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Update vllm_deployment_guide.md for the AMD GPU support #32
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@@ -151,6 +151,36 @@ cd vllm/ | |
| pip install -e . | ||
| ``` | ||
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| ### AMD GPU Support | ||
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| ### Please follow the steps here to install and run LongCat-Flash-Chat models on AMD MI300X GPU. | ||
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| ### Step By Step Guide | ||
| #### Step 1 | ||
| Launch the Rocm-vllm docker: | ||
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| ```shell | ||
| docker pull rocm/vllm-dev:nightly | ||
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| docker run -d -it --ipc=host --network=host --privileged --cap-add=CAP_SYS_ADMIN --device=/dev/kfd --device=/dev/dri --device=/dev/mem --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined -v /:/work -e SHELL=/bin/bash --name vllm_minimax rocm/vllm-dev:nightly | ||
| ``` | ||
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| #### Step 2 | ||
| Huggingface login | ||
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| ```shell | ||
| huggingface-cli login | ||
| ``` | ||
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| #### Step 3 | ||
| Run the vllm online serving | ||
| Sample Command | ||
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| ```shell | ||
| SAFETENSORS_FAST_GPU=1 VLLM_USE_V1=0 vllm serve MiniMaxAI/MiniMax-M1-80k -tp 8 --gpu-memory-utilization 0.95 --no-enable-prefix-caching --trust-remote-code --max_model_len 4096 --dtype bfloat16 | ||
| ``` | ||
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| ## 📮 Getting Support | ||
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| If you encounter any issues while deploying MiniMax-M1 model: | ||
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It’s best to use ## AMD GPU Support; otherwise, this section will be considered part of the Common Issues chapter.