Deep Dive into Efficient LLM Inference with Nano-vLLM

Nano-vLLM is a lightweight reimplementation of the vLLM inference engine that optimizes large language model performance through paged attention for memory management, KV caching to avoid recomputing previous tokens, and continuous batching that dynamically adds and removes requests. The system reduces memory waste from over 50% to under 5% by managing KV cache in fixed-size blocks, enables multi-GPU tensor parallelism for larger models, and supports prefix caching where shared prompt tokens are reused across requests.