Comparative Characterization of KV Cache Management Strategies for LLM Inference
Efficient inference with Large Language Models (LLMs) increasingly relies on Key-Value (KV) caches to store previously computed key and value vectors at each layer. These caches are essential to minimize redundant computation during autoregressive token generation, lowering computational complexity from quadratic to linear. However, the growth of KV caches has posed significant system-level challenges, particularly as model sizes increase, context lengths grow, and concurrent requests compete for limited memory resources. Even though several recent frameworks for KV cache management have emerged, their comparative trade-offs in memory consumption and inference performance have not been fu...
| Authors | Oteo Mamo, Olga Kogiou, Hyunjin Yi et al. |
|---|---|
| Primary category | cs.AR |
| Published | 6 April 2026 |
| Last updated at source | 15 September 2026 |
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