The hardware accelerators for LLM-powered applications can be costly. Enter vLLM, an open-source machine learning library designed to enhance the throughput of LLM serving systems.
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Challenges with existing systems
- High throughput serving of LLMs requires numerous requests, and current systems struggle with the bulky sequence memory.
- Inefficient memory management results in system hindrances such as fragmentation and redundant duplication.
The revolutionary answer: vLLM & PagedAttention
- Researchers have introduced vLLM and PagedAttention, a newly designed attention algorithm, to resolve these issues.
- vLLM allows for minimal memory waste and efficiently manages attention keys and values. It provides up to 24 times more throughput than former systems.
The Mechanics of PagedAttention
- PagedAttention offers a novel approach to memory management by permitting continuous storage in non-contiguous memory spaces.
- It enhances memory efficiency resulting in better GPU utilization, with practically only 4% inefficiency.
Improved memory sharing and system performance
- PagedAttention significantly improves memory sharing, resulting in a 2.2 times speed gain while lowering memory usage by 55%.
- With vLLM, the throughput of known LLMs can be increased by 2-4 times without impacting accuracy or causing delay.
(arXiv) (github) (reference article)
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