFWI).

Deep learning frameworks

ROCm 7.9.0 supports PyTorch 2.7.1 on Linux and PyTorch 2.9.0 on Windows.

ROCm Core SDK components

The following table lists core components included in the ROCm 7.9.0 release.
Expect future releases in this stream to expand the list of components.

ROCm 7.0.2 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Supported hardware, operating system, and virtualization changes

• User space, driver, and firmware dependent changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm upcoming changes

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.

Supported hardware, operating system, and virtualization changes

ROCm 7.0.2 adds support for the RDNA4 architecture-based AMD Radeon RX 9060. For more information about supported AMD hardware, see Supported GPUs (Linux).

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 and install instructions.

Virtualization support

Virtualization support remains unchanged in this release. For more information, see 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.

[1]: PLDM bundle 01.25.05.00 will be available by October 31, 2025.

AMD Instinct MI300X GPU resiliency improvement

Multimedia Engine Reset is now supported in AMD GPU Driver (amdgpu) 30.10.2 for AMD Instinct MI300X GPUs. This finer-grain GPU resiliency feature allows recovery from faults related to VCN or JPEG without requiring a full GPU reset, thereby improving system stability and fault tolerance. Note that VCN queue reset functionality requires PLDM bundle 01.25.05.00 (or later) firmware.

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.

gsplat support enabled for ROCm

Gaussian splatting (gsplat) is an open-source library for GPU-accelerated differentiable rasterization of 3D Gaussians with Python bindings. This ROCm-enabled release of gsplat is built on top of PyTorch for ROCm, enabling 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 on ROCm, see installation instructions.

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.

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.

• MONAI for AMD ROCm, a ROCm-enabled version of MONAI, is built on top of PyTorch for AMD ROCm, 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.

Deep learning and AI framework updates

ROCm provides a comprehensive ecosystem for deep learning development. For more information, see Deep learning frameworks for ROCm and the Compatibility
matrix 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-perf...

ROCm 7.0.1 release notes

ROCm 7.0.1 is a quality release that resolves the issue listed in the Release highlights.

Release highlights

The following issue has been resolved in the AMD GPU Driver (amdgpu) 30.10.1 to be used with ROCm 7.0.1.

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

The issue of failing to declare Out-Of-Band Common Platform Error Records (CPERs) when exceeding bad memory page threshold has been resolved. The fix applies to all AMD Instinct MI300 Series and MI350 Series GPUs.

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 7.0.0 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Operating system, hardware, and virtualization support changes

• User space, driver, and firmware dependent changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm resolved issues

• ROCm upcoming changes

Release highlights

The following are notable new features and improvements in ROCm 7.0.0. For changes to individual components, see
Detailed component changes.

Operating system, hardware, and virtualization support changes

ROCm 7.0.0 adds support for AMD Instinct MI355X and MI350X. For details, see the full list of Supported GPUs (Linux).

ROCm 7.0.0 adds support for the following operating systems and kernel versions:

• Ubuntu 24.04.3 (kernel: 6.8 [GA], 6.14 [HWE])
• Rocky Linux 9 (kernel: 5.14.0-570)

ROCm 7.0.0 marks the end of support (EoS) for Ubuntu 24.04.2 (kernel: 6.8 [GA], 6.11 [HWE]) and SLES 15 SP6.

For more information about supported operating systems, see Supported operating systems and install instructions.

See the Compatibility matrix for more information about operating system and hardware compatibility.

Virtualization support

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 for more details. In addition, support for VMware ESXi 8 has been introduced for AMD Instinct MI300X GPUs. For more information, see Virtualization Support.

Deep learning and AI framework updates

ROCm provides a comprehensive ecosystem for deep learning development. For more information, see Deep learning frameworks for ROCm and the Compatibility
matrix 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 7.0.0 enables the following PyTorch features:

• Support for PyTorch 2.7.
• Integrated Fused Rope kernels in APEX.
• Compilation of Python C++ extensions using amdclang++.
• Support for channels-last NHWC format for convolutions via MIOpen.

JAX

ROCm 7.0.0 enables support for JAX 0.6.0.

Megatron-LM

Megatron-LM for ROCm now supports:

• Fused Gradient Accumulation via APEX.

• Fused Rope Kernel in APEX.

• Fused_bias_swiglu kernel.

TensorFlow

ROCm 7.0.0 enables support for TensorFlow 2.19.1.

ONNX Runtime

ROCm 7.0.0 enables support for ONNX Runtime 1.22.0.

vLLM

• Support for Open Compute Project (OCP) FP8 data type.
• FP4 precision for Llama 3.1 405B.

Triton

ROCm 7.0.0 enables support for Triton 3.3.0.

New frameworks

AMD ROCm has officially added support for the following Deep learning and AI frameworks:

• Ray is a unified framework for scaling AI and Python applications from your laptop to a full cluster, without changing your code. Ray consists of a core distributed runtime and a set of AI libraries for simplifying machine learning computations. It is currently supported on ROCm 6.4.1. For more information, see Ray compatibility.

• 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 currently supported on ROCm 6.4.0. For more information, see llama.cpp compatibility.

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. The first release is designated as AMD GPU Driver (amdgpu) version 30.10. See the User and kernel-space support matrix for more information.

AMD SMI continues to stay with the ROCm software stack under the ROCm organization repository.

Consolidation of ROCm library repositories

The following ROCm library repositories are migrating from multiple repositories under {fab}github ROCm to a single repository under {fab}github rocm-libraries in the ROCm organization GitHub: hipBLAS, hipBLASLt
, hipCUB, hipFFT, hipRAND, hipSPARSE, hipSPARSELt, MIOpen, rocBLAS, rocFFT, rocPRIM, rocRAND, rocSPARSE, rocThrust, and Tensile.

Use the new ROCm Libraries repository to access source code, clone projects, and contribute to the code base and documentation.The change helps to streamline development, CI, and integration. For more information about working with the ROCm Libraries repository, see Contributing to the ROCm Libraries in GitHub.

Other ROCm libraries are also in the process of migration along with ROCm tools to {fab}github rocm-systems. For latest status information, see the README file. The official completion of migration will be communicated in a future ROCm release.

HIP API compatibility improvements

To improve code portability between AMD ROCm and other programming models, HIP API has been updated in ROCm 7.0.0 to simplify cross-platform programming. These changes are incompatible with prior ROCm releases and might require recompiling existing HIP applications for use with ROCm 7.0.0. For more information, see the HIP API 7.0.0 changes and the HIP changelog below.

HIP runtime updates

The HIP runtime now includes support for:

• Open Compute Project (OCP) MX floating-point FP4, FP6, and FP8 data types and APIs.
• Improved logging by adding more precise pointer information and launch arguments for better tracking and debugging in dispatch met...

ROCm 6.4.3 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Operating system and hardware support changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm upcoming changes

If you’re using AMD Radeon™ PRO or Radeon GPUs in a workstation setting with a display connected, see the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/native_linux/native_linux_compatibility.html)
documentation to verify compatibility and system requirements.

Release highlights

ROCm 6.4.3 is a quality release that resolves the following issues. For changes to individual components, see Detailed component changes.

AMDGPU driver updates

• Resolved an issue causing performance degradation in communication operations, caused by increased latency in certain RCCL applications. The fix prevents unnecessary queue eviction during the fork process.
• Fixed an issue in the AMDGPU driver’s scheduler constraints that could cause queue preemption to fail during workload execution.

ROCm SMI update

• Fixed the failure to load GPU data like System Clock (SCLK) by adjusting the logic for retrieving GPU board voltage.

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 have been expanded with the following five new tutorials:

  • Inference tutorials
    • ChatQnA vLLM deployment and performance evaluation
    • Text-to-video generation with ComfyUI
    • DeepSeek Janus Pro on CPU or GPU
    • DeepSeek-R1 with vLLM V1
  • GPU development and optimization tutorial: MLA decoding kernel of AITER library

  For more information about the changes, see Changelog for the AI Developer Hub.

• ROCm provides a comprehensive ecosystem for deep learning development. For more details, see Deep learning frameworks for ROCm. AMD ROCm adds support for the following deep learning frameworks:

  • Taichi 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. It is currently supported on ROCm 6.3.2. For more information, see Taichi compatibility.
  • Megablocks is a light-weight library for mixture-of-experts (MoE) training. The core of the system is efficient "dropless-MoE" and standard MoE layers. Megablocks is integrated with Megatron-LM, where data and pipeline parallel training of MoEs is supported. It is currently supported on ROCm 6.3.0. For more information, see Megablocks compatibility.

• The Data types and precision support topic now includes new hardware and library support information.

Operating system and hardware support changes

Operating system and hardware support remain unchanged in this release.

See the Compatibility matrix for more information about operating system and hardware compatibility.

ROCm components

The following table lists the versions of ROCm components for ROCm 6.4.3.
Click {fab}github to go to the component's source code on GitHub.

ROCm 6.4.2 release notes

The release notes provide a summary of notable changes since the previous ROCm release.

• Release highlights

• Operating system and hardware support changes

• ROCm components versioning

• Detailed component changes

• ROCm known issues

• ROCm resolved issues

• ROCm upcoming changes

If you’re using AMD Radeon™ PRO or Radeon GPUs in a workstation setting with a display connected, see the [Use ROCm on Radeon GPUs](https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/native_linux/native_linux_compatibility.html)
documentation to verify compatibility and system requirements.

Release highlights

The following are notable new features and improvements in ROCm 6.4.2. For changes to individual components, see
Detailed component changes.

ROCm Compute Profiler enhancements

ROCm Compute Profiler includes the following changes:

• The --roofline-data-type option now supports FP8, FP16, BF16, FP32, FP64, I8, I32, and I64 data types. This is dependent on the GPU architecture. For more information, see Roofline options.

• ROCm Compute Profiler now uses AMD SMI instead of ROCm SMI. The AMD System Management Interface Library (AMD SMI) is a successor to ROCm SMI. It is a unified system management interface tool that provides a user-space interface for applications to monitor and control GPU applications and gives users the ability to query information about drivers and GPUs on the system. For more information, see https://github.com/ROCm/amdsmi and the AMD SMI documentation.

• ROCm Compute Profiler has added 8-bit floating point (FP8) metrics support for AMD Instinct MI300 series accelerators. For more information, see System Speed-of-Light.

rocSOLVER enhancements

rocSOLVER has improved the performance of eigensolvers and singular value decomposition (SVD). For more information, see rocSOLVER documentation.

ROCm Offline Installer Creator updates

The ROCm Offline Installer Creator 6.4.2 includes the following features and improvements:

• Added support for Oracle Linux 8.10 and 9.6, and SLES 15 SP7.
• Additional package options for the Offline Installer Creator, including amd-smi, rocdecode, rocjpeg, and rdc.
• ROCm meta packages are now used for selecting ROCm components and use cases.
• Improved separation of kernel/driver and ROCm prerequisite packages to reduce the size of ROCm-only or driver-only offline installers.

In addition, the option to build an offline installer based on ROCm version 5.7.3 has been removed. To build an offline installer for ROCm 5.7.3, use the Offline Installer Creator from version 6.4.1 or earlier. See ROCm Offline Installer Creator for more information.

ROCm Runfile Installer updates

The ROCm Runfile Installer 6.4.2 adds support for Oracle Linux 8.10 and 9.6 (using the RHEL 8 or 9 .run files), Debian 12 (using the Ubuntu 22.04 .run file), and SLES 15 SP7. It also fixes permission settings issues during ROCm and AMDGPU driver installation. For more information, see ROCm Runfile Installer.

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 have been expanded with the following four new tutorials:

  • Inference tutorial: AI agent with MCPs using vLLM and PydanticAI
  • GPU development and optimization tutorials:
    • Kernel development and optimization with Triton
    • Profiling Llama-4 inference with vLLM
    • FP8 quantization with AMD Quark for vLLM

  For more information about the changes, see Changelog for the AI Developer Hub.

• ROCm provides a comprehensive ecosystem for deep learning development. For more details, see Deep learning frameworks for ROCm. As of July 2025, AMD ROCm provides support for the following additional deep learning frameworks:

  • Deep Graph Library is an easy-to-use, high-performance, and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component in an end-to-end application, the rest of the logic is implemented using PyTorch. It is currently supported on ROCm 6.4.0. For more information, see DGL compatibility.
  • Stanford Megatron-LM is a large-scale language model training framework. It’s designed to train massive transformer-based language models efficiently by model and data parallelism. It is currently supported on ROCm 6.3.0. For more information, see Stanford Megatron-LM compatibility.
  • Volcano Engine Reinforcement Learning for LLMs (verl) is a reinforcement learning framework designed for large language models (LLMs). verl offers a scalable, open-source fine-tuning solution optimized for AMD Instinct GPUs with full ROCm support. It is currently supported on ROCm 6.2.0. For more information, see verl compatibility.

• Documentation for the new ROCprof Compute Viewer was added in May 2025. This tool is used to visualize and analyze GPU thread trace data collected using rocprofv3. Note that ROCprof Compute Viewer is in an early access state. Running production workloads is not recommended.

• The AMDGPU installer documentation has been removed to encourage the use of the package manager for ROCm installation. While the package manager is the recommended method, you can still install ROCm using the AMDGPU installer by following the legacy process. Ensure to update the command with the intended ROCm version before running it. For more information, see Installation via native package manager.

Operating system and hardware support changes

ROCm 6.4.2 adds support for SLES 15 SP7. For more information, see SLES installation.

ROCm 6.4.2 marks the end of support (EoS) for RHEL 9.5.

ROCm 6.4.2 adds support for RDNA3 architecture-based Radeon RX 7700 XT GPU. This GPU is supported on Ubuntu 24.04.2 and RHEL 9.6.
For details, see the full list of Supported GPUs
(Linux).

See the Compatibility
matrix
for more information about operating system and hardware compatibility.

ROCm components

The following table lists the versions of ROCm components for ROCm 6.4.2, including any version
changes from 6.4.1 to 6.4.2. 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.