Inside vLLM: how a high-throughput inference engine actually works
A detailed technical breakdown of vLLM walks through the engine core and scheduler, paged attention, continuous batching, chunked prefill, prefix caching, guided and speculative decoding, disaggregated prefill/decode, multi-GPU scaling, the distributed serving layer, and benchmarking with auto-tuning.
The analysis is pinned to a specific commit and structured inverse-pyramid style, building a full mental model before drilling into subsystems. It is aimed at anyone who wants to understand or contribute to vLLM or SGLang, and doubles as a map of where inference cost actually goes.
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