Related papers: A Survey on Efficient Inference for Large Language Models

A Survey on Efficient Inference for Large Language Models

URL: http://arxiv.org/abs/2404.14294v3
Date: Fri, 19 Jul 2024 04:47:36 GMT
Title: A Survey on Efficient Inference for Large Language Models
Authors: Zixuan Zhou, Xuefei Ning, Ke Hong, Tianyu Fu, Jiaming Xu, Shiyao Li, Yuming Lou, Luning Wang, Zhihang Yuan, Xiuhong Li, Shengen Yan, Guohao Dai, Xiao-Ping Zhang, Yuhan Dong, Yu Wang,
Abstract summary: Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. The substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. This paper presents a comprehensive survey of the existing literature on efficient LLM inference.
Score: 25.572035747669275
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Large Language Models (LLMs) have attracted extensive attention due to their remarkable performance across various tasks. However, the substantial computational and memory requirements of LLM inference pose challenges for deployment in resource-constrained scenarios. Efforts within the field have been directed towards developing techniques aimed at enhancing the efficiency of LLM inference. This paper presents a comprehensive survey of the existing literature on efficient LLM inference. We start by analyzing the primary causes of the inefficient LLM inference, i.e., the large model size, the quadratic-complexity attention operation, and the auto-regressive decoding approach. Then, we introduce a comprehensive taxonomy that organizes the current literature into data-level, model-level, and system-level optimization. Moreover, the paper includes comparative experiments on representative methods within critical sub-fields to provide quantitative insights. Last but not least, we provide some knowledge summary and discuss future research directions.

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