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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11You can run AI models on an AMD GPU with ROCm only when the GPU, operating system, driver, ROCm release, framework, and Python version form a supported combination. Start by checking those exact details in AMD’s ROCm 10.0.0 compatibility matrix (dated August 25, 2026), then install the matching framework and verify that it detects the GPU. An installer completing successfully does not, by itself, establish that a particular model or setup is supported.
Check whether your AMD GPU and software combination is supported
Before installing anything, record the full GPU or APU model, operating system and version, and whether you plan to use Linux or Windows. Then check the exact combination of device, driver, ROCm release, framework, and Python version in AMD’s ROCm 10.0.0 compatibility matrix, dated August 25, 2026. The matrix covers Linux and Windows and lists supported ecosystem components including PyTorch, JAX, vLLM, SGLang, TensorFlow, MIGraphX, and ONNX Runtime. Its entries are version-paired configurations—not a promise that every framework works with every listed GPU and operating system.
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For example, AMD lists the Radeon RX 9070 XT in its ROCm support documentation. That makes it a device to check against the full matrix, not a guarantee that every AI application, driver, or OS combination will work with it.
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Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →- Exact device: Match the full GPU or APU model, not just the AMD brand or product family.
- Operating system: Confirm the OS and release; support can differ between Linux and Windows.
- Software versions: Check the ROCm release, driver, framework, and Python version together.
- Workload: Confirm support for the model package, inference engine, quantization, or kernel you intend to use.
Unlisted GPUs are not officially supported
AMD’s Linux system-requirements documentation states: “If your GPU is not listed on this table, it’s not officially supported by AMD.” The same page notes that HIP may run on an unsupported GPU, but prebuilt ROCm libraries are not officially supported for it and can cause runtime errors. Apparent runtime compatibility is therefore not equivalent to an AMD-supported configuration.
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Choose one ROCm installation path
Use the installation instructions that match the release, device, and operating system you verified. AMD documents Linux package-manager installation, the amdgpu-install utility for Radeon and Ryzen on Linux, pip installation for Python and machine-learning workflows on Linux and Windows, tarballs, and a Linux runfile installer. Select the method for your configuration in AMD’s installation documentation rather than combining commands from different release guides.
ROCm 10.0.0 path
For a ROCm 10.0.0 setup, use its compatibility matrix and the corresponding current installation and framework instructions. AMD’s install guide describes the available methods, while its PyTorch documentation gives a Python workflow using a virtual environment and an AMD-hosted ROCm wheel index. Wheel and command variants depend on the operating system and GPU architecture, so follow the live instructions for your checked configuration instead of reusing an older command.
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Radeon and Ryzen 7.2.1 documentation path
AMD’s separate Radeon/Ryzen guide currently documents through ROCm 7.2.1 and describes support for Radeon 9000 and select 7000 series devices, as well as select Ryzen APUs. Treat this as a distinct, version-specific path: do not combine its release numbers or commands with ROCm 10.0.0 instructions.
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AMD’s Radeon/Ryzen documentation describes supported Windows 11 configurations for PyTorch, but says the entire ROCm stack is not yet supported on Windows. Check the specific device and framework entry in AMD’s Windows installation guidance and compatibility documentation before choosing this route; do not infer full-stack Windows support from the presence of Windows in the broader ROCm 10.0.0 matrix.
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Install PyTorch and verify GPU detection
Once you have selected a supported release path, use AMD’s current PyTorch instructions for that exact operating system and device. For Python-based work, AMD documents using a virtual environment and installing ROCm-enabled packages through its wheel index. The commands differ by configuration, so the guide—not a command copied from a different release—is the authority for the installation.
- Follow AMD’s matching PyTorch setup instructions. Choose the instructions for your OS, GPU architecture, and selected ROCm release in the ROCm installation guide.
- Check that PyTorch sees an accelerator. Run
python -c "import torch; print(torch.cuda.is_available())". AMD’s guide expectsTruewhen installation is correct. - Confirm which device PyTorch sees. Run
python -c "import torch; print(torch.cuda.get_device_name(0))". - Collect environment details if detection fails. Run
python -m torch.utils.collect_envand use the output when diagnosing the setup against AMD’s version-specific instructions.
A successful visibility check establishes that PyTorch detects the GPU; it does not establish that every model, extension, or inference engine will run on it.
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Check the model or inference engine separately
ROCm and framework compatibility are only part of the setup. Before running a workload, consult the application’s own AMD instructions and confirm that its model implementation, kernels, quantization method, and other dependencies support your configuration. AMD’s matrix lists framework-level versions for tools such as vLLM and SGLang, but that does not make every model or workflow available on every device.
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