arXiv AI By Sasho Nedelkoski, Alexander Acker, Soeren Becker, Dominik Scheinert, Odej Kao

E8 Lattice Key Quantization for Low-Bit KV-Cache Compression

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arXiv Machine Learning
1d ago

OScaR: The Occam's Razor for Extreme KV Cache Quantization in LLMs and Beyond

arXiv:2605.19660v3 Announce Type: replace Abstract: The rapid advancement toward long-context reasoning and multi-modal intelligence has made the memory footprint of the Key-Value (KV) cache a domina...

By Zunhai Su, Rui Yang, Chao Zhang, Yaxiu Liu, Yifan Zhang, Wei Wu, Jing Xiong, Dayou Du, Xialie Zhuang, Yulei Qian, Jianchao Tan, Yuchen Xie, Yik-Chung Wu, Hongxia Yang, Ngai Wong
arXiv Machine Learning
1d ago

PN-Mix: Pair-Norm Guided Mixed-Precision Key Cache Quantization for Efficient Long-Output LLM Inference

PN-Mix is a mixed‑precision key‑cache quantization technique for RoPE‑based decoder‑only large language models that allocates higher precision to salient channel pairs identified during the prefill stage. By focusing on channel pairs with large query–key pair‑norm products, PN‑Mix reduces logit variation caused by quantization errors. Experiments on reasoning, code‑generation, and AIME benchmarks show that PN‑Mix achieves comparable or better accuracy than prior methods while using an average precision of 2.26 bits and degrades gracefully down to 1.5 bits.

By Gil Jae Song, Sungjoo Yoo