vllm-deployment

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 <[e-mail]>","version":"1.0.3"}

vLLM Model Serving and Inference

Quick Start

Docker (CPU)

docker 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)

docker 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

Hardware	Minimum RAM	Recommended
CPU	2x model size	4x model size
GPU	Model size + 2GB	Model size + 4GB VRAM

• 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

# 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:

docker 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:

docker 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

# 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

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

Cloud VM Deployment

1. Provision Infrastructure

# 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

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

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

3. Verify External Access

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

4. Cleanup

# Stop container
docker stop <container-id>

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

Configuration Reference

Environment Variables

Variable	Purpose	Example
VLLM_CPU_KVCACHE_SPACE	KV cache size in GB (CPU)	4
VLLM_CPU_OMP_THREADS_BIND	CPU core binding (CPU)	0-7
CUDA_VISIBLE_DEVICES	GPU device selection	0,1
HF_TOKEN	HuggingFace authentication	hf_xxx

Docker Flags

Flag	Purpose
--shm-size=4g	Shared memory for IPC
--cap-add SYS_NICE	NUMA optimization (CPU)
--security-opt seccomp=unconfined	Memory policy syscalls (CPU)
--gpus all	GPU access
-p 8000:8000	Port mapping

vLLM Arguments

Argument	Purpose	Example
--model	Model name/path	<model-name>
--dtype	Data type	float32, auto, bfloat16
--max-model-len	Max context length	2048
--tensor-parallel-size	Multi-GPU parallelism	2

API Endpoints

Endpoint	Method	Purpose
/health	GET	Health check
/v1/models	GET	List available models
/v1/completions	POST	Text completion
/v1/chat/completions	POST	Chat completion
/metrics	GET	Prometheus metrics

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

Issue	Solution
Container exits immediately	Increase RAM or use smaller model
Slow inference (CPU)	Verify OMP thread binding configuration
Connection refused externally	Check firewall/security group rules
Model download fails	Set HF_TOKEN for gated models
Out of memory during inference	Reduce max_model_len or batch size
Port already in use	Change host port mapping
Warmup takes too long	Normal for large models (1-5 min)

References

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