This post delves into the inner workings of vLLM, a high-throughput LLM inference system, by breaking down its core components and advanced features. The analysis is based on commit 42172ad (August 9th, 2025), focusing on the V1 engine.

The LLM engine, the fundamental building block, enables high-throughput inference, initially in an offline setting. Key components include the vLLM config, processor, engine core client, and output processor. The engine core itself comprises the Model Executor, Structured Output Manager, and Scheduler. The Scheduler, managing waiting and running queues, relies on the KV cache manager, which is central to paged attention and maintains a pool of KV-cache blocks.

During model executor construction, procedures like initializing the device, verifying VRAM, setting up distributed settings, instantiating a model runner, and loading model weights are executed. The KV cache is initialized, and a dummy forward pass is run to determine how many KV cache blocks fit within the available VRAM. The generate function validates and feeds requests into the engine, tokenizing prompts and packing them into Engine Core Requests. The engine then repeatedly calls its step() function to process requests, supporting continuous batching for asynchronous operations.