<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
  <channel>
    <title>MLPerf Storage on AI内参</title>
    <link>https://www.neican.ai/tags/mlperf-storage/</link>
    <description>Recent content in MLPerf Storage on AI内参</description>
    <generator>Hugo</generator>
    <language>zh-cn</language>
    <lastBuildDate>Mon, 27 Oct 2025 10:40:04 +0800</lastBuildDate>
    <atom:link href="https://www.neican.ai/tags/mlperf-storage/index.xml" rel="self" type="application/rss+xml" />
    <item>
      <title>超越GPU，掘金AI算力盲区：MLPerf Storage v2.0揭示存储基建的深层博弈</title>
      <link>https://www.neican.ai/insights/gpuaimlperf-storage-v20-20251027104004774-0/</link>
      <pubDate>Mon, 27 Oct 2025 10:40:04 +0800</pubDate>
      <guid>https://www.neican.ai/insights/gpuaimlperf-storage-v20-20251027104004774-0/</guid>
      <description>MLPerf Storage v2.0基准测试揭示，AI训练中存储性能是释放GPU算力、提升训练效率和确保大规模模型稳定性的关键。文章深入分析了以太网与InfiniBand存储方案在不同AI负载下的性能与成本权衡，并预判未来AI存储架构将向异构、软件定义和云原生方向演进，对AI产业的投资逻辑、生态竞争乃至科学发现和社会进程产生深远影响。</description>
    </item>
  </channel>
</rss>
