探索压缩键值缓存提升大模型长文本推理效率的方法
Key, Value, Compress: A Systematic Exploration of KV Cache Compression Techniques
- 按原理与实现对多种KV缓存压缩技术分类
- 发现压缩会带来性能与延迟的权衡
- 适合关注大模型推理优化的研究者
大语言模型（LLMs）在生成文本、图像和视频内容方面表现出色。然而，随着上下文长度增加，注意力计算成本随标记数呈二次增长，带来显著的效率挑战。本文分析了多种键值（KV）缓存压缩策略，提出一个全面的分类体系，按其底层原理和实现技术对方法进行归类。进一步评估了这些方法对性能和推理延迟的影响，揭示了压缩带来的关键权衡。研究结果表明，压缩策略在处理长上下文场景时具有重要影响，为更高效的LLM实现提供了依据。
原文摘要 · Abstract (English)
Large language models (LLMs) have demonstrated exceptional capabilities in generating text, images, and video content. However, as context length grows, the computational cost of attention increases quadratically with the number of tokens, presenting significant efficiency challenges. This paper presents an analysis of various Key-Value (KV) cache compression strategies, offering a comprehensive taxonomy that categorizes these methods by their underlying principles and implementation techniques. Furthermore, we evaluate their impact on performance and inference latency, providing critical insights into their effectiveness. Our findings highlight the trade-offs involved in KV cache compression and its influence on handling long-context scenarios, paving the way for more efficient LLM implementations.
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