vLLM explained: how PagedAttention and continuous batching power high-throughput LLM serving
goyalshaliniuk · x · 2026-11-02
Kicking off a thread on 5 inference engines worth knowing, the author covers vLLM first:
- Positioning: built for high-throughput LLM serving; 96.1k stars on GitHub
- Core tech: PagedAttention and continuous batching to manage concurrent requests efficiently
- Why it matters: running an LLM is one thing; serving it to thousands of users with lower latency and cost is another — inference engines handle the latter
vLLM is the popular default for production AI applications.
Related event: A Look at 5 LLM Inference Engines: vLLM and SGLang Lead the Pack(3 posts)→
More from Infra
- Kioxia up 216% in a year but down 68% from its June 2026 peak — zephyr_z9 · 2026-11-02
- Intel plunges 29%, worst day since the 1970s, wiping $12B off US government stake — emmanuelvivier · 2026-11-02
- Researcher hoards nearly 1TB of DRAM, refuses to sell a stick to data centers — kevinnbass · 2026-11-02
- Self-Hosting LLMs at a Law Firm: Infra and Data Governance Harder Than the Model — EducationalGroup7245 · 2026-11-02
- Pika speeds up video generation 60% with pure inference engineering in H3 Flex — ghumare64 · 2026-11-02
- Predictions: Opus 5.5-tier models at V4.1F inference cost within a year — teortaxesTex · 2026-11-02