Ex-DeepMind engineer's deep dive dissects vLLM's inference architecture
solyarisoftware · x · 2026-09-15
Aleksa Gordić published "Inside vLLM: Anatomy of a High-Throughput LLM Inference System", a comprehensive breakdown of how a modern high-throughput LLM inference engine works, widely recommended for anyone learning inference systems.
Based on vLLM's V1 engine as of Aug 9, 2025 (commit 42172ad), the post has five parts:
- LLM Engine & Engine Core: scheduler, paged attention, continuous batching fundamentals
- Advanced features: chunked prefill, prefix caching, guided & speculative decoding, disaggregated P/D
- Scaling up: single-GPU to multi-GPU, multi-node execution
- Serving layer: distributed/concurrent web scaffolding
- Benchmarks & auto-tuning: latency and throughput measurement
It follows an inverse-pyramid structure, building an accurate top-level mental model before layering in detail, and constructs the system from first principles rather than treating vLLM as a black box. First in a series; ideal for understanding what happens between generate() and a token hitting your screen, or for contributing to vLLM/SGLang.
More from Infra
- Voodoo Dynamic Quant Goes MIT: Gradient Descent Picks Per-Tensor Quant Levels — 1ncehost · 2026-09-15
- Europe needs compute sovereignty at massive scale, not just AI regulation — VraserX · 2026-09-15
- EU compute report reportedly cites 45GW, making the case even weaker, critic says — eliebakouch · 2026-09-15
- Palantir and NVIDIA launch Sovereign AI OS Reference Architecture for enterprises — arieljalali · 2026-09-15
- Domingos: AI Computing Is So Inefficient Nvidia Won't Keep Its Moat — pmddomingos · 2026-09-15
- Animated comparison shows streaming feels faster but computes at the same speed — Shehao · 2026-09-15