The risk of KV cache compression

2026-07-01

AI Summary

This work addresses the challenge of excessive memory consumption by KV caches in Transformer inference, which severely limits efficient long-sequence processing. While existing compression approaches lack rigorous theoretical foundations, this paper presents the first systematic analysis of the fundamental limits of KV cache compression through the lens of minimax risk. By integrating information theory and statistical learning theory, the study establishes a formal connection between the intrinsic compressibility of the cache and the feasibility of compression under causal attention mechanisms. Building on these theoretical insights, the authors derive principled design guidelines for effective KV cache compression. Empirical validation on the LongBench benchmark demonstrates that the proposed method achieves strong performance while offering both theoretical guarantees and practical deployability.

Abstract

Transformer inference on long sequences is expensive because softmax attention repeatedly reads from a large KV cache. The prevalent approach to this bottleneck is KV cache compression, which replaces the full cache with a compact summary. Despite its practical importance, the design of such summaries is largely driven by empirical experimentation. On the theoretical side, existing results show that KV cache compression can be impossible in the worst case, but offer little systematic guidance for designing algorithms in regimes where accurate compression is possible. We bridge this gap by characterizing the minimax risk of KV cache compression in terms of the intrinsic compressibility of a cache, revealing when and how accurate compression is possible. These results yield novel design principles for KV cache compression under causal masking that map efficiently to prefill and autoregressive decoding while achieving minimax-optimal risk. We instantiate these principles in a practical algorithm and report promising performance on LongBench in targeted experiments. Overall, our results provide a principled avenue for practical KV cache compression with theoretical guarantees.

Problem

• KV cache compression
• minimax risk
• intrinsic compressibility
• causal masking
• theoretical guarantees

Innovation

• KV cache compression
• minimax risk
• intrinsic compressibility
• causal masking
• theoretical guarantees