Efficient Memory Management for Large Language Model Serving with PagedAttention
Kwon et al., 2023, Efficient Memory Management for Large Language Model Serving with PagedAttention (Proceedings of the 29th Symposium on Operating Systems Principles (SOSP ’23))
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7.1 Kernel Microbenchmark
- What does PagedAttention cost at the kernel level?Accessing block tables, extra branches and variable sequence lengths made the attention kernel 20–26% slower than FasterTransformer’s, but this affects only the attention operator, and end-to-end throughput is far higher.
- Kwon et al., 2023, Efficient Memory Management for Large Language Model Serving with PagedAttention (Proceedings of the 29th Symposium on Operating Systems Principles (SOSP ’23)), §7.1
Source
- Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph E. Gonzalez, Hao Zhang and Ion Stoica. 2023. Efficient Memory Management for Large Language Model Serving with PagedAttention. In Proceedings of the 29th Symposium on Operating Systems Principles (SOSP ’23). doi:10.1145/3600006.3613165 https://arxiv.org/abs/2309.06180