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To check support, match five items in AMD’s compatibility documentation: your exact GPU or APU model, operating system and version, ROCm release, AI framework and framework version, and (when listed) Python version. Confirm that one matrix entry contains the entire combination, then install exactly that configuration and run a sample workload. A statement that ROCm supports a framework does not mean every AMD device is supported.

Start with AMD’s Radeon and Ryzen ROCm overview for the supported product families, but use the linked detailed compatibility matrix for a model-level decision. Support can change between ROCm releases and between Linux and Windows, so record the release number whenever you make or document a compatibility decision.

Why AMD GPU compatibility is a matrix, not a brand name

“AMD GPU” covers discrete Radeon cards, integrated graphics and Ryzen AI processors with different architectures and software requirements. Framework support is also versioned. A framework may be listed for one ROCm release, operating system or GPU architecture and absent from another. The useful question is therefore not “Does this framework support AMD?” but “Does this exact framework version run on this exact AMD device, operating system, ROCm release and Python environment?”

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AMD describes ROCm and framework support in configuration-specific terms. Its documentation states that supported versions depend on the ROCm release and system configuration. Treat a family-level badge, a forum post or a successful installation on another Radeon model as a lead to investigate, not as proof for your machine.

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Record your intended configuration before opening the matrix

Write down the values below. If one is unknown, identify it before checking compatibility; otherwise it is easy to select a nearby but unsupported row.

Exact GPU or APU model

Use the complete product name reported by your operating system or firmware, including the series and model number. For an integrated processor, record the full Ryzen AI APU designation rather than only the CPU family. Do not substitute a GPU architecture for a model when the matrix lists individual devices.

Operating system and release

Record Linux distribution and release, or the exact Windows edition and build if applicable. The same Radeon family can have different framework coverage on Linux and Windows. A matrix entry for one operating system cannot be assumed to apply to the other.

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Framework and version

Name the framework and the version your application requires, such as a particular PyTorch, TensorFlow, JAX or ONNX release. “Latest” is not a reproducible target: framework packages are tied to ROCm releases and may drop or add hardware support.

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ROCm release

Choose a ROCm release deliberately. The release determines available framework builds, supported GPU architectures and installation instructions. If you have not chosen one, compare the releases that contain your required framework version and device, then select a single documented combination.

Python version and application dependencies

Check Python only when the matrix or framework installation page specifies it; otherwise use the version required by the framework package. Also list libraries your application imports directly. A framework may support your GPU while a separate extension, custom kernel or CUDA-only dependency does not.

How to check AMD’s compatibility information step by step

  1. Open AMD’s current Radeon/Ryzen page. Use AMD’s ROCm Radeon and Ryzen documentation to identify whether your product family is in scope. The page is a high-level overview, not the final model-level test.
  2. Open the detailed compatibility matrix for the ROCm release you intend to install. Select the release first. Do not read a row from one release and combine it with a framework version documented for another.
  3. Find the exact GPU or APU. Confirm the model or the architecture explicitly named by the matrix. “Radeon 7000,” for example, may include only selected models; an architecture name alone may cover fewer devices than the product family suggests.
  4. Match the operating system. Verify the matrix column for your Linux distribution and release or your Windows version. If your OS is not listed, stop and choose a supported OS or a different ROCm release rather than treating an adjacent version as equivalent.
  5. Match the framework and version. Check that the exact PyTorch, TensorFlow, JAX or ONNX version appears for your device and OS. A framework’s presence elsewhere on the page is not evidence that it works in your row.
  6. Check Python and package constraints. Where the matrix specifies Python versions, use one of those versions. Confirm that the installation guide provides a package for your operating system and selected ROCm release.
  7. Save the result. Record the matrix URL, ROCm release, framework version, GPU model and OS in your project notes or environment file. This makes future upgrades auditable and prevents an installer from silently changing one dimension.

AMD’s general ROCm guidance gives the same operational sequence: check compatibility for the GPU and operating system, follow the matching installation guide, and run a sample workload before scaling up. See the ROCm software overview.

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What the current AMD overview actually says

AMD’s current Radeon/Ryzen overview names ROCm 7.2.1 and summarizes support by broad product grouping. These entries show the scope of the overview; they do not replace the detailed matrix for a particular card, APU or software version.

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Hardware grouping named by AMD Operating system Frameworks named in the overview
Radeon 9000 and selected Radeon 7000 series Linux PyTorch, TensorFlow, JAX and ONNX
Radeon 9000 and selected Radeon 7000 series Windows PyTorch
Ryzen AI Max 300 and selected AI 400/300 series APUs Linux and Windows PyTorch

“Selected” is important: the overview does not promise every model in those families. Use the model-level row to determine whether your device is included and which release and framework versions apply.

Why ROCm release numbers change the answer

AMD’s ROCm 10.1.0 release notes are a separate, release-specific reference. They list PyTorch 2.14.0 for Linux and Windows, TensorFlow 2.21 for Linux on specified GPU architectures, and JAX 0.11.1 for Linux on specified architectures. Those numbers describe the 10.1.0 ecosystem; they are not a promise that every Radeon or Ryzen device runs every listed framework.

Document Examples listed How to interpret it
Radeon/Ryzen overview, ROCm 7.2.1 Family-level framework and OS groupings Useful for initial scope; confirm the exact device in the detailed matrix.
ROCm 10.1.0 release notes PyTorch 2.14.0 on Linux and Windows; TensorFlow 2.21 and JAX 0.11.1 on Linux for specified architectures Version-specific examples; architecture, device and OS restrictions still apply.

Do not merge these documents into one broad compatibility promise. When you publish an internal support statement or choose an environment, name the ROCm release, operating system, device and framework version together.

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Install the supported combination, not merely a package that resolves

Once the matrix shows a match, use AMD’s installation instructions for that exact hardware and operating system. The ROCm and deep learning frameworks installation guide, dated February 19, 2026, directs users to system requirements and prerequisites before installation, then to post-install configuration and verification.

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Before installation

  • Confirm that the operating system, kernel or Windows build and GPU driver meet the release requirements.
  • Remove or isolate conflicting ROCm, GPU-driver or framework environments according to AMD’s instructions.
  • Choose the Python environment required by the matrix and framework package.
  • Decide whether the host installation or an official ROCm prebuilt Docker image is more appropriate. AMD recommends its official prebuilt images as an easier way to obtain a consistent framework environment.

After installation

  1. Complete AMD’s post-install steps for PATH and linker configuration.
  2. Run the verification checks specified for your ROCm release and framework.
  3. Launch a small, known sample workload before installing the full application.
  4. Run a system-health benchmark before a long training or inference job. A successful import alone does not prove that kernels execute correctly on the GPU.

If the installer succeeds but the device is absent from the framework, return to the matrix and compare every dimension. Installation success means that packages were placed on the system; it does not establish model-level support.

Verify that the framework is using the AMD GPU

Verification has three layers. First, the ROCm installation must recognize the device. Second, the framework must expose an accelerator backend for the selected build. Third, a real operation must execute and return a correct result.

Layer 1: platform visibility

Use the hardware and ROCm diagnostic checks documented for your release. Confirm that the expected model is reported, not just that a generic accelerator is present. If the diagnostic identifies a different device, fix the driver or host configuration before testing the framework.

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Layer 2: framework visibility

Run the framework’s official installation test or sample from its AMD/ROCm instructions. Check both the reported device count and the selected device name. Some ROCm-enabled framework builds retain APIs whose names contain “CUDA”; that naming does not turn ROCm into CUDA and should be interpreted according to the framework’s ROCm documentation.

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Layer 3: a representative workload

Execute a small inference or training example using the same model operations your application needs. Watch for kernel compilation errors, unsupported operators, out-of-memory messages and unexpected CPU execution. Compare the output with a CPU reference on a small input to test correctness before measuring speed.

Keep the sample, package versions and output logs with the environment record. Re-run them after changing the ROCm release, framework version, driver or GPU.

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CUDA code needs a separate portability check

ROCm and CUDA are separate GPU-computing platforms; their tools and APIs are not directly interchangeable. A framework build that supports ROCm may run an application, while application code that calls CUDA APIs directly or imports CUDA-specific libraries may require changes.

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Identify CUDA-specific dependencies

  • Search source code for direct CUDA runtime, driver or kernel API calls.
  • List extensions that compile against CUDA headers or link to CUDA libraries.
  • Check model-serving, quantization and custom-operator packages independently; framework-level support does not cover every extension.

Port and test

AMD’s HIP programming model provides a porting path, and HIPIFY can translate supported CUDA source to HIP. Translation is not the end of the port: build the converted code with the ROCm toolchain, test numerical correctness against a known result, and tune performance on the target GPU. Unsupported CUDA-specific behavior may still require manual changes.

Diagnose common compatibility failures

Symptom Likely explanation Next action
The GPU is absent from the framework The model, driver, ROCm release or framework build is outside the matrix entry. Recheck the exact model and OS row, then install the framework build tied to that ROCm release.
The framework imports but operations fail An operator, kernel or extension is unsupported on that architecture, or a dependency is CUDA-only. Reproduce with the framework’s sample; isolate the failing operation and check its ROCm support and dependency requirements.
Linux works but Windows does not Framework coverage differs by operating system. Use the Windows column of the matrix; do not copy Linux package instructions.
A newer framework version cannot be installed The chosen ROCm release may not provide a package for that framework or Python version. Select a framework version listed for the release, or choose a ROCm release whose matrix includes the required version.
Performance is unexpectedly low The application may be running on the CPU, using an unoptimized path or spending time compiling kernels. Confirm device selection during the sample workload, run a health benchmark, then profile the representative operation.
A package update breaks a working environment An unpinned framework, ROCm or Python dependency changed the tested combination. Restore the recorded versions, pin the environment, and validate upgrades with the sample workload before rollout.

How to choose between several candidate setups

When more than one GPU, operating system or software stack is available, compare complete configurations rather than isolated specifications.

Comparison axis Question to answer
Device Is the exact GPU or APU model, or the explicitly listed architecture, present in the matrix?
Operating system Which candidate has a matrix entry for the OS release you will maintain?
ROCm release Which release contains the framework version and device combination you need?
Framework Does the exact framework version appear, rather than only the framework name?
Python Does the package support the Python version required by your application?
Application portability Does the workload rely on CUDA APIs, CUDA-only libraries or custom kernels that need porting?
Operational validation Can you install the documented stack and complete a sample workload and health benchmark?

A setup with slightly newer hardware is not automatically the better choice if its framework or operating-system row is missing. Prefer the complete, documented combination that your team can reproduce and validate.

Keep compatibility valid after the first installation

  • Pin ROCm, framework, Python and critical extension versions in the environment definition.
  • Store the exact GPU model, OS release and matrix reference with deployment documentation.
  • Read the release notes before upgrading any one component; a new framework may require a different ROCm release or architecture.
  • Re-run the sample workload and a health benchmark after driver, ROCm, framework or kernel updates.
  • Recheck the matrix when moving from Linux to Windows, from a discrete GPU to an APU, or from one product generation to another.

AMD’s compatibility pages are the authority for the current supported intersection. The practical proof is a successful sample workload on the exact environment you intend to operate, not a generic claim that ROCm supports your framework.

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