vllm-setup

name	vllm-setup
description	Deploy a vLLM inference server on an NVIDIA DGX Station GB300 with validated container, GPU targeting, and tuning parameters. Use when the user asks to serve a model with vLLM, start a vLLM endpoint, or set up OpenAI-compatible inference on DGX Station.
metadata	{"publisher":"nvidia","hardware":"DGX Station GB300"}

vLLM Setup on DGX Station

Deploy a vLLM inference server on DGX Station with validated configuration.

Steps

1. Find the GB300 GPU index. Run:

   nvidia-smi --query-gpu=index,name --format=csv,noheader
   

   Identify the device index for the GB300 (typically device 1). Use this index for --gpus below. Do NOT use --gpus all — mixed coherency will cause CUDA failures.

2. Ask the user which model to serve. If they don't have a preference, suggest:

   • nvidia/Qwen3-235B-A22B-NVFP4 — large MoE model, fits in 279 GB HBM
   • meta-llama/Llama-3.1-70B-Instruct — solid general-purpose model
   • Qwen/Qwen3-8B — small model for testing

3. Check if the user has an HF_TOKEN. Many models require HuggingFace authentication. The token must be passed inline with -e HF_TOKEN="..." — do not rely on shell export in background Docker tasks.

4. Deploy the container. Use this validated configuration:

   docker pull nvcr.io/nvidia/vllm:26.01-py3
   
   docker run -d \
     --name vllm-server \
     --gpus '"device=<GB300_INDEX>"' \
     --ipc host \
     --ulimit memlock=-1 \
     --ulimit stack=67108864 \
     -p 8000:8000 \
     -e HF_TOKEN="<TOKEN>" \
     -v "$HOME/.cache/huggingface/hub:/root/.cache/huggingface/hub" \
     nvcr.io/nvidia/vllm:26.01-py3 \
     vllm serve "<MODEL>" \
       --max-model-len 32768 \
       --gpu-memory-utilization 0.9
   

   Container version: Use nvcr.io/nvidia/vllm:26.01-py3. Do NOT use 25.10 — it has a FlashInfer buffer overflow on DGX Station.

5. Wait for the server to be ready. Monitor logs:

   docker logs -f vllm-server
   

   Wait for the line indicating the server is listening on port 8000.

6. Test the server:

   curl http://localhost:8000/v1/chat/completions \
     -H "Content-Type: application/json" \
     -d '{
       "model": "<MODEL>",
       "messages": [{"role": "user", "content": "Hello"}],
       "max_tokens": 64
     }'
   

7. Report the result to the user, including:

   • Model loaded and serving on port 8000
   • GPU memory utilization
   • How to stop: docker stop vllm-server && docker rm vllm-server

Tuning parameters

Adjust these based on the user's workload:

Parameter	Default	Agent workloads	Throughput workloads
--max-model-len	32768	32768-65536	8192-16384
--gpu-memory-utilization	0.9	0.85-0.90	0.90-0.92
--enable-prefix-caching	off	Enable (multi-turn reuse)	Enable
--max-num-seqs	default	4-16 (lower latency)	32+ (higher throughput)