Inside vLLM: Anatomy of a High-Throughput LLM Inference System (2025)
In a detailed blog post, Aleksa Gordić provides an anatomy of vLLM, a high-throughput LLM inference system, analyzing commit 42172ad from August 9, 2025. The post is structured into five parts: LLM engine and engine core, advanced features, scaling up, serving layer, and benchmarks and auto-tuning. It focuses on the V1 engine, while also exploring the now-deprecated V0 to show the project's evolution. Core components covered include scheduling, paged attention, and continuous batching, which enable high-throughput inference in offline settings. Advanced features discussed are chunked prefill, prefix caching, guided and speculative decoding, and disaggregated prefill/decode. The post also covers scaling from single-GPU to multi-GPU execution and the serving layer for distributed web scaffolding. The author notes that the first section may be dry but includes examples and visuals throughout. The target audience includes those curious about state-of-the-art LLM engines and potential contributors to vLLM or SGLang.
Provides a foundational understanding of vLLM's architecture, useful for developers building or contributing to high-throughput LLM inference systems.