Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving

• URL: http://arxiv.org/abs/2503.24000v1
• Date: Mon, 31 Mar 2025 12:23:31 GMT
• Title: Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving
• Authors: Wei Gao, Xinyu Zhou, Peng Sun, Tianwei Zhang, Yonggang Wen,
• Abstract summary: Key-Value cache (textttKV textttcache) compression has emerged as a promising technique to optimize Large Language Model (LLM) serving.<n>It primarily decreases the memory consumption of textttKV textttcache to reduce the computation cost.<n>Despite the development of many compression algorithms, their applications in production environments are still not prevalent.
• Score: 23.2180736755494
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Key-Value cache (\texttt{KV} \texttt{cache}) compression has emerged as a promising technique to optimize Large Language Model (LLM) serving. It primarily decreases the memory consumption of \texttt{KV} \texttt{cache} to reduce the computation cost. Despite the development of many compression algorithms, their applications in production environments are still not prevalent. In this paper, we revisit mainstream \texttt{KV} \texttt{cache} compression solutions from a practical perspective. Our contributions are three-fold. First, we comprehensively review existing algorithmic designs and benchmark studies for \texttt{KV} \texttt{cache} compression and identify missing pieces in their performance measurement, which could hinder their adoption in practice. Second, we empirically evaluate representative \texttt{KV} \texttt{cache} compression methods to uncover two key issues that affect the computational efficiency: (1) while compressing \texttt{KV} \texttt{cache} can reduce memory consumption, current implementations (e.g., FlashAttention, PagedAttention) do not optimize for production-level LLM serving, resulting in suboptimal throughput performance; (2) compressing \texttt{KV} \texttt{cache} may lead to longer outputs, resulting in increased end-to-end latency. We further investigate the accuracy performance of individual samples rather than the overall performance, revealing the intrinsic limitations in \texttt{KV} \texttt{cache} compression when handling specific LLM tasks. Third, we provide tools to shed light on future \texttt{KV} \texttt{cache} compression studies and facilitate their practical deployment in production. They are open-sourced in \href{https://github.com/LLMkvsys/rethink-kv-compression}{https://github.com/LLMkvsys/rethink-kv-compression}.

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