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Summary of Efficient and Economic Large Language Model Inference with Attention Offloading, by Shaoyuan Chen et al.


Efficient and Economic Large Language Model Inference with Attention Offloading

by Shaoyuan Chen, Yutong Lin, Mingxing Zhang, Yongwei Wu

First submitted to arxiv on: 3 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)

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GrooveSquid.com Paper Summaries

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Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
The paper introduces a novel approach to improving the efficiency and cost-effectiveness of serving large language models (LLMs) in real-world applications. The authors identify a mismatch between the computation demands of LLMs and the capabilities of modern accelerators, which is exacerbated by the autoregressive nature of the attention operator. They propose an “attention offloading” technique that leverages cheap, memory-optimized devices for the attention operator while utilizing high-end accelerators for other parts of the model. The heterogeneous setup is designed to maximize overall performance and cost efficiency. The authors conduct a comprehensive analysis and experiments to validate their theory, developing Lamina, an LLM inference system that incorporates attention offloading. Results show that Lamina can provide 1.48x-12.1x higher estimated throughput per dollar than homogeneous solutions.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper helps solve a problem with using large language models (LLMs) in real life. These models are really good at generating text, but they use too much computer power and cost a lot to run. The authors figured out that this is because the part of the model that does most of the work, called attention, uses a lot of memory and doesn’t get along well with modern computers. They came up with an idea to split the attention job into smaller parts and do some of it on cheaper computers while keeping the rest on more powerful ones. This way, they can make LLMs faster and cheaper to use.

Keywords

» Artificial intelligence  » Attention  » Autoregressive  » Inference