vLLM is a full-featured serving engine for self-hosting open-weight language models at scale, capable of handling thousands of requests through innovations like PagedAttention and continuous batching. The post evaluates vLLM's performance characteristics on NVIDIA hardware, demonstrating how bandwidth constraints, quantization strategies, and model architecture choices affect throughput for local LLM inference.