---

name: vllm-deployment description: | Deploy vLLM for high-performance LLM inference. Covers Docker CPU/GPU deployments and cloud VM provisioning with OpenAI-compatible API endpoints. license: MIT tags:

• vllm
• llm
• inference
• gpu
• ai
• machine-learning
• docker
• openai-api metadata: author: Stakpak team@stakpak.dev version: "1.0.3"

---

vLLM Model Serving and Inference

Quick Start

Docker (CPU)

Copydocker run --rm -p 8000:8000 \
  --shm-size=4g \
  --cap-add SYS_NICE \
  --security-opt seccomp=unconfined \
  -e VLLM_CPU_KVCACHE_SPACE=4 \
  <vllm-cpu-image> \
  --model <model-name> \
  --dtype float32
# Access: http://localhost:8000

Docker (GPU)

Copydocker run --rm -p 8000:8000 \
  --gpus all \
  --shm-size=4g \
  <vllm-gpu-image> \
  --model <model-name>
# Access: http://localhost:8000

Docker Deployment

1. Assess Hardware Requirements

• Check model documentation for specific requirements
• Consider quantized variants to reduce memory footprint
• Allocate 50-100GB storage for model downloads

2. Pull the Container Image

Copy# CPU image (check vLLM docs for latest tag)
docker pull <vllm-cpu-image>

# GPU image (check vLLM docs for latest tag)
docker pull <vllm-gpu-image>

Notes:

• Use CPU-specific images for CPU inference
• Use CUDA-enabled images matching your GPU architecture
• Verify CPU instruction set compatibility (AVX512, AVX2)

3. Configure and Run

CPU Deployment:

Copydocker run --rm \
  --shm-size=4g \
  --cap-add SYS_NICE \
  --security-opt seccomp=unconfined \
  -p 8000:8000 \
  -e VLLM_CPU_KVCACHE_SPACE=4 \
  -e VLLM_CPU_OMP_THREADS_BIND=0-7 \
  <vllm-cpu-image> \
  --model <model-name> \
  --dtype float32 \
  --max-model-len 2048

GPU Deployment:

Copydocker run --rm \
  --gpus all \
  --shm-size=4g \
  -p 8000:8000 \
  <vllm-gpu-image> \
  --model <model-name> \
  --dtype auto \
  --max-model-len 4096

4. Verify Deployment

Copy# Check health
curl http://localhost:8000/health

# List models
curl http://localhost:8000/v1/models

# Test inference
curl http://localhost:8000/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "<model-name>", "prompt": "Hello", "max_tokens": 10}'

5. Update

Copydocker pull <vllm-image>
docker stop <container-id>
# Re-run with same parameters

Cloud VM Deployment

1. Provision Infrastructure

Copy# Create security group with rules:
# - TCP 22 (SSH)
# - TCP 8000 (API)

# Launch instance with:
# - Sufficient RAM/VRAM for model
# - Docker pre-installed (or install manually)
# - 50-100GB root volume
# - Public IP for external access

2. Connect and Deploy

Copyssh -i <key-file> <user>@<instance-ip>

# Install Docker if not present
# Pull and run vLLM container (see Docker Deployment section)

3. Verify External Access

Copy# From local machine
curl http://<instance-ip>:8000/health
curl http://<instance-ip>:8000/v1/models

4. Cleanup

Copy# Stop container
docker stop <container-id>

# Terminate instance to stop costs
# Delete associated resources (volumes, security groups) if temporary

Configuration Reference

Environment Variables

Docker Flags

vLLM Arguments

API Endpoints

Production Checklist

• Verify model fits in available memory
• Configure appropriate data type for hardware
• Set up firewall/security group rules
• Test API endpoints before production use
• Configure monitoring (Prometheus metrics)
• Set up health check alerts
• Document model and configuration used
• Plan for model updates and rollbacks

Troubleshooting

References

• vLLM Documentation
• vLLM CPU Installation
• vLLM GPU Installation
• vLLM Supported Models
• OpenAI API Reference
• HuggingFace Model Hub