Inside vLLM: Anatomy of a High-Throughput LLM Inference System
Summary
Aleks Gordic presents a detailed breakdown of vLLM's architecture for high-throughput LLM inference, highlighting core components such as paging attention, continuous batching, prefix caching, and KV-cache management. The post traces the path from a single-GPU offline prototype to multi-GPU, multi-node online serving, and covers advanced features like chunked prefill, prefix caching, guided decoding, speculative decoding, and disaggregated prefill/decode, supported by diagrams and examples.