EntroKV: Entropy-Guided Dynamic Budget Allocation for KV-Cache Compression

The prohibitive memory footprint of the Key-Value (KV) cache imposes a critical bottleneck for efficient long-context LLM serving. Current compression techniques typically rely on static or uniform budget allocation, overlooking the significant heterogeneity in information density across attention heads. To address this, we introduce EntroKV, an entropy-driven dynamic budget allocation framework. Our method enables dynamic and rational allocation across layers, attention heads, and different tasks. We demonstrate that attention entropy serves as a robust proxy for compression sensitivity: heads with high entropy require larger retention budgets, whereas low-entropy heads can be aggressively compressed without accuracy degradation. Functioning as a lightweight, plug-and-play module, EntroKV optimizes budget scheduling in real-time and is compatible with diverse compression operators. Extensive experiments demonstrate that EntroKV consistently outperforms baselines, retaining $\sim$98% of full-cache performance at a 30% budget ratio with negligible computational overhead. Our code is available at https://anonymous.4open.science/r/EntroKV-D0C8/.