English(EN) Characterizing High Bandwidth Flash for LLM Serving

研究发现：超高带宽闪存可提升大语言模型服务性能

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-02 04:00

一项新的研究论文探讨了使用超高带宽闪存（HBF）来增强高带宽内存（HBM）以用于大语言模型（LLM）服务。该研究引入了一个分层存储系统和一个缓冲感知调度方法，以管理HBF的访问成本和有限的写入寿命。模拟显示，HBF增强的系统可以将完成时间最多缩短87%，节省能源，并延长HBF的估计写入寿命。 AI

影响这项研究可能带来更高效、更具成本效益的大语言模型服务基础设施，从而降低AI应用的延迟和能耗。

排序理由在arXiv上发表的研究论文，详细介绍了改进大语言模型服务基础设施的技术方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

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arXiv cs.LG TIER_1 English(EN) · Zack Yu, Chloe Wong, Coleman Hooper, Minjae Lee, Wonjun Kang, Youngjin Cho, Michael W. Mahoney, Yakun Sophia Shao, Kurt Keutzer, Amir Gholami · 2026-10-02 04:00

LLM服务的高带宽闪存特性分析

arXiv:2609.39131v1 Announce Type: new Abstract: Large language model (LLM) serving requires substantial memory to store model weights and KV caches. As models grow larger and contexts become longer, memory capacity and bandwidth increasingly become bottlenecks for serving perform…