Name: VLLM: Easy, Fast, and Cheap LLM Serving for Everyone - Woosuk Kwon, UC Berkeley & Xiaoxuan Liu, UCB
Start: 2024-09-19T14:00:00-0700
End: 2024-09-19T14:25:00-0700

September 18-19, 2024
San Francisco, California
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Note: The schedule is subject to change.

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IMPORTANT NOTE: Timing of sessions and room locations are subject to change.

Thursday September 19, 2024 2:00pm - 2:25pm PDT

Room B

We will present vLLM, an open-source high-performance LLM inference engine built on top of PyTorch. Starting as a research project at UC Berkeley, vLLM has been one of the fastest and most popular LLM inference solutions in industry, reaching 20K+ stars and 350+ contributors. In this talk, we will cover how vLLM adopts various LLM inference optimizations and how it supports various AI accelerators such as AMD GPUs, Google TPUs, and AWS Inferentia. Also, we will discuss how vLLM benefits from PyTorch 2 and its ecosystem.

Speakers

Lily Liu

PhD Student, UCB

Lily (Xiaoxuan) Liu is a PhD student at UC Berkeley, working with Professors Ion Stoica and Alvin Cheung. Her research focuses on machine learning systems, particularly optimizing latency for LLM inference and addressing memory bottlenecks in LLM systems. Her recent work explores... Read More →

Woosuk Kwon

PhD Student, UC Berkeley

Woosuk Kwon is a Ph.D. student at UC Berkeley, advised by Prof. Ion Stoica. He is interested in building practical, flexible, and high-performance software systems for emerging applications such as large language models. Recently, he has been developing vLLM, a high-performance open-source... Read More →

Thursday September 19, 2024 2:00pm - 2:25pm PDT
Room B

Breakout Sessions

Audience Intermediate

PyTorch Conference 2024

Lily Liu

Woosuk Kwon

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