Woosuk Kwon
vLLM / UC Berkeley
Verified Proof of Work Artifacts
2 items catalogedEach artifact below represents an authenticated research publication, production code repository, or technical architectural framework directly authored or co-created by Woosuk Kwon. Every entry undergoes editorial source verification.
vLLM: High-Throughput and Memory-Efficient LLM Serving Engine
Engineered and open-sourced vLLM, an enterprise serving system providing continuous batching, chunked prefill, speculative decoding, and tensor parallelism, serving billions of production inference tokens daily worldwide.
Requires dedicated GPU clusters and NVLink interconnects for multi-node tensor-parallel deployments.
Efficient Memory Management for Large Language Model Serving with PagedAttention
Authored the seminal SOSP 2023 paper introducing PagedAttention, inspired by operating system virtual memory paging, allowing non-contiguous KV-cache storage and near-zero memory waste during high-concurrency LLM inference.
Paged cache lookup introduces minor pointer indirection overhead in memory-bandwidth-bound token generation loops.