<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>GPU 訓練 on Peter's Blog</title><link>https://peter-blog.pages.dev/tags/gpu-%E8%A8%93%E7%B7%B4/</link><description>Recent content in GPU 訓練 on Peter's Blog</description><generator>Hugo</generator><language>zh-tw</language><lastBuildDate>Thu, 09 Apr 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://peter-blog.pages.dev/tags/gpu-%E8%A8%93%E7%B7%B4/index.xml" rel="self" type="application/rss+xml"/><item><title>MegaTrain：單 GPU 全精度訓練千億參數大模型的突破性架構</title><link>https://peter-blog.pages.dev/tech/megatrain-single-gpu-precision-training/</link><pubDate>Thu, 09 Apr 2026 00:00:00 +0000</pubDate><guid>https://peter-blog.pages.dev/tech/megatrain-single-gpu-precision-training/</guid><description>研究團隊提出記憶體中心架構 MegaTrain，透過 CPU-GPU 協同計算與管線優化，在單一 H200 GPU 上實現 120B 參數模型的全精度訓練，吞吐量較 DeepSpeed ZeRO-3 提升 84%</description></item></channel></rss>