Woosuk Kwon

vLLM is an open-source high-throughput and memory-efficient inference and serving engine for large language models, developed initially at UC Berkeley and widely adopted in production AI deployments. The project introduced PagedAttention, a novel memory management technique that significantly increases GPU utilization during LLM inference by managing key-value cache memory analogously to how operating systems manage virtual memory pages.\n\nThe engine is used in production by AI infrastructure teams at major technology companies, AI labs, and cloud providers who need to maximize the number of concurrent LLM requests served per GPU. vLLM benchmarks consistently demonstrate throughput improvements of 10 to 20 times over naive inference implementations, translating directly into lower cost per inference query at scale. The project is maintained by a community of contributors from both academia and industry.\n\nHigh-throughput LLM serving infrastructure is foundational to the economics of AI deployment. As inference costs represent an increasing share of AI operating budgets, the performance characteristics of the serving engine directly determine the financial viability of AI-powered products. vLLM dominant position in open-source LLM serving gives it deep adoption among infrastructure engineers and makes it a reference implementation against which commercial serving solutions are measured.