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ROCm 7.14.0 Release

ROCm 7.14.0 Release Latest
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@alexxu-amd alexxu-amd released this 16 Jul 03:28
rocm-7.14.0
830cc1b
This commit was created on GitHub.com and signed with GitHub’s verified signature.
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ROCm Core SDK 7.14.0 release notes

ROCm Core SDK 7.14.0 transitions ROCm to TheRock, a build and release system that introduces a modular architecture to improve flexibility, maintainability, and alignment with community use cases:

  • Leaner core: The Core SDK focuses on essential runtime and development components.
  • Use case-specific expansions: Optional domain-specific SDKs for AI, data science, and HPC.
  • Modular installation: Install only the components required for your workflow.

This approach streamlines installation, reduces footprint, and accelerates innovation through independently released packages. To learn more, see the transition guide.

ROCm 7.14.0 follows the versioning discontinuity that began with the 7.9.0 preview release.

Release highlights

This release focuses on AI inference, distributed workloads, and profiling across AMD Instinct™, Radeon™, and Ryzen™ AI platforms. Highlights include inference-ready vLLM images and packages, ROCprofiler-SDK adoption across AI profiling workflows, expanded system telemetry and validation coverage, and updates to math, sparse, and communication libraries.

Platform and hardware support

This release expands GPU, operating system, virtualization, and partitioning support.

Expanded AMD GPU support

ROCm 7.14.0 adds support for the following AMD APUs:

For the complete list of supported AMD hardware, see AMD hardware support.

Expanded operating system support

ROCm 7.14.0 adds support for RHEL 10.2 and RHEL 9.8 on AMD Instinct and Radeon GPUs. RHEL 10.2 replaces RHEL 10.1 as the validated RHEL 10 release; RHEL 9.8 replaces RHEL 9.7 as the validated RHEL 9 release.

SUSE Linux Enterprise Server (SLES) 15 SP7, SLES 16, and Debian 13 are now supported on AMD Instinct MI350P.

For the full list of supported Linux distributions, see Operating system support.

Expanded GPU virtualization support for Instinct GPUs

GPU virtualization configurations supported on AMD Instinct GPUs remain unchanged from the previous ROCm 7.13.0 preview release.

Supported Single Root I/O Virtualization (SR-IOV) configurations require the AMD GPU Virtualization Driver (GIM) 9.1.0.K. For details, see GPU virtualization support.

Expanded Instinct GPU partitioning support

ROCm 7.14.0 has enabled and optimized multi-VF partition modes for the following GPU partitioning configurations in SR-IOV deployments:

On MI355X and MI350X:

DPX compute partition mode with NPS2 memory partitioning.

CPX compute partition mode with NPS2 memory partitioning.

For details, see GPU partitioning support.

AI inference and frameworks

This release enables support for the following frameworks:

  • PyTorch 2.12.0
  • JAX 0.10.0
  • vLLM 0.23.0
  • SGLang 0.5.13
  • TensorFlow 2.21

The updated framework support replaces the previous PyTorch 2.9.1, JAX 0.8.2, vLLM 0.19.1, and SGLang 0.5.9 support.

For details, see AI ecosystem support.

Developer tools, profiling, and validation

This release improves ROCm developer workflows with new HIP APIs, expanded profiling and tracing capabilities, and broader telemetry coverage.

HIP feature highlights

The following are notable enhancements to HIP:

  • HIP execution context support: HIP now supports Execution Context APIs, enabling GPU compute resource partitioning and lightweight execution-context management on a single device. These APIs allow you to query and partition device resources (primarily CU count for HIP runtime), create execution contexts on resource subsets, and create streams and events scoped to those contexts. For more information, see Execution Context Management.

  • HIP API additions for CUDA parity:

    • Batch memory management: New batch asynchronous memory management APIs let applications discard (hipMemDiscardBatchAsync), prefetch (hipMemPrefetchBatchAsync), or combine both operations (hipMemDiscardAndPrefetchBatchAsync) across multiple memory ranges in a single call, reducing API call overhead. Both HIP runtime and HIP driver variants are available.

    • Library management: New library management APIs return the device pointer and size of a device global (hipLibraryGetGlobal) and the host pointer and size of a managed variable (hipLibraryGetManaged) defined in a hipLibrary_t, improving parity with CUDA library APIs.

  • Faster HIP graph replay for asynchronous memory allocations: HIP graph replay now reduces overhead for graphs that interleave asynchronous memory allocations with compute. Allocation nodes no longer block during replay. Physical memory is reused across nodes instead of being mapped and unmapped on each launch, eliminating the gaps between kernels this pattern previously caused. For background on HIP graphs, see Graph Management.

For more information, see the HIP section in the ROCm component changelogs.

ROCprofiler-SDK feature highlights

The following are notable enhancements to ROCprofiler-SDK:

ROCprofiler-SDK integration with PyTorch Profiler

Starting with PyTorch 2.12, rocprofiler-sdk is used as the ROCm profiling backend for PyTorch Profiler on supported ROCm configurations, replacing the legacy roctracer-based profiling path. This enables PyTorch users to collect GPU activity traces through the rocprofiler-sdk infrastructure and provides a stronger foundation for correctness, stability, and future profiling capabilities. The integration also positions PyTorch Profiler to benefit from additional rocprofiler-sdk capabilities as framework-level support continues to evolve.

ROCprofiler-SDK beta support for Streaming Performance Monitors

rocprofiler-sdk and rocprofv3 add beta support for Streaming Performance Monitors (SPM), enabling selected hardware counters to be sampled over time while workloads execute. Unlike traditional counter collection, which captures a single aggregated value per kernel dispatch, SPM provides time-resolved hardware counter data. This is useful for analyzing long-running workloads and training jobs where temporal behavior matters as much as aggregate metrics. ROCpd support is planned for a future release.

In ROCm 7.14.0, SPM support is available through the rocprofiler-sdk API and rocprofv3. To enable SPM in rocprofv3, use the --spm-beta-enabled flag or set the ROCPROFILER_SPM_BETA_ENABLED environment variable. For API-based usage, set ROCPROFILER_SPM_BETA_ENABLED.

Supported hardware: AMD Instinct MI300X, MI325X, MI350X, and MI355X GPUs.

For more information, see the SPM API reference guide and the SPM usage guide for rocprofv3.

SPM is a beta capability under active development and may affect system stability, including unexpected reboots. Do not use in production environments. See ROCm known issues for current limitations.

Selective ROCTx region profiling with counter collection

rocprofiler-sdk and rocprofv3 include support for profiling selected ROCTx regions, allowing users to focus profiling on specific application phases instead of collecting data for the entire workload. By inserting roctxProfilerPause and roctxProfilerResume markers in application code and using the --selected-regions option, only the GPU activity within the marked regions is captured. This helps reduce profiling noise and output size while making it easier to isolate performance behavior in targeted code paths. This is particularly useful for long-running workloads where full-execution traces are impractical.

Counter collection for selected regions is available in ROCm 7.14.0. For details on --selected-regions, including usage with RCCL collectives and ROCTx markers, see Using ROCprofiler-SDK ROCTx.

Improved attach and re-attach profiling workflows

rocprofiler-sdk and rocprofv3 improve attach-bas...

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ROCm 7.2.4 Release

ROCm 7.2.4 Release

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@alexxu-amd alexxu-amd released this 29 May 19:02
rocm-7.2.4
e0b62c2

ROCm 7.2.4 release notes

ROCm 7.2.4 is a quality release focused on performance and stability fixes for AI inference workloads on AMD Instinct GPUs.

Release highlights

The following are the notable changes in ROCm 7.2.4.

Reduced hipGraphLaunch latency for multi-list graphs

The HIP runtime's graph dispatch mechanism has been optimized, reducing launch latency for workloads using hipGraphLaunch with multi-list graph topologies.

Fixed H2D memory copy latency regression in CPX mode

HIP runtime synchronization behavior has been corrected on AMD Instinct MI300 Series GPUs in CPX mode, restoring latency to previous levels for inference workloads that run multiple HIP streams with concurrent memory copies.

Reduced ROCprofiler-SDK profiling overhead

Profiling stability has been improved for vLLM workloads traced with PyTorch torch.profiler using the ROCprofiler-SDK backend. The large, sporadic idle gaps that previously appeared between GPU kernels in the trace have been substantially reduced in common configurations, and the traces now more accurately reflect actual runtime behavior. Coverage may vary depending on model and parallelism settings.

Reduced copy overhead in MIGraphX concat operations

MIGraphX now recognizes ONNX models that concatenate the same tensor multiple times and avoids redundant device-side copies, improving inference throughput at small batch sizes for the affected model class on AMD Instinct MI300X GPUs.

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 the AMD Platform Level Data Model (PLDM) bundle, which includes the Integrated Firmware Image (IFWI).

GPU and baseboard firmware versioning might differ across GPU families.

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.2.4 MI355X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.x where x (0-4)
30.20.x where x (0-1)
30.10.x where x (0-2) 		8.7.1.K
MI350X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.x where x (0-4)
30.20.x where x (0-1)
30.10.x where x (0-2)
MI325X[1] 01.25.06.08
01.25.04.02 		30.30.x where x (0-4)
30.20.x where x (0-1)[1]
30.10.x where x (0-2)
6.4.z where z (0-3)
6.3.3
MI300X[2] 01.25.06.04
01.25.03.12
01.25.02.04		 30.30.x where x (0-4)
30.20.x where x (0-1)
30.10.x where x (0-2)
6.4.z where z (0–3)
6.3.3 		8.7.1.K
MI300A BKC 26.1 Not Applicable
MI250X IFWI 47 (or later)
MI250 MU5 w/ IFWI 75 (or later)
MI210 MU5 w/ IFWI 75 (or later) 8.7.1.K
MI100 VBIOS D3430401-037 Not Applicable

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

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

ROCm binaries

For detailed installation instructions, refer to ROCm installation on Linux. ROCm binaries for installation are located at repo.radeon.com and listed below:

ROCm 7.2.4 doesn't include any other significant changes or feature additions. For comprehensive changes, new features, and enhancements in ROCm 7.2.3, refer to the [ROCm 7.2.3 release notes](#rocm-7-2-3-release-notes) below.

ROCm 7.2.3 release notes

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

If you’re using AMD Radeon™ GPUs or Ryzen™ for graphics workloads, 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 7.2.3. For changes to individual components, see
Detailed component changes.

Supported hardware, operating system, and virtualization changes

Hardware, operating system, and virtualization support remains unchanged in this release.

For more information about:

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 bas...

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ROCm 7.2.3 Release

ROCm 7.2.3 Release

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@alexxu-amd alexxu-amd released this 04 May 22:08
rocm-7.2.3
14f8138

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

If you’re using AMD Radeon™ GPUs or Ryzen™ for graphics workloads, 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 7.2.3. For changes to individual components, see
Detailed component changes.

Supported hardware, operating system, and virtualization changes

Hardware, operating system, and virtualization support remains unchanged in this release.

For more information about:

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 the AMD Platform Level Data Model (PLDM) bundle, which includes the Integrated Firmware Image (IFWI).

GPU and baseboard firmware versioning might differ across GPU families.

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.2.3 MI355X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.x where x (0-3)
30.20.x where x (0-1)
30.10.x where x (0-2) 		8.7.1.K
MI350X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.x where x (0-3)
30.20.x where x (0-1)
30.10.x where x (0-2)
MI325X[1] 01.25.06.08
01.25.04.02 		30.30.x where x (0-3)
30.20.x where x (0-1)[1]
30.10.x where x (0-2)
6.4.z where z (0-3)
6.3.3
MI300X[2] 01.25.06.04
01.25.03.12
01.25.02.04		 30.30.x where x (0-3)
30.20.x where x (0-1)
30.10.x where x (0-2)
6.4.z where z (0–3)
6.3.3 		8.7.1.K
MI300A BKC 26.1 Not Applicable
MI250X IFWI 47 (or later)
MI250 MU5 w/ IFWI 75 (or later)
MI210 MU5 w/ IFWI 75 (or later) 8.7.1.K
MI100 VBIOS D3430401-037 Not Applicable

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

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

Improved profiling accuracy for vLLM workloads

ROCm 7.2.3 improves profiling stability for vLLM workloads traced with PyTorch torch.profiler. The large, sporadic idle gaps that previously appeared between GPU kernels in the trace have been substantially reduced in common configurations, and the traces now more accurately reflect actual runtime behavior. Coverage may vary depending on model and parallelism settings; additional improvements are in progress.

MIGraphX update

MIGraphX has the following enhancements:

Improved performance of the Gather operator

Performance for embedding‑heavy inference workloads is improved by merging multiple independent gather operations from similar embedding tables into a single batched operation. Multi‑gather workloads now run more efficiently with fewer kernel launches and reduced memory traffic by adding horizontal fusion for cross-embedding gather operators. These gather operators have been updated to use transpose/reshape/broadcast/slice, enabling better optimization across different backends and data layouts.

ONNX Runtime reliability improvement

ONNX Runtime workloads accelerated with MIGraphX now provide a more reliable experience through external stream support in the MIGraphX Execution Provider, with improved memory allocation and deallocation for multi-stream inference.

ROCm documentation updates

ROCm documentation has been updated with ROCm XIO documentation. ROCm XIO provides an API for Accelerator-Initiated IO (XIO) for an AMD GPU __device__ code. It enables AMD GPUs to perform direct IO operations to hardware devices without CPU intervention. ROCm XIO was initially released in April 2026 as an early-access software technology preview. Running production workloads is not recommended.
For more information, see the ROCm XIO documentation and {fab}github ROCm/rocm-xio GitHub repository.

ROCm components

The following table lists the versions of ROCm components for ROCm 7.2.3, including any version
changes from 7.2.2/7.2.1 to 7.2.3. 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 1.2.0
MIGraphX 2.15.0 ⇒ 2.15.0
MIOpen 3.5.1
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ROCm 7.2.2 Release

ROCm 7.2.2 Release

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@alexxu-amd alexxu-amd released this 14 Apr 20:47
rocm-7.2.2
874b7b1
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

ROCm 7.2.2 is a quality release that resolves the issue listed in the Release highlights.

Release highlights

The following are the notable changes in ROCm 7.2.2.

ROCTracer failure to report kernel operations is fixed

In ROCm 7.2.1, applications using ROCTracer failed to receive some or all kernel operation events due to a ROCTracer reporting failure. This issue has been resolved, and the fix has been applied to ROCTracer.

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 firmware releases via an AMD Platform Level Data Model (PLDM) bundle, which includes the Integrated Firmware Image (IFWI).

GPU and baseboard firmware versioning might differ across GPU families.

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.2.2 MI355X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.x where x (0-2)
30.20.x where x (0-1)
30.10.x where x (0-2) 		8.7.1.K
MI350X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.x where x (0-2)
30.20.x where x (0-1)
30.10.x where x (0-2)
MI325X[1] 01.25.06.08
01.25.04.02 		30.30.x where x (0-2)
30.20.x where x (0-1)[1]
30.10.x where x (0-2)
6.4.z where z (0-3)
6.3.3
MI300X[2] 01.25.06.04
01.25.03.12
01.25.02.04		 30.30.x where x (0-2)
30.20.x where x (0-1)
30.10.x where x (0-2)
6.4.z where z (0–3)
6.3.3 		8.7.1.K
MI300A BKC 26.1 Not Applicable
MI250X IFWI 47 (or later)
MI250 MU5 w/ IFWI 75 (or later)
MI210 MU5 w/ IFWI 75 (or later) 8.7.1.K
MI100 VBIOS D3430401-037 Not Applicable

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

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

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.

  • The new AMD RDNA3.5 system optimization topic describes how to optimize systems powered by AMD Ryzen APUs with RDNA3.5 architecture. These APUs combine high-performance CPU cores with integrated RDNA3.5 graphics, and support LPDDR5X-8000 or DDR5 memory.

ROCm binaries

For detailed installation instructions, refer to ROCm installation on Linux. ROCm binaries for installation are located at repo.radeon.com and listed below:

ROCm 7.2.2 doesn't include any other significant changes or feature additions. For comprehensive changes, new features, and enhancements in ROCm 7.2.1, refer to the [ROCm 7.2.1 release notes](#rocm-7-2-1-release-notes) below.

ROCm 7.2.1 release notes

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

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:

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...

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ROCm 7.2.1 Release

ROCm 7.2.1 Release

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@alexxu-amd alexxu-amd released this 25 Mar 19:15
rocm-7.2.1
01a1e46
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

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

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:

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.

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.2.1 MI355X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.1
30.30.0
30.20.1
30.20.0
30.10.2
30.10.1
30.10 		8.7.1.K
MI350X 01.26.00.02
01.25.17.07
01.25.16.03 		30.30.1
30.30.0
30.20.1
30.20.0
30.10.2
30.10.1
30.10
MI325X[1] 01.25.06.08
01.25.04.02 		30.30.1
30.30.0
30.20.1
30.20.0[1]
30.10.2
30.10.1
30.10
6.4.z where z (0-3)
6.3.3
MI300X[2] 01.25.06.04
01.25.03.12
01.25.02.04		 30.30.1
30.30.0
30.20.1
30.20.0
30.10.2
30.10.1
30.10
6.4.z where z (0–3)
6.3.3 		8.7.1.K
MI300A BKC 26.1 Not Applicable
MI250X IFWI 47 (or later)
MI250 MU5 w/ IFWI 75 (or later)
MI210 MU5 w/ IFWI 75 (or later) 8.7.1.K
MI100 VBIOS D3430401-037 Not Applicable

[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.

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.

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ROCm 7.2.0 Release

ROCm 7.2.0 Release

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@alexxu-amd alexxu-amd released this 23 Jan 18:06
rocm-7.2.0
c6bf8d2
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

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

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:

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.

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.2.0 MI355X 01.25.17.07
01.25.16.03 		30.30.0
30.20.1
30.20.0
30.10.2
30.10.1
30.10 		8.7.0.K
MI350X 01.25.17.07
01.25.16.03 		30.30.0
30.20.1
30.20.0
30.10.2
30.10.1
30.10
MI325X[1] 01.25.04.02 30.30.0
30.20.1
30.20.0[1]
30.10.2
30.10.1
30.10
6.4.z where z (0-3)
6.3.y where y (2-3)
MI300X[2] 01.25.03.12 30.30.0
30.20.1
30.20.0
30.10.2
30.10.1
30.10
6.4.z where z (0–3)
6.3.y where y (2–3)
 8.7.0.K
MI300A BKC 26 Not Applicable
MI250X IFWI 47 (or later)
MI250 MU5 w/ IFWI 75 (or later)
MI210 MU5 w/ IFWI 75 (or later) 8.7.0.K
MI100 VBIOS D3430401-037 Not Applicable

[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...
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ROCm 7.1.1 Release

ROCm 7.1.1 Release

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@alexxu-amd alexxu-amd released this 26 Nov 19:15
rocm-7.1.1
42cad29

ROCm 7.1.1 release notes

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

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:

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.

<style> tbody#virtualization-support-instinct tr:last-child { border-bottom: 2px solid var(--pst-color-primary); } </style>

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.1.1 MI355X 01.25.16.03
01.25.15.04 		30.20.1
30.20.0
30.10.2
30.10.1
30.10 		8.6.0.K
MI350X 01.25.16.03
01.25.15.04 		30.20.1
30.20.0
30.10.2
30.10.1
30.10
MI325X[1] 01.25.04.02 30.20.1
30.20.0[1]
30.10.2
30.10.1
30.10
6.4.z where z (0-3)
6.3.y where y (1-3)
MI300X 01.25.03.12 30.20.1
30.20.0
30.10.2
30.10.1
30.10
6.4.z where z (0–3)
6.3.y where y (1–3)
 8.6.0.K
MI300A BKC 26 Not Applicable
MI250X IFWI 47 (or later)
MI250 MU5 w/ IFWI 75 (or later)
MI210 MU5 w/ IFWI 75 (or later) 8.6.0.K
MI100 VBIOS D3430401-037 Not Applicable

[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:

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...

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ROCm 7.1.0 Release

ROCm 7.1.0 Release

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@alexxu-amd alexxu-amd released this 03 Nov 20:21
rocm-7.1.0
d3ff9d7
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
Learn about vigilant mode.

ROCm 7.1.0 release notes

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

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:

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.

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
ROCm 7.1.0 MI355X 01.25.15.04 (or later)
01.25.13.09 		30.20.0
30.10.2
30.10.1
30.10		 8.5.0.K
MI350X 01.25.15.04 (or later)
01.25.13.09 		30.20.0
30.10.2
30.10.1
30.10
MI325X[2] 01.25.04.02 30.20.0[*]
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		 30.20.0
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) 		8.5.0.K
MI300A BKC 26
BKC 25		 Not Applicable
MI250X IFWI 47
MI250 MU3 w/ IFWI 73
MI210 MU3 w/ IFWI 73 8.5.0.K
MI100 VBIOS D3430401-037 Not Applicable

[1]: PLDM bundle 01.25.05.00 will be available by November 2025.

[2]: For AMD Instinct MI325X KVM SR-IOV users, do not use AMD GPU Driver (amdgpu) 30.20.0.

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.
    ...
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ROCm 7.9.0 Preview Release

ROCm 7.9.0 Preview Release Pre-release
Pre-release

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@alexxu-amd alexxu-amd released this 20 Oct 22:06
therock-7.9.0
2d560a7
This commit was created on GitHub.com and signed with GitHub’s verified signature.
GPG key ID: B5690EEEBB952194
Verified
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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.

Important

ROCm 7.9.0 introduces a versioning discontinuity following the previous 7.0 releases.
Versions 7.0 through 7.8 are reserved for production stream ROCm releases,
while versions 7.9 and later represent the technology preview release stream.
Both streams share a largely similar code base but differ in their build systems.
These differences include the CMake configuration, operating system package dependencies,
and integration of AMD GPU driver components.

Maintaining parallel release streams allows users ample time to evaluate and
adopt the new build system and dependency changes. The technology preview
stream is planned to continue through mid‑2026, after which it will replace the
current production stream.

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.

Note

If you're running ROCm on Linux, ensure your system is using a supported kernel version.
Future preview releases will expand operating system support coverage.

AMD device series

Device

Architecture

LLVM target

Supported OS

Instinct MI350 Series

Instinct MI355X

Instinct MI350X

CDNA4

gfx950

Ubuntu 24.04.3
(GA kernel: 6.8)

Ubuntu 22.04.5
(GA kernel: 5.15)

RHEL 10.0
(kernel: 6.12.0-55)

RHEL 9.6
(kernel: 5.14.0-570)

Instinct MI300 Series

Instinct MI325X

Instinct MI300X

Instinct MI300A

CDNA3

gfx942

Ryzen AI Max+ PRO 300 Series

Ryzen AI Max+ PRO 395

Ryzen AI Max+ PRO 390

Ryzen AI Max+ PRO 385

Ryzen AI Max+ PRO 380

RDNA3.5

gfx1151

Ubuntu 24.04.3
(HWE kernel: 6.14)

Windows 11 24H2

Ryzen AI Max 300 Series

Ryzen AI Max 395

Ryzen AI Max 390

Ryzen AI Max 385

RDNA3.5

gfx1151

Ubuntu 24.04.3
(HWE kernel: 6.14)

Windows 11 24H2

Note

This release supports a limited number of GPU and APUs.
Hardware support will be expanded with future releases -- following the six-week release cadence.

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).

Note

Supported Ryzen AI APUs require the inbox kernel driver included with Ubuntu 24.04.3.
GPU virtualization is not supported in ROCm 7.9.0.

AMD device

Linux driver

Adrenalin Driver (Windows)

PLDM bundle (firmware)

Instinct MI355X

AMD GPU Driver (amdgpu)
30.10
30.10.1
30.10.2

Not applicable

01.25.15.04

01.25.13.09

Instinct MI350X

Instinct MI325X

01.25.04.02

01.25.03.03

Instinct MI300X

01.25.03.12

Instinct MI300A

BKC 26

BKC 25

Ryzen AI Max+ PRO 395

Inbox kernel driver
in Ubuntu 24.04.3

25.9.2

Not applicable

Ryzen AI Max+ PRO 390

Ryzen AI Max+ PRO 385

Ryzen AI Max+ PRO 380

Ryzen AI Max 395

Ryzen AI Max 390

Ryzen AI Max 385

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.

...
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ROCm 7.0.2 Release

ROCm 7.0.2 Release

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@alexxu-amd alexxu-amd released this 10 Oct 18:36
rocm-7.0.2
07bf464

ROCm 7.0.2 release notes

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

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.

<style> tbody#virtualization-support-instinct tr:last-child { border-bottom: 2px solid var(--pst-color-primary); } </style>

ROCm Version

GPU

PLDM Bundle (Firmware)

AMD GPU Driver (amdgpu)

AMD GPU
Virtualization Driver (GIM)
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 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...
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