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Update README.md to add AMD GPU instructions #83

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Update README.md to add AMD GPU instructions
zhangnju Jul 25, 2025
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Update README.md
zhangnju Jul 26, 2025
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Update README.md
zhangnju Jul 26, 2025
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20 changes: 19 additions & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,25 @@ See `bench.py` for benchmark.
| vLLM | 133,966 | 98.37 | 1361.84 |
| Nano-vLLM | 133,966 | 93.41 | 1434.13 |

## Run Nano-vllm on AMD GPU
### CDNA3 datacenter GPUs
Access AMD CDNA3 datacenter GPU through [AMD developer cloud] (https://www.amd.com/en/developer/resources/cloud-access/amd-developer-cloud.html).It is stronly advised to use the pre-built vLLM GPU instance, which has already installed Triton and Rocm version flash attention library.if you have other MI300 GPU resource, you could try the docker image of rocm/vllm:latest to run nano-vllm

### RDNA3 Desktop GPUs
#### using pre-built rocm navi vllm docker image
in these [ROCM vllm navi images](https://hub.docker.com/r/rocm/vllm-dev/tags?name=navi), flash attention library has been installed already. you can choose the latest verison for your test.
#### using pre-built rocm pytorch docker image
1) use the pre-built rocm docker image to setup ROCM, like rocm/pytorch:latest
2) install the rocm-version flash triton library
```bash
git clone --recursive https://github.com/Dao-AILab/flash-attention.git
cd flash-attention
FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" python setup.py install
```
4) install nano-vllm
5) run benchmark: FLASH_ATTENTION_TRITON_AMD_ENABLE="TRUE" python3 bench.py


## Star History

[![Star History Chart](https://api.star-history.com/svg?repos=GeeeekExplorer/nano-vllm&type=Date)](https://www.star-history.com/#GeeeekExplorer/nano-vllm&Date)
[![Star History Chart](https://api.star-history.com/svg?repos=GeeeekExplorer/nano-vllm&type=Date)](https://www.star-history.com/#GeeeekExplorer/nano-vllm&Date)