vllm-server

docker run --runtime nvidia --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -p 8000:8000 \
  --ipc=host \
  vllm/vllm-openai:latest \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --api-key your-secret-key

services:
  vllm:
    image: vllm/vllm-openai:latest
    runtime: nvidia
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}
    volumes:
      - model-cache:/root/.cache/huggingface
    ports:
      - "8000:8000"
    ipc: host
    command: >
      --model meta-llama/Llama-3.1-70B-Instruct
      --tensor-parallel-size 2
      --max-model-len 32768
      --gpu-memory-utilization 0.90
      --api-key ${VLLM_API_KEY}
    restart: unless-stopped
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8000/health"]
      interval: 30s
      timeout: 10s
      retries: 3
 
volumes:
  model-cache:

# Split one model across 4 GPUs
vllm serve meta-llama/Llama-3.1-70B-Instruct \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.90

# AWQ quantization (70B on 2x A100 40GB)
vllm serve casperhansen/llama-3-70b-instruct-awq \
  --quantization awq \
  --tensor-parallel-size 2
 
# GPTQ quantization
vllm serve TheBloke/Llama-2-70B-Chat-GPTQ \
  --quantization gptq
 
# FP8 (H100 NVL native)
vllm serve meta-llama/Llama-3.1-405B-Instruct \
  --quantization fp8 \
  --tensor-parallel-size 8

vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --enable-auto-tool-choice \
  --tool-call-parser llama3_json \
  --guided-decoding-backend outlines

vllm serve meta-llama/Llama-3.1-8B-Instruct \
  --enable-lora \
  --lora-modules sql-lora=/path/to/sql-lora \
                 code-lora=/path/to/code-lora \
  --max-lora-rank 64

# Maximize throughput for batch workloads
vllm serve <model> \
  --max-num-seqs 256 \          # max concurrent sequences
  --max-num-batched-tokens 8192 \ # tokens per batch
  --gpu-memory-utilization 0.95 \ # use 95% VRAM
  --swap-space 4                  # CPU swap (GiB)
 
# Minimize latency for interactive use
vllm serve <model> \
  --max-num-seqs 32 \
  --enforce-eager              # disable CUDA graph capture

# Install benchmark tool
pip install vllm
 
# Run throughput benchmark
python -m vllm.entrypoints.openai.run_batch \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --input-file prompts.jsonl \
  --output-file results.jsonl
 
# Benchmark with vllm bench
vllm bench throughput \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --num-prompts 1000 \
  --input-len 512 \
  --output-len 128

# Check running server stats
curl http://localhost:8000/metrics  # Prometheus metrics
 
# Key metrics to watch:
# vllm:num_requests_running       - active requests
# vllm:gpu_cache_usage_perc       - KV cache utilization
# vllm:generation_tokens_per_s    - throughput
# vllm:time_to_first_token_ms     - TTFT latency
# vllm:e2e_request_latency_seconds - end-to-end latency