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For local LLM inference with llama.cpp on an AMD GPU, ROCm/HIP is AMD’s focused compute backend; Vulkan is a more general GPU backend. Neither is a guaranteed fit for every AMD card. Check compatibility for your exact GPU, operating system, driver, backend build, and llama.cpp revision first. If both work, compare them on your own model and settings: upstream describes ROCm as generally faster but notes cases where Vulkan generates text faster.
Start with hardware and software compatibility
Backend choice is not simply “AMD card means ROCm.” Support depends on the particular GPU and software combination. AMD’s ROCm system requirements are release-specific: check the GPU and operating system against the version you plan to install. AMD says an unlisted GPU is not officially supported, and prebuilt libraries can produce runtime errors even when the HIP runtime appears to work.
Vulkan has a different check: confirm that the target host exposes a working Vulkan device through its driver stack, then verify that the llama.cpp Vulkan backend covers the model and features you need. Availability of a Vulkan device alone does not establish that every model operation will run as intended.
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ROCm/HIP
ROCm is the AMD-oriented compute path in llama.cpp. AMD’s current llama.cpp guide describes supported AMD Instinct accelerators, Radeon discrete GPUs, and Ryzen APUs for its documented ROCm release. Which devices and operating systems qualify varies by release, so use the matching compatibility information rather than assuming the guide covers every AMD product.
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On Linux, the guide lists the AMD GPU driver, membership in the video and render groups, and packages including libgomp1 and libcurl4 among the setup prerequisites. If those access or dependency conditions are missing, a build may not have the expected access to the GPU or runtime libraries.
Vulkan
Vulkan is a general GPU backend that upstream llama.cpp documents alongside HIP. The upstream build guide gives a Linux setup path. For Debian or Ubuntu, it lists Vulkan development headers and libraries, glslc, and SPIR-V headers. Its instructions recommend checking the host with vulkaninfo before compiling.
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The guide’s example build commands are:
cmake -B build -DGGML_VULKAN=1
cmake --build build --config ReleaseA successful compile is not by itself proof that inference uses the intended GPU. Check device detection and confirm that the run offloads the layers you expect.
Compare support, setup, and performance
| Decision point | ROCm/HIP | Vulkan |
|---|---|---|
| Compatibility | Check the GPU and operating system against AMD’s requirements for the exact ROCm release. | Confirm that the target host and driver expose a working Vulkan device. |
| Linux setup | AMD GPU driver, video and render group access, and documented dependencies such as libgomp1 and libcurl4. |
Vulkan development dependencies; run vulkaninfo before building. |
| Backend build | Follow AMD’s current llama.cpp ROCm guide for the matching release. |
Upstream documents the -DGGML_VULKAN=1 CMake option. |
| Feature coverage | Check operation support for the backend and version you will use. | Check operation support for the backend and version you will use. |
| Performance expectation | Upstream characterizes ROCm as generally faster, but this is not a guarantee for a given configuration. | Upstream notes cases where Vulkan has faster text generation; this is not a guarantee for a given configuration. |
The speed comparison comes from the upstream feature matrix, which gives qualitative guidance rather than controlled results for every GPU, model, quantization, or prompt. There is no universal tokens-per-second winner established for an unspecified AMD system.
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Check model and operation coverage
Different backends may support different operations. Consult the upstream operation support documentation for the operations and features required by your model and serving path. Do not infer complete feature parity from the fact that both backends are listed in llama.cpp.
Benchmark both backends fairly
When both builds run correctly, hold the workload constant so the comparison reflects the backend rather than changed settings.
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- Use the same software and model. Keep the
llama.cpprevision and model file identical; record each backend’s runtime and build configuration. - Match inference settings. Keep quantization, context length, prompt and generation lengths, batch settings, GPU-layer offload, and server or client load the same.
- Verify the actual GPU path. Confirm that each run detects the intended GPU and offloads the intended layers before comparing speed.
- Separate prompt processing from generation. Record both when your measurement tool exposes them; the upstream performance note specifically allows for Vulkan text-generation exceptions.
- Repeat noisy runs and document the setup. Record GPU, driver, operating system, backend/runtime versions, build flags, model, and inference settings alongside any results.
Which one should you try first?
- Try ROCm/HIP first if your exact GPU and operating system are supported by the ROCm release you intend to install, and its driver and Linux prerequisites fit your setup.
- Try Vulkan first if the host exposes a working Vulkan device and you prefer to build with the upstream Vulkan path, or if ROCm compatibility does not cover your device.
- Test both if both are compatible and your workload makes speed or feature coverage consequential. Use the same model and settings, and choose based on measured behavior rather than a backend-wide speed claim.
AMD’s versioned ROCm setup guide and upstream build documentation are the relevant starting points for installation; consult the ROCm requirements and operation table for the specific release and workload you plan to run.
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