Cerebras 详解 MoE 规模化：WSE 单设备训练万亿参数模型
原标题：MoE at Scale: Making Sparse Models Fast on Real Hardware
AI 摘要
Cerebras 发布技术博客，讨论如何在真实硬件上高效扩展 MoE 稀疏模型。文章指出，传统 GPU 实现因需将所有专家加载进显存而受限于内存，业界常用专家并行（EP）来缓解，但会带来负载均衡与模型质量之间的张力，以及随专家数量增加的通信开销。Cerebras 称其 WSE 凭借约 900 倍于单 GPU 的片上 SRAM 和权重流式（weight streaming）技术，无需分布式计算即可在单设备上训练万亿参数 MoE 模型。
正文节选
In this video we discuss scaling MoE models on modern hardware and address key optimization challenges. If you can’t open the video displayed above, please use this link to open it on YouTube: https://youtu.be/MXo9LEYzwkg Mixture-of-Experts (MoE) models allow you to increase total parameter count without proportional increase in compute, letting you train bigger and better models efficiently (Soboleva, 2025a). You might wonder if extracting theoretical benefits from MoE models requires significa