Computer Science ›› 2026, Vol. 53 ›› Issue (1): 12-28.doi: 10.11896/jsjkx.250300030

• Research and Application of Large Language Model Technology • Previous Articles     Next Articles

Efficient Inference Techniques of Large Models in Real-world Applications:A Comprehensive Survey

LIU Lilong1, LIU Guoming2, QI Baoyuan3, DENG Xueshan4, XUE Dizhan4, QIAN Shengsheng4   

  1. 1 Henan Institute of Advanced Technology, Zhengzhou University, Zhengzhou 450003, China;
    2 Group Technical Committee, Xiaomi Automobile Technology Co., Ltd., Beijing 100085, China;
    3 Group Technical Committee, Beijing Xiaomi Pinecone Electronics Co., Ltd., Beijing 100085, China;
    4 State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2025-03-06 Revised:2025-07-02 Published:2026-01-08
  • About author:LIU Lilong,born in 2000,postgraduate.His main research interests include artificial intelligence and natural language processing.
    QIAN Shengsheng,born in 1991,Ph.D,professor,is a member of CCF(No.77702M).His main research interests include data mining and multimedia content analysis.
  • Supported by:
    National Key Research and Development Program of China(2023YFC3310700) and National Natural Science Foundation of China(62276257).

Abstract: 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.

Key words: Large language models, Efficient inference, Practical application scenarios, Algorithm optimization, Parameter optimization, System optimization

CLC Number: 

  • TP391
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