Long-context Large Language Models (LLMs) enable powerful applications but incur high memory costs due to the key-value states (KV-Cache). Recent studies attempt to share KV-Cache across layers, but these approaches either require expensive pretraining or rely on per-token cross-layer cosine similarity that is often limited in practice. We show, via Centered Kernel Alignment (CKA), that the dominant singular vectors of KV-Cache are well aligned across layers. Motivated by this observation, we propose xKV, a post-training compression method that jointly factorizes grouped-layer KV-Cache into a shared low-rank subspace, substantially reducing KV-Cache memory. Across widely used LLMs, xKV achieves up to 8x KV-Cache compression while preserving accuracy on long-context tasks and in multi-turn settings. To further improve efficiency, we introduce Selective Reconstruction (SR) at decode time. Combined with SR, xKV achieves up to 4.23x end-to-end speedup over the full attention baseline, and surpasses notable baselines with 30% higher throughput under a similar accuracy level. Overall, xKV provides a plug-and-play approach to reduce both memory and latency for long-context LLM inference. Our code is publicly available at: https://github.com/abdelfattah-lab/xKV.
@article{arxiv.2503.18893,
title = {xKV: Cross-Layer KV-Cache Compression via Aligned Singular Vector Extraction},
author = {Chi-Chih Chang and Wei-Cheng Lin and Chien-Yu Lin and Hung-Yueh Chiang and Yash Akhauri and Xilai Dai and Huiqiang Jiang and Yucheng Li and Luis Ceze and Kai-Chiang Wu and Mohamed S. Abdelfattah},
journal= {arXiv preprint arXiv:2503.18893},
year = {2026}
}