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+## AMD GPU Support
+Recommended approaches by hardware type are:
+
+
+MI300X/MI325X/MI355X  with fp8: Use FP8 checkpoint for optimal memory efficiency.
+
+- **MI300X/MI325X/MI355X with `fp8`**: Use FP8 checkpoint for optimal memory efficiency.
+- **MI300X/MI325X/MI355X with `bfloat16`**
+  
+#### Step by Step Guide
+Please follow the steps here to install and run Qwen3-Next-80B-A3B-Instruct models on AMD MI300X GPU.
+#### Step 1
+Pull the latest vllm docker:
+```shell
+docker pull vllm/vllm-openai-rocm:v0.14.1
+```
+Launch the Rocm-vllm docker: 
+```shell
+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 Qwen3-next vllm/vllm-openai-rocm:v0.14.1
+```
+### Step 2: Log in to Hugging Face
+Log in to your Hugging Face account:
+```shell
+hf auth login
+```
+
+### Step 3: Start the vLLM server
+
+Run the vllm online serving
+### BF16 
+```shell
+VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct --tensor-parallel-size 4 --max-model-len 32768  --no-enable-prefix-caching 
+```
+### FP8
+```shell
+VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve Qwen/Qwen3-Next-80B-A3B-Instruct-FP8 --tensor-parallel-size 4 --max-model-len 32768  --no-enable-prefix-caching 
+```
+#### Step 4 Run Benchmark
+
+Open a new terminal and run the following command to execute the benchmark script inside the container.
+```shell
+docker exec -it Qwen3-next vllm bench serve \
+  --model "Qwen/Qwen3-Next-80B-A3B-Instruct" \
+  --dataset-name random \
+  --random-input-len 8192 \
+  --random-output-len 1024 \
+  --request-rate 10000 \
+  --num-prompts 16 \
+  --ignore-eos \
+  --trust-remote-code 
+```
+