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Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks; however, these models face critical efficiency challenges due to their resource-intensive nature. This paper presents a comprehensive analysis of recent advances in LLM inference optimization, systematically categorizing optimization techniques into three fundamental domains: computation, memory, and system-level enhancements. We examine key approaches including attention mechanism improvements, key-value cache optimizations, efficient decoding strategies, batching techniques, and model compression methods. These innovations directly address critical performance bottlenecks in memory utilization, computational efficiency, and response latency. Breakthrough technologies such as FlashAttention, PagedAttention, speculative decoding, and advanced quantization methods have demonstrated substantial improvements in inference performance. Our analysis further explores the inherent trade-offs between various optimization strategies and their practical implications for deploying LLMs in production environments.

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A Review of Optimization Techniques for Large Language Model Inference

  • 会议论文

  • 发表日期：

    2026年

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  • Yujia Cao,
  • Xi Tao,
  • Weipeng Cao,
  • Chuanfei Xu,
  • Zhong Ming

摘要

Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks; however, these models face critical efficiency challenges due to their resource-intensive nature. This paper presents a comprehensive analysis of recent advances in LLM inference optimization, systematically categorizing optimization techniques into three fundamental domains: computation, memory, and system-level enhancements. We examine key approaches including attention mechanism improvements, key-value cache optimizations, efficient decoding strategies, batching techniques, and model compression methods. These innovations directly address critical performance bottlenecks in memory utilization, computational efficiency, and response latency. Breakthrough technologies such as FlashAttention, PagedAttention, speculative decoding, and advanced quantization methods have demonstrated substantial improvements in inference performance. Our analysis further explores the inherent trade-offs between various optimization strategies and their practical implications for deploying LLMs in production environments.