Accelerating LLM Inference with vLLM

Explore the cutting-edge advancements in LLM inference performance through this 36-minute conference talk by Cade Daniel and Zhuohan Li. Dive into the world of vLLM, an open-source engine developed at UC Berkeley that has revolutionized LLM inference and serving. Learn about key performance-enhancing techniques such as paged attention and continuous batching. Discover recent innovations in vLLM, including Speculative Decoding, Prefix Caching, Disaggregated Prefill, and multi-accelerator support. Gain insights from industry case studies and get a glimpse of vLLM's future roadmap. Understand how vLLM's focus on production-readiness and extensibility has led to new system insights and widespread community adoption, making it a state-of-the-art, accelerator-agnostic solution for LLM inference.

Accelerating LLM Inference with vLLM