新的GateRQ方法提高了LLM的内存效率和速度

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-28 04:00

研究人员开发了一种名为GateRQ的新方法，以提高大型语言模型中量化条件内存的效率。该技术解决了低比特量化不成比例地影响长尾事实的问题。GateRQ通过将行存储为一系列1比特残差码，并且仅为高排名查找访问更深的内存级别来优化内存使用，从而显著降低了GPU内存需求并提高了吞吐量。 AI

影响这项研究通过减小内存占用和提高吞吐量，有望实现更高效、更快速的LLM推理。

排序理由该集群包含一篇详细介绍LLM内存优化新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Tianqi Chen, Xunchao Li, Linchuan Xie, Guanghua Yu, Jianchen Zhu, Peisong Wang, Jian Cheng · 2026-10-28 04:00

让门决定：LLM中量化条件内存的每查找精度

arXiv:2610.35985v1 Announce Type: cross Abstract: Conditional memory layers, such as DeepSeek's Engram, attach large hashed n-gram tables to a language model, and removing them from DeepSeek-V4.1-Flash costs 36.8 PopQA points. Low-bit quantization loses long-tail facts first, and…