A comprehensive production deployment guide for vLLM, covering Docker single-GPU and multi-GPU setups, Kubernetes manifests with startup/readiness/liveness probes, KEDA-based autoscaling triggered by Prometheus queue depth metrics, OpenAI-compatible API configuration with secure credential handling, PagedAttention and V1 engine architecture internals, quantization options (AWQ, GPTQ, FP8), performance tuning parameters like --gpu-memory-utilization and --max-model-len, Grafana dashboard setup, and a production readiness checklist.
Table of contents
How to Deploy vLLM in ProductionTable of ContentsvLLM Architecture Essentials for Production EngineersDocker Deployment for vLLMKubernetes Deployment for vLLM at ScaleOpenAI-Compatible API ConfigurationPerformance Optimization for Production WorkloadsMonitoring and ObservabilitySecurity and Reliability in ProductionProduction Readiness ChecklistSort: