Efficient Inference Techniques of Large Models in Real-world Applications:A Comprehensive Survey
摘要
In recent years,the technologies of LLMs have been rapidly developed,with their applications across various industries experiencing vigorous growth.From natural language processing to intelligent recommendations,and from information retrieval to automated writing,LLMs are becoming indispensable tools in many fields.However,with the diversification of application scena-rios and the increase in demands,the efficiency of LLM inference is becoming increasingly prominent.In practical applications,ra-pid and accurate inference capabilities are crucial for responding to user queries,handling large-scale data,and making real-time decisions.To address this challenge,academia has undertaken extensive research and exploration to enhance the inference efficiency of LLMs.This paper comprehensively surveys the literature on efficient LLM inference in practical application scenarios.Firstly,it introduces the principles of LLMs and analyzes how to improve LLM inference efficiency in practical application scenarios.Secondly,it proposes a taxonomy tailored for real-world applications,which consists of three main levels:algorithm optimization,parameter optimization,and system optimization.This survey summarizes and categorizes related work about LLMs.Finally,it discusses potential future research directions.