KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction

• URL: http://arxiv.org/abs/2505.23416v1
• Date: Thu, 29 May 2025 13:05:47 GMT
• Title: KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
• Authors: Jang-Hyun Kim, Jinuk Kim, Sangwoo Kwon, Jae W. Lee, Sangdoo Yun, Hyun Oh Song,
• Abstract summary: Transformer-based large language models (LLMs) cache context as key-value ( KV) pairs during inference.<n>As context length grows, KV cache sizes expand, leading to substantial memory overhead and increased attention latency.<n>This paper introduces KVzip, a query-agnostic KV cache eviction method enabling effective reuse of compressed KV caches across diverse queries.
• Score: 37.489744618357655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transformer-based large language models (LLMs) cache context as key-value (KV) pairs during inference. As context length grows, KV cache sizes expand, leading to substantial memory overhead and increased attention latency. This paper introduces KVzip, a query-agnostic KV cache eviction method enabling effective reuse of compressed KV caches across diverse queries. KVzip quantifies the importance of a KV pair using the underlying LLM to reconstruct original contexts from cached KV pairs, subsequently evicting pairs with lower importance. Extensive empirical evaluations demonstrate that KVzip reduces KV cache size by 3-4$\times$ and FlashAttention decoding latency by approximately 2$\times$, with negligible performance loss in question-answering, retrieval, reasoning, and code comprehension tasks. Evaluations include various models such as LLaMA3.1-8B, Qwen2.5-14B, and Gemma3-12B, with context lengths reaching up to 170K tokens. KVzip significantly outperforms existing query-aware KV eviction methods, which suffer from performance degradation even at a 90% cache budget ratio under multi-query scenarios.

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