xKV: Cross-Layer SVD for KV-Cache Compression

• URL: http://arxiv.org/abs/2503.18893v1
• Date: Mon, 24 Mar 2025 17:06:37 GMT
• Title: xKV: Cross-Layer SVD for KV-Cache Compression
• Authors: Chi-Chih Chang, Chien-Yu Lin, Yash Akhauri, Wei-Cheng Lin, Kai-Chiang Wu, Luis Ceze, Mohamed S. Abdelfattah,
• Abstract summary: Large Language Models (LLMs) with long context windows enable powerful applications but come at the cost of high memory consumption.<n>Recent studies attempted to merge KV-cache from multiple layers into shared representations.<n>We find that the dominant singular vectors are remarkably well-aligned across multiple layers of the KV-Cache.<n>xKV consolidates the KV-Cache of multiple layers into a shared low-rank subspace, significantly reducing KV-Cache sizes.
• Score: 8.250015628919098
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) with long context windows enable powerful applications but come at the cost of high memory consumption to store the Key and Value states (KV-Cache). Recent studies attempted to merge KV-cache from multiple layers into shared representations, yet these approaches either require expensive pretraining or rely on assumptions of high per-token cosine similarity across layers which generally does not hold in practice. We find that the dominant singular vectors are remarkably well-aligned across multiple layers of the KV-Cache. Exploiting this insight, we propose xKV, a simple post-training method that applies Singular Value Decomposition (SVD) on the KV-Cache of grouped layers. xKV consolidates the KV-Cache of multiple layers into a shared low-rank subspace, significantly reducing KV-Cache sizes. Through extensive evaluations on the RULER long-context benchmark with widely-used LLMs (e.g., Llama-3.1 and Qwen2.5), xKV achieves up to 6.8x higher compression rates than state-of-the-art inter-layer technique while improving accuracy by 2.7%. Moreover, xKV is compatible with the emerging Multi-Head Latent Attention (MLA) (e.g., DeepSeek-Coder-V2), yielding a notable 3x compression rates on coding tasks without performance degradation. These results highlight xKV's strong capability and versatility in addressing memory bottlenecks for long-context LLM inference. Our code is publicly available at: https://github.com/abdelfattah-lab/xKV.

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