[Docs, ROCm] update install to cover ROCm with MI GPUs (#1915)

docs/start/install.md

@@ -7,7 +7,7 @@ You can install SGLang using any of the methods below.
 pip install --upgrade pip
 pip install "sglang[all]"
 
-# Install FlashInfer accelerated kernels
+# Install FlashInfer accelerated kernels (CUDA only for now)
 pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
 ```
 
@@ -22,7 +22,7 @@ cd sglang
 pip install --upgrade pip
 pip install -e "python[all]"
 
-# Install FlashInfer accelerated kernels
+# Install FlashInfer accelerated kernels (CUDA only for now)
 pip install flashinfer -i https://flashinfer.ai/whl/cu121/torch2.4/
 ```
 
@@ -42,6 +42,25 @@ docker run --gpus all \
     python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
 ```
 
+Note: To AMD ROCm system with Instinct/MI GPUs, it is recommended to use `docker/Dockerfile.rocm` to build images, example and usage as below:
+
+```bash
+docker build --build-arg SGL_BRANCH=v0.3.5 -t v0.3.5-rocm620 -f Dockerfile.rocm .
+
+alias drun='docker run -it --rm --network=host --device=/dev/kfd --device=/dev/dri --ipc=host \
+    --shm-size 16G --group-add video --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
+    -v $HOME/dockerx:/dockerx -v /data:/data'
+
+drun -p 30000:30000 \
+    -v ~/.cache/huggingface:/root/.cache/huggingface \
+    --env "HF_TOKEN=<secret>" \
+    v0.3.5-rocm620 \
+    python3 -m sglang.launch_server --model-path meta-llama/Llama-3.1-8B-Instruct --host 0.0.0.0 --port 30000
+
+# Till flashinfer backend available, --attention-backend triton --sampling-backend pytorch are set by default
+drun v0.3.5-rocm620 python3 -m sglang.bench_latency --batch-size 32 --input 1024 --output 128 --model amd/Meta-Llama-3.1-8B-Instruct-FP8-KV --tp 8 --quantization fp8
+```
+
 ## Method 4: Using docker compose
 
 <details>