WindowKV: Task-Adaptive Group-Wise KV Cache Window Selection for Efficient LLM Inference

• URL: http://arxiv.org/abs/2503.17922v2
• Date: Thu, 27 Mar 2025 14:11:37 GMT
• Title: WindowKV: Task-Adaptive Group-Wise KV Cache Window Selection for Efficient LLM Inference
• Authors: Youhui Zuo, Sibo Wei, Chen Zhang, Zhuorui Liu, Wenpeng Lu, Dawei Song,
• Abstract summary: We propose a novel task-adaptive KV cache window selection method, WindowKV.<n>We show that WindowKV maintains a performance comparable to full KV cache retention while using only 12% of the original KV cache.<n>Our method also achieves state-of-the-art results in the Needle-in-a-Haystack evaluation, highlighting its effectiveness and robustness.
• Score: 9.572076809796448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the advancements in long-context inference capabilities of large language models (LLMs), the KV cache has become one of the foundational components. However, its substantial GPU memory consumption makes KV cache compression a key technique for enabling efficient LLM inference in industrial scenarios. While recent studies have focused on optimizing the memory occupied by the KV cache, they overlook two critical factors: preserving semantic coherence and considering task-specific characteristic during compression. To address these limitations, we propose a novel task-adaptive KV cache window selection method, WindowKV. WindowKV dynamically selects local semantic windows consisting of consecutive tokens, according to task-specific characteristics, ensuring the retained KV cache captures continuous, essential context. Additionally, we introduce an intra-group layer KV cache indices sharing strategy to reduce computational overhead, achieving a balance between performance and efficiency. We rigorously evaluate WindowKV on the LongBench benchmark, and the results demonstrate that it maintains a performance comparable to full KV cache retention while using only 12% of the original KV cache, significantly reducing memory requirements. Furthermore, our method also achieves state-of-the-art results in the Needle-in-a-Haystack evaluation, highlighting its effectiveness and robustness.

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