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Local AI Inference Recipes

Run Models Optimally On Your Local NVIDIA AI System

DGX Station

10 models available on this hardware at 748 GB.

Recipe details

Execution
Container
Backend version
v0.31.0
Backend min version
v0.25.1
Peak GPU Memory
Draft model
Speculative Decoding
Concurrency
64
Topology
1× DGX Station
Served model
deepseek-v4-flash-0731
Port
8000
Recipe ID
deepseek/deepseek-v4-flash-0731/dgx-station/linux/vllm/fp4-fp8-mixed

Selected recipe

OptimizedRecommended

DGX Station · Linux · vLLM · FP4 + FP8 Mixed

Start container

Set HF_TOKEN in the host environment. Container image: vllm/vllm-openai:v0.31.0

docker run --rm -it \
  --gpus all \
  --ipc host \
  --network host \
  --shm-size=16g \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  -e HF_TOKEN=$HF_TOKEN \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --entrypoint /bin/bash \
  vllm/vllm-openai:v0.31.0

Launch

vllm serve deepseek-ai/DeepSeek-V4-Flash-0731 \
  --served-model-name deepseek-v4-flash-0731 \
  --host 0.0.0.0 --port 8000 \
  --trust-remote-code \
  --tensor-parallel-size 1 \
  --kv-cache-dtype fp8_ds_mla \
  --block-size 256 \
  --max-model-len 1048576 \
  --max-num-seqs 64 \
  --max-num-batched-tokens 32768 \
  --max-cudagraph-capture-size 512 \
  --gpu-memory-utilization 0.88 \
  --enable-prefix-caching \
  --enable-prompt-tokens-details \
  --async-scheduling \
  --enable-chunked-prefill \
  --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"probabilistic"}' \
  --tokenizer-mode deepseek_v4 \
  --tool-call-parser deepseek_v4 --enable-auto-tool-choice \
  --reasoning-parser deepseek_v4 \
  --generation-config vllm \
  --compilation-config '{"custom_ops":["all"]}'

Measured performance

Optimized

Measured 2026-10-14 · NVIDIA DGX Station

Decode Throughput
2,059.10 tok/s
SPEED-Bench Qualitative Coding
1× NVIDIA DGX Station (GB300)Ubuntu 24.04.4 LTSDriver 610.43.03CUDA 13.0