vLLM Architecture DissectedReading the source top-down

A Chinese-to-English study site that dissects vllm-project/vllm, a high-throughput LLM inference engine, layer by layer; every step links straight to the corresponding source lines on GitHub, pinned to tag v0.25.1.

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Engine main line

Scheduler

GitHub repository

Top-down

From EngineArgs → LLMEngine → EngineCore → Scheduler → Executor → Worker/ModelRunner → model loading → KV Cache → Attention → sampling → distributed, expanded layer by layer.

Source location

Every key file carries a SrcLink that opens the exact lines on GitHub; the version is pinned to v0.25.1, bumping it only changes one place.

Real code inlined

Not just pointers — the 5-15 lines of real Python source for key functions are pasted directly into the page, with prose in English explaining what is happening before and after.

Design motivation and boundaries

Beyond responsibilities, each page explains why the design is shaped this way, plus edge cases and how failure modes are handled.

v1 architecture and PagedAttention

v0.25 has switched to the v1 architecture: EngineCore runs as a ZMQ background process, Scheduler does continuous batching and preemption, KVCacheCoordinator + BlockPool implement PagedAttention and prefix caching.

Reuse official docs

This site focuses on architecture dissection and source location; for installation and usage, refer to the official docs — we don't re-author usage documentation.