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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Start by checking your exact GPU or APU, operating system, ROCm version and inference application: AMD GPU support is specific to that combination. For llama.cpp, install and validate the runtime for the environment you will actually use, then compare HIP and Vulkan on your own model and workload. Neither backend is universally faster, and HSA_OVERRIDE_GFX_VERSION is a workaround—not proof of official support.
What AMD GPU and operating-system combinations are supported?
AMD’s Radeon/Ryzen overview displayed ROCm 7.2.1 on October 5, 2026. It reports support for Radeon 9000-series and selected 7000-series GPUs, plus selected Ryzen AI APU families. Its framework support table is not a blanket promise for every application: it lists Linux support for PyTorch, TensorFlow, JAX and ONNX on the named Radeon families; Windows PyTorch support for those Radeon families; and PyTorch on Windows and Linux for specified APU families.
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AMD’s separate llama.cpp setup guide documents inference on supported Radeon, Ryzen and Instinct devices. Its selector showed Ubuntu 24.04, Windows 11 and ROCm 7.14.0 on October 5, 2026, alongside multiple installation options. These are separate documentation surfaces and release tracks. Framework support in the overview does not by itself establish that a particular llama.cpp build supports the same device and OS.
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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →| What to check | What AMD’s documentation showed on October 5, 2026 | How to use that information |
|---|---|---|
| Radeon and Ryzen AI families | ROCm 7.2.1 overview: Radeon 9000-series and selected 7000-series GPUs, plus selected Ryzen AI APU families. | Confirm the exact device architecture and framework/OS row in the compatibility matrix; family names alone do not establish support for every model or application. |
llama.cpp guide environment selector |
Ubuntu 24.04, Windows 11 and ROCm 7.14.0 were displayed. | Choose the matching OS and device in the guide, then follow the instructions for the selected installation method and version. |
Both version displays can change. Verify the live device compatibility matrix and the selected llama.cpp guide before installing; do not treat “ROCm supported” as a universal statement across GPUs, operating systems, runtimes and builds.
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How to prepare a local llama.cpp setup
- Inventory your system. Record the exact GPU or APU model and its
gfxarchitecture, OS release, driver/runtime and intendedllama.cppbuild. Check that exact combination in AMD’s compatibility information and select the same environment in AMD’s inference guide. - Install ROCm for the environment that will run inference. Linux and Windows have distinct prerequisites and runtime handling. AMD’s general installation guidance offers OS-specific methods, including package-manager and tarball starting points; use the instructions for your chosen method rather than mixing steps from different methods.
- Configure only the paths required by that installation. AMD documents Linux variables such as
ROCM_PATH,PATHandLD_LIBRARY_PATH, and Windows HIP/LLVM path variables. Their values depend on the installation method and version. Do not combine them indiscriminately or copy a path from an unrelated package setup. - On Windows, follow the matching DLL instructions. In the configuration described by AMD’s
llama.cppguide, copy the matchingamdhip64_7.dll,rocm_kpack.dllandamd_comgr.dllnext tollama-cli.exe. Copying only the HIP DLL can leave its runtime dependencies unavailable. Windows DLL search order may also select the driver’samdhip64_7.dllinSystem32instead of the ROCm copy found throughPATH. These are instructions for that documented configuration, not a universal DLL recipe for every package or ROCm version. - Check device visibility, then run an actual workload. Use
llama-cli --list-devicesto see whatllama.cppdetects. Detection alone does not demonstrate that inference is executing on the GPU; AMD recommends running a short GGUF model benchmark as a runtime check. If the system has integrated and discrete GPUs,HIP_VISIBLE_DEVICEScan select the intended device. - Benchmark only after the runtime works. Use
llama-benchand record the model, build and workload settings. Compare prompt processing and generation separately, and include an end-to-end total only when the prompt and generation lengths used to calculate it are clear.
What does HSA_OVERRIDE_GFX_VERSION do?
HSA_OVERRIDE_GFX_VERSION changes the GPU architecture identity presented at runtime. In some unsupported-device scenarios, a user may try it to make software target a nearby architecture. That can be useful as a diagnostic or compatibility workaround, but it does not add native support, prove official compatibility or guarantee correct execution.
Do not copy a universal override value: the appropriate value depends on the GPU architecture, runtime and workload. Upstream llama.cpp guidance and a specific RX 6700 XT issue describe an attempted gfx1031-to-gfx1030 workaround. That report also required a manual patch to bypass a flash-attention assertion and said the correctness impact was unknown. It is an example of a risky, setup-specific workaround—not a generally safe performance setting.
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| Variable | Purpose |
|---|---|
ROCM_PATH, PATH, LD_LIBRARY_PATH, Windows HIP/LLVM paths |
Locate runtime components for the relevant installation method. |
HIP_VISIBLE_DEVICES |
Select which HIP device is visible to the application. |
HSA_OVERRIDE_GFX_VERSION |
Work around architecture recognition or targeting; it is not a runtime-path setting or a compatibility guarantee. |
Keep any override temporary and scoped to the shell or launch configuration being tested. If a native supported path is available, remove the override and validate that path instead.
How should you compare Vulkan with HIP/ROCm?
There is no defensible universal winner. The upstream llama.cpp feature matrix describes ROCm/CUDA as generally faster for K-quants, while noting cases where Vulkan produces faster text generation. It also documents differences in backend feature support. The result therefore depends on both the model and what the application needs to do.
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Make the comparison fair
Change the backend and hold the rest of the test constant. Use the same GPU, model file and quantization, llama.cpp commit/build, driver and runtime, prompt and generation lengths, batch and ubatch sizes, GPU layers, flash-attention setting and KV-cache settings. Repeat runs, report the mean or spread, and keep prompt processing (pp) separate from token generation (tg). If overall response time matters, report a total for a clearly specified prompt-plus-generation workload.
One RX 6700 XT report illustrates the trade-off
A 2026 upstream issue report compared HIP and Vulkan on an RX 6700 XT using a Gemma 4 12B GGUF, an 8,192-token prompt and 512 generated tokens. The reporter used the same stated cache and batch settings for both backends, enabled flash attention and ran each backend three times. The reporter also identifies an architecture override and manual patch in the tested ROCm path, so these results are not a clean general prediction for other systems.
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| Backend | Prompt processing (pp) |
Generation (tg) |
Reporter’s calculated total for this workload |
|---|---|---|---|
| HIP | 653.9 tokens/s | 34.60 tokens/s | 27.3 seconds |
| Vulkan | 354.4 tokens/s | 40.92 tokens/s | 35.6 seconds |
These are the issue reporter’s measurements and calculations for that particular setup, not independently reproduced results or AMD performance claims. In this test, HIP processed the prompt faster, while Vulkan generated tokens faster; for the stated long-prompt scenario, the reporter calculated a shorter total for HIP. The report’s estimated crossover near 1,760 prompt tokens is likewise the author’s calculation for that workload and setup, not a threshold to apply to other GPUs or models.
How to troubleshoot common setup failures
- No device appears: Confirm the exact device/OS/runtime combination is supported for the selected build, and check that ROCm was installed in the same environment where
llama.cppruns. Review the runtime paths for your installation method. - The wrong GPU is used: On a machine with integrated and discrete GPUs, use
HIP_VISIBLE_DEVICESas documented by AMD to select the intended device, then rerun a short workload. - Windows reports zero device memory: AMD identifies
LLVM_PATHas a possible cause in the documented Windows scenario. Its guide suggests clearing that variable or using the matching copied runtime libraries. Apply that advice to the corresponding setup rather than treating it as a universal Windows fix. - Device detection succeeds but performance is unexpectedly low: Detection is not proof of GPU computation. Validate with a short GGUF benchmark and inspect whether the chosen build, runtime and backend support the workload features you enabled.
- An override or patch seems necessary: Treat it as an unsupported-path experiment. Record the change, test output correctness as well as speed, and prefer the native supported configuration when possible; an override or assertion bypass can have unknown consequences.
Which backend should you choose?
First eliminate configurations that do not support your exact device, OS, model features or runtime. Of the remaining options, use HIP/ROCm or Vulkan based on repeated tests with the prompts and generation lengths you actually expect. A workload dominated by processing a long prompt can rank the backends differently from one dominated by generating many tokens, so keep pp, tg and any end-to-end timing distinct.
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