How vLLM Processes an Inference Request from API Call to GPU Execution

A technical deep-dive published on DEV Community traces the full lifecycle of a single inference request through vLLM V1, the open-source LLM serving framework. The article, adapted from a Chinese engineering post on Zhihu, follows a request from the public LLM.generate() call through inter-process communication, scheduling, and GPU execution. In vLLM 0.22's V1 architecture, the engine separates concerns across a main process and a child EngineCore process connected via IPC queues. The EngineCore runs a continuous busy loop that handles scheduling, model execution, and output updates in three distinct stages. The series, planned across three parts, aims to explain internals such as PagedAttention, CUDA kernels, and FlashAttention for engineers already familiar with Transformer inference.