Check the exact AMD GPU or APU model, operating system, ROCm release, framework version and (when listed) Python version against AMD’s compatibility matrix. Then install the matching configuration and run a sample workload. A general statement that ROCm supports a framework does not prove that your particular AMD device is supported.
Why AMD GPU compatibility is a matrix, not a yes-or-no label
AI software support is the intersection of several versioned components. The same framework can work on one AMD GPU and fail on another, or work on Linux but not Windows. A release that supports a particular architecture may also require a specific ROCm release, framework build and Python version.
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AMD describes the rule plainly in its ROCm documentation: “Supported versions depend on the ROCm release and system configuration.” The practical consequence is that “I have a Radeon card” or “this framework supports ROCm” is not enough evidence. You need one row in the current, model-specific matrix that matches your entire planned stack.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteStart with AMD’s Radeon and Ryzen ROCm compatibility documentation for the platform overview, then open the detailed matrix linked from the relevant release and framework documentation. Treat the detailed matrix as authoritative for a specific installation.
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Information to collect before checking
Write down the following before opening the matrix. Exact values prevent a common mistake: matching a family name while overlooking a model, operating-system or release restriction.
- GPU or APU model: Record the complete retail or system-reported name, including a mobile, integrated or workstation suffix. For an APU, record the Ryzen model and its integrated-GPU designation.
- GPU architecture, if shown: Some tables identify architectures rather than every board name. Use the architecture only to locate entries; do not treat an architecture match as final approval when the table also lists individual models.
- Operating system and version: Record the Linux distribution and release, or the exact Windows edition and build. Linux and Windows support can differ for the same hardware.
- ROCm release: Choose the ROCm version you intend to install. Compatibility changes between releases, so do not use a matrix for one release to justify installing another.
- Framework and version: Name the framework and its exact release, such as a particular PyTorch, TensorFlow, JAX or ONNX version. A framework name without a version is incomplete.
- Python version: Check this when the matrix or framework installation page specifies it. Python compatibility can determine whether an otherwise supported wheel or container will install.
- Workload dependencies: Note whether your application calls CUDA APIs directly or depends on CUDA-only libraries. That is a separate portability question even when the framework itself has a ROCm build.
How to check compatibility step by step
- Identify the exact hardware. Use the operating system’s hardware information or the system specification to capture the full GPU or APU model. Do not stop at a broad label such as Radeon 7000 or Ryzen AI.
- Choose a ROCm release deliberately. Decide which release your project, framework package or container requires. Record the release number before consulting a table; otherwise it is easy to mix entries from different documentation sets.
- Open AMD’s current compatibility matrix. AMD’s ROCm overview and the Radeon/Ryzen documentation provide the starting points. Follow the links to the detailed matrix for your release and platform.
- Match the hardware entry. Confirm the exact model or the architecture entry that AMD says covers it. If the table names selected models, a different model in the same family is not automatically included.
- Match the operating system. Verify both the operating-system family and version where the matrix specifies one. A Linux entry does not establish Windows support, and a Windows entry does not establish Linux support.
- Match the framework and framework version. Find the framework column or row and verify the exact version. If your installed version is newer or older than the documented one, treat the combination as unconfirmed until a current matrix entry covers it.
- Check Python and other prerequisites. Where AMD lists Python, compiler, kernel or driver requirements, compare those values too. Every required condition must match; one unmatched prerequisite can invalidate the installation.
- Save the evidence. Record the matrix URL, ROCm release, framework version and date you checked. This makes later troubleshooting possible when documentation or packages change.
- Use the matching installation guide. Follow AMD’s guide for the same hardware and operating-system combination rather than a generic package tutorial. The installation page tells you to review system requirements and prerequisites, complete post-install configuration, and verify the result.
- Run a sample workload. After installation, execute a small framework example that performs a real operation on the GPU. A successful import alone is weaker evidence than a workload that allocates tensors, compiles or loads a model, and completes without falling back to the CPU.
- Run a health benchmark before production work. AMD recommends a system-health check before a larger AI workload. Use the framework and ROCm verification instructions for your release, then compare the observed device and errors with the documented setup.
What AMD’s current Radeon and Ryzen overview actually says
AMD’s current Radeon/Ryzen landing page, accessed October 9, 2026, identifies ROCm 7.2.1 and summarizes support by hardware grouping and operating system. It is useful for narrowing the search, but AMD says the detailed matrix remains necessary for a particular card, APU and software stack.
| Hardware grouping in AMD’s overview | 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 |
Several qualifications matter. “Selected” means the family label is not a blanket promise for every model. The table is a ROCm 7.2.1 overview, so it cannot by itself establish support for a different ROCm release. It also groups frameworks broadly; it does not replace the row that specifies your exact framework version, operating system and hardware.
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Why release notes can show different framework versions
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. These numbers describe the ecosystem documented for ROCm 10.1.0; they do not mean that every Radeon or Ryzen device runs every listed framework.
Use the release notes to understand what a particular ROCm release packages or validates, then use the compatibility matrix to determine whether your hardware is included. Do not merge the ROCm 7.2.1 Radeon/Ryzen overview and ROCm 10.1.0 release notes into one universal support claim. When publishing an internal support record or opening a bug, name the ROCm release, operating system, framework version and matrix entry together.
| Documentation set | Version context | What it can establish | What it cannot establish alone |
|---|---|---|---|
| Radeon/Ryzen platform overview | ROCm 7.2.1 | Broad framework and OS groupings for current Radeon GPUs and selected Ryzen APUs | Support for every model, framework release or another ROCm version |
| ROCm Core SDK release notes | ROCm 10.1.0 | Framework versions and architecture-specific release information documented for that release | Universal support across all Radeon/Ryzen hardware |
| Detailed compatibility matrix | The selected ROCm release | The exact hardware, OS, framework and other conditions covered by a matrix entry | Compatibility for a different release or an unlisted configuration |
Consult AMD’s ROCm Core SDK 10.1.0 release notes when you are evaluating that release, and recheck the current matrix immediately before installation because supported versions can change.
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Installation and verification that provide useful evidence
AMD’s framework installation guidance is dated February 19, 2026. It directs users to system requirements and prerequisites, installation for a compatible system, post-install configuration such as PATH and linker settings, and framework-specific documentation. Follow those stages in order; skipping post-install configuration can make a valid device appear unavailable.
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Install the configuration the matrix covers
- Open AMD’s ROCm and deep-learning framework installation guide.
- Check the system requirements and prerequisites for your operating system, GPU or APU and selected ROCm release.
- Install ROCm and the framework using the instructions for that exact combination. If AMD provides a release-matched package or container, do not substitute an unrelated version simply because it has the same framework name.
- Complete the post-install steps for environment paths and dynamic-library linking described by AMD and by the framework’s own installation documentation.
AMD notes that official ROCm prebuilt Docker images can simplify installation. A container can make the user-space framework stack reproducible, but it does not remove host requirements: the host kernel, driver, device permissions and GPU support still have to match the documented configuration.
Verify the device and run a real operation
- Use the framework’s documented verification procedure to confirm that the ROCm device is visible.
- Run a small sample workload supplied by the framework or AMD. The sample should perform an actual GPU operation rather than merely import a package.
- Run a system-health benchmark before starting a long training or inference job, as AMD recommends.
- Record whether the workload used the intended GPU, completed successfully and produced the expected result. Keep the ROCm, framework, operating-system and Python versions with the log.
A package import can succeed while the application later fails during kernel compilation, memory allocation or model execution. Conversely, a warning about an optional component does not necessarily mean the core framework is unsupported. The sample workload and health check give you stronger evidence than either message in isolation.
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CUDA code is a separate compatibility check
ROCm and CUDA are separate GPU-computing platforms. AMD states that their tools and APIs are not directly interchangeable. A framework build enabled for ROCm may run an application that uses the framework’s portable operations, but code that calls CUDA APIs directly or loads CUDA-specific libraries may need changes.
Inspect dependencies before declaring a port successful
- Search the application and its extensions for direct CUDA API calls, CUDA kernel launches and CUDA-only environment variables.
- Check compiled libraries and custom operators for CUDA-specific dependencies. A framework wheel that installs successfully does not guarantee that every extension has a ROCm build.
- Identify any code path that assumes CUDA device names, memory behavior or tooling. Replace or isolate those assumptions before testing performance.
AMD describes HIP as a porting path and says HIPIFY can help translate supported CUDA source to HIP. Translation is not the end of the job: test numerical correctness, then tune performance on the AMD device. A workload that produces output is not automatically equivalent in speed, memory use or determinism.
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The GPU is not detected
- Recheck the exact model and operating-system entry; a neighboring model or unsupported mobile variant may look similar in marketing material.
- Confirm that the installed ROCm release is the one used by the matrix entry. Mixed packages from different releases can produce missing libraries or incompatible kernels.
- Repeat the post-install PATH, linker and device-permission steps from AMD’s installation guide.
- Run the documented verification procedure before troubleshooting the application. If the basic verification cannot see the device, the problem is below the framework layer.
The framework installs but uses the CPU
- Check that the framework package is the ROCm-enabled build specified for your release, not a CPU-only package.
- Run the sample workload and inspect its device selection. An application may default to CPU even when the GPU is available.
- Check custom operators and optional dependencies separately; one unsupported extension can force a fallback or stop only part of the workload.
A model fails during execution
- Reduce the sample to a small operation to distinguish installation problems from model-specific kernels or memory requirements.
- Compare the model’s operators and extensions with the framework version documented in the matrix.
- If the code originated on CUDA, audit CUDA APIs and libraries before treating the failure as a hardware-support issue.
- Capture the complete versions and the failing operation, then consult the release-matched framework and ROCm issue guidance.
Performance is unexpectedly low
Compatibility is a prerequisite, not a performance guarantee. First establish correctness with the sample workload and health benchmark. Then check whether the application is actually using the GPU, whether a CUDA-specific path was replaced with a portable or HIP path, and whether the model uses operations optimized for your architecture. AMD’s documentation does not provide a universal performance figure for every supported combination, so measure your own workload after the port.
How to compare two candidate setups
When you have more than one possible installation, compare complete configurations rather than isolated hardware or framework names.
| Comparison axis | Question to answer | Why it matters |
|---|---|---|
| GPU or APU | Is the exact model or documented architecture listed? | Family-level support can exclude individual models. |
| Operating system | Does the matrix list this OS and version? | Linux and Windows framework coverage differ. |
| ROCm release | Which release does the matrix and installer describe? | Framework versions and supported hardware change by release. |
| Framework version | Is the exact PyTorch, TensorFlow, JAX or ONNX version documented? | A different package version may be unvalidated or require another ROCm release. |
| Python version | Does the package require a particular Python version? | Python incompatibility can block installation even when the GPU is supported. |
| Application dependencies | Does the code call CUDA APIs or load CUDA-only libraries? | Portability work may be required beyond installing a ROCm framework build. |
| Validation path | Can you run the sample workload and health benchmark? | Successful installation is weaker evidence than an end-to-end test. |
| Installation method | Is there a release-matched package or official prebuilt container? | A documented method reduces version and dependency drift. |
The best candidate is the one with a complete matrix match and a reproducible verification path, not necessarily the newest framework or the broadest family label.
A record you can keep for every machine
For repeatable support checks, keep a short compatibility record with these fields:
- Exact GPU or APU model and, if relevant, architecture
- Operating system, distribution or edition, and version
- ROCm release and installation method
- Framework name and exact version
- Python version, when specified
- AMD matrix URL and the date checked
- Post-install verification result
- Sample workload result and health-benchmark result
- CUDA or custom-extension dependencies that required porting
This record distinguishes three states that are often confused: documented support (the configuration appears in AMD’s matrix), installed support (the matching software is configured correctly), and application validation (your actual workload runs correctly on the GPU).
Sources and version scope
Use these AMD pages for the current checks, and note the version context on each page:
Quick Recap
- Use ROCm on Radeon and Ryzen — platform overview accessed October 9, 2026; reports ROCm 7.2.1.
- ROCm Software — AMD product overview accessed October 9, 2026.
- ROCm Core SDK 10.1.0 release notes — release-specific framework and architecture notes accessed October 9, 2026.
- Installing ROCm and deep learning frameworks — page dated February 19, 2026; installation, prerequisites and verification guidance.
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