Supported hardware
ROCm support on consumer AMD GPUs is Linux-only and varies by architecture. RDNA3 (7900 XTX) and RDNA2 (6800 XT) have the best community reports. Windows ROCm for llama.cpp remains experimental.
Good: RX 7900 XTX, RX 6800 XT, RX 6900 XT (Linux)
Patchy: RX 6700 XT, older Polaris
Not supported: integrated Radeon graphicsBuild llama.cpp with HIP
Install ROCm 6.x, then build with GGML_HIP=ON. Set HSA_OVERRIDE_GFX_VERSION if your GPU is not officially listed.
# Ubuntu 22.04 example
sudo apt install rocm-dev
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
cmake -B build -DGGML_HIP=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++
cmake --build build -j$(nproc)
./build/bin/llama-server -m ./model.gguf -ngl 99 -c 4096 --host 0.0.0.0Related guides
Running 70B on Dual RTX 3090 with llama.cpp
Tensor-split across two 24GB cards to run Llama 3.1 70B or Qwen2.5 72B at Q4.
Quantize Your Own Model to GGUF
Use llama.cpp's quantize tool to convert any HF model to GGUF Q4_K_M for local inference.
llama.cpp on Windows with CUDA
Build llama.cpp with NVIDIA GPU support on Windows 11 — the path of least resistance for PC gamers.