<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>GPU集群 on AI 早报</title><link>https://ai-news.example.com/tags/gpu%E9%9B%86%E7%BE%A4/</link><description>Recent content in GPU集群 on AI 早报</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Fri, 07 Aug 2026 01:34:02 +0800</lastBuildDate><atom:link href="https://ai-news.example.com/tags/gpu%E9%9B%86%E7%BE%A4/feed.xml" rel="self" type="application/rss+xml"/><item><title>亿级日活App的算力生死劫：跨云架构如何砍掉75% GPU集群？</title><link>https://ai-news.example.com/articles/app75-gpu/</link><pubDate>Fri, 07 Aug 2026 01:34:02 +0800</pubDate><guid>https://ai-news.example.com/articles/app75-gpu/</guid><description>面对推理成本倒挂的严峻挑战，亿级日活应用通过创新的跨云架构成功削减了75%的GPU集群开销，为高并发AI应用的算力优化提供了新范式。</description></item><item><title>亿级日活App的算力生死劫：跨云架构如何砍掉75% GPU集群成本</title><link>https://ai-news.example.com/articles/app75-gpu/</link><pubDate>Tue, 04 Aug 2026 23:14:12 +0800</pubDate><guid>https://ai-news.example.com/articles/app75-gpu/</guid><description>面对推理成本倒挂的生存压力，一家亿级日活App通过创新的跨云架构设计，成功将GPU集群规模缩减75%，大幅降低算力开支，为高并发AI应用的成本控制提供了新范式。</description></item></channel></rss>