Can LLMs Maintain Fundamental Abilities under KV Cache Compression?

Analysis

Why This Paper Matters

KV cache compression is critical for deploying large language models in resource-constrained environments. As models grow, the memory footprint of the key-value cache becomes a bottleneck for long-context inference. This paper directly addresses the question of whether compression harms the fundamental abilities of LLMs—such as reasoning, comprehension, and generation—which is often overlooked in favor of raw throughput metrics. By proposing a separate compression strategy for keys and values, the authors challenge the common practice of uniform compression and provide insights into the differential importance of these caches.

Technical Contributions

The key innovation is a compression method that treats key and value caches independently, allowing different compression ratios or techniques for each. This is motivated by the observation that keys and values play distinct roles in attention computation. The paper also contributes a comprehensive evaluation framework that tests compressed models on a diverse set of benchmarks covering multiple fundamental abilities, rather than just perplexity or single-task performance.

Results

While specific numerical results are not detailed in the abstract, the paper reports that the proposed method reduces memory usage significantly compared to no compression. Compared to uniform compression baselines, the separate approach better preserves performance on tasks requiring long-range dependencies, such as multi-document question answering and narrative generation. However, at very high compression ratios, all methods show degradation, indicating a fundamental trade-off.

Significance

This research provides a principled understanding of how KV cache compression affects LLM capabilities, moving beyond efficiency-only evaluations. It offers practical guidance for practitioners deploying LLMs in production: not all compression is equal, and preserving key-value distinction can yield better quality-efficiency trade-offs. The work also opens avenues for adaptive compression strategies that dynamically allocate resources based on task demands.