vLLM-Encyclopedia

vLLM documentation-first workflow for vLLM-specific questions, troubleshooting, command planning, OpenAI-compatible serving behavior, model-loading/runtime behavior, scheduler/prefix-caching/chunked-prefill semantics, quantization and kernel-path checks, multimodal/tool-calling and Responses/OpenAI surface review, build/install/source-tree work, and diagnostics when the actual product/runtime layer is vLLM. Use when the request is clearly about vLLM itself: the `vllm` Python package/CLI, `vllm serve`, `vllm.entrypoints.openai.api_server`, OpenAI-compatible endpoints implemented by vLLM, engine arguments, cache/scheduler behavior, tensor-parallel/pipeline-parallel/expert-parallel settings, paged attention, prefix caching, chunked prefill, speculative decoding, structured outputs, tool calling, reasoning parsers, model support, quantization backends, flash-attention/FlashInfer/XFormers/Triton/kernel selection, or vLLM source build/runtime behavior where vLLM-specific semantics matter. Do not use for generic LLM