vLLM is an open-source inference and serving engine for teams that want to run large language models behind production APIs. Its core architecture uses PagedAttention-style KV-cache management, continuous batching and related optimizations to improve GPU utilization for real online workloads rather than only offline benchmark scripts.
The project exposes OpenAI-compatible serving paths, structured-output controls, metrics, benchmarking tools and deployment guidance for Docker, Kubernetes and production networking. Current documentation also covers areas such as the OpenAI Responses API surface, tool-use examples, LoRA, quantization, multimodal models and integrations with frameworks including LangChain, LlamaIndex, Codex and Claude Code.
vLLM is a strong default for throughput-heavy self-hosted inference, but teams should avoid treating generic benchmark multipliers as procurement guarantees. Performance depends on the model, GPU, context length, quantization, parallelism and request mix, so production buyers should run their own tests before sizing hardware or promising latency targets.
