研究人员推出FoMoE，一个旨在克服跨地理分布式数据中心训练大型语言模型（LLMs）限制的新颖系统。与先前要求每个站点拥有完整模型副本的方法不同，FoMoE将专家层划分到各个工作节点，显著降低了通信成本和内存开销。这种方法能够更有效地扩展LLMs，实现了经验上的吞吐量加速，并预计为高达1000亿参数的模型带来巨大效益。 AI
影响 能够更高效、可扩展地在分布式、弱连接的数据中心中训练大型语言模型。
排序理由 该集群描述了一篇详细介绍训练LLMs新颖系统的研究论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
arXiv:2606.19025v1 Announce Type: cross Abstract: Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. While increasing model and dataset scale remains the dominant driver of performance, Mixture-of-Exp…
Pre-training Large Language Models (LLMs) typically demands large-scale infrastructure with tightly coupled hardware accelerators. While increasing model and dataset scale remains the dominant driver of performance, Mixture-of-Experts (MoEs) architectures have recently achieved s…