Entropy-Adaptive KV Cache Compression: A New Breakthrough in Large Model Inference Efficiency

This article introduces the entropy-based adaptive KV cache compression technology. Addressing the memory bottleneck of KV cache in large language model (LLM) inference, it uses the information entropy differences among attention heads to achieve adaptive compression. Compared with traditional uniform strategies, it improves compression efficiency by 2.6x, providing new ideas for accelerating LLM inference.

In LLM inference, KV cache supports the self-attention mechanism, but its memory grows linearly with sequence length, limiting the ability to handle long contexts. Traditional uniform compression uses the same compression rate for all attention heads, ignoring the differences in information distribution between heads, leading to low compression efficiency.

Core Insight: Different attention heads carry different amounts of information. The importance is quantified by calculating the information entropy of each head: high-entropy heads focus on global context (retain more cache), while low-entropy heads focus on specific patterns (can be aggressively compressed). Entropy reflects the uncertainty of attention weight distribution—high entropy means uniform distribution, low entropy means concentration in a few positions.

1. Dynamic compression rate allocation: Real-time entropy monitoring → entropy-based grading → differentiated compression; 2. Compression algorithms: Quantization compression (FP16/FP32 to INT8), sparsification (sparse processing of low-entropy head cache), dynamic cropping (selectively retaining historical tokens).

Experiments show: At the same 2x compression rate, the model has smaller performance loss and 2.6x higher compression efficiency; memory usage is reduced, and inference speed is accelerated. Technical features: Model-agnostic (applicable to Transformer-based LLMs), plug-and-play (no retraining required), adjustable parameters (compression rate and entropy threshold can be adjusted).

1. Long document processing: Supports longer contexts and reduces memory accumulation in multi-turn dialogues; 2. Edge devices: Runs LLMs on low-memory configurations and reduces energy consumption; 3. Batch processing: Increases single-device capacity and reduces per-token inference cost.

Current challenges: Extra overhead of real-time entropy calculation, large differences in task tolerance to compression, insufficient dynamic adaptation to input changes. Future directions: Combining learning methods to predict optimal strategies, multi-dimensional importance measurement, hardware co-optimization (dedicated accelerators).