Inside vLLM: How a Modern LLM Inference Engine Hits High Throughput

This deep-dive walks through the internal architecture of vLLM, the open-source engine widely used to serve large language models at scale. Working from a single commit (August 2025) and focusing on the current V1 engine, it starts with the simplest possible setup — offline, synchronous, single-GPU inference — and progressively layers in the machinery that turns that into an online, asynchronous, multi-GPU, multi-node serving system. The goal is a correct high-level mental model rather than an exhaustive tour of the code.

The core of the engine is the scheduling and memory subsystem. A scheduler with waiting and running queues decides which requests run in each step under either first-come-first-served or priority policies, while a KV-cache manager implements paged attention by handing out fixed-size blocks (default 16 tokens) from a pool that can number in the hundreds of thousands. Engine startup does the heavy lifting: assigning GPUs, loading weights, running a profiling forward pass to measure how much KV cache fits in VRAM, and — unless eager mode is forced — capturing CUDA graphs to cut per-launch overhead. Each generation step then repeats a tight loop of schedule, forward pass and sample, then postprocess with detokenization and stop-condition checks.

The piece is framed as the first in a series and previews the features that give vLLM its throughput edge: continuous batching (injecting new requests mid-run), chunked prefill, prefix caching, guided and speculative decoding, and disaggregated prefill/decode. For engineers evaluating or contributing to inference stacks like vLLM or SGLang, it’s a useful map of where scheduling, memory management, and parallelism actually live — the parts that determine whether a model deployment is cost-effective or not.