新的LLM量化方法提升速度和准确性

报道来源 [3]

1. arXiv cs.LG TIER_1 English(EN) · Donghyun Lee, Yuhang Li, Ruokai Yin, Priyadarshini Panda · 2026-07-10 04:00

   KronQ：通过 Kronecker 因子化 Hessian 实现 LLM 量化

   arXiv:2607.07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining. Existing second-order PTQ methods, including GPTQ, construct quantization objectives exclusively from in…

2. arXiv cs.LG TIER_1 English(EN) · Boris van Breugel, Yelysei Bondarenko, Paul Whatmough, Markus Nagel · 2026-07-09 04:00

   FPTQuant：用于大模型量化的函数保持变换

   arXiv:2506.04985v2 Announce Type: replace Abstract: Large language models (LLMs) require substantial compute, and thus energy, at inference time. While quantizing weights and activations is effective at improving efficiency, naive quantization of LLMs can significantly degrade pe…

3. arXiv cs.AI TIER_1 English(EN) · Haoqian Meng, Yilun Luo, Yafei Zhao, Wenyuan Liu, Peng Zhang, Xindian Ma · 2026-07-07 04:00

   ARCQUANT：通过增强残差通道提升LLM的NVFP4量化性能

   arXiv:2601.07475v2 Announce Type: replace-cross Abstract: The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing Post-Training Quantization (PTQ) strategi…