KVzip: Query-Agnostic KV Cache Compression with Context Reconstruction
Authors: name: Jang-Hyun Kim, name: Jinuk Kim, name: Sangwoo Kwon, name: Jae W. Lee, name: Sangdoo Yun, name: Hyun Oh Song

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 3-4times and FlashAttention decoding latency by approximately
2times, 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.