<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ - GPU</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ GPU feed</description>
    <item>
      <title>NVIDIA Personal AI Router Distributes AI Tasks across Local Compute</title>
      <link>https://www.infoq.com/news/2026/09/nvidia-pair-ai-task-router/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=GPU</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/09/nvidia-pair-ai-task-router/en/headerimage/nvidia-ingest-1789135586036.jpeg"/&gt;&lt;p&gt;NVIDIA Personal AI Router (PAIR), now available in beta, lets you combine the inference capacity of multiple computers on your local network and automatically distribute AI requests among them. It is primarily designed for local multi-agent AI workloads, where multiple independent model calls can otherwise overwhelm one GPU.&lt;/p&gt; &lt;i&gt;By Sergio De Simone&lt;/i&gt;</description>
      <category>Open Source</category>
      <category>Large language models</category>
      <category>GPU</category>
      <category>Agents</category>
      <category>Orchestration</category>
      <category>Performance</category>
      <category>Development</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>Architecture &amp; Design</category>
      <category>news</category>
      <pubDate>Fri, 11 Sep 2026 15:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/news/2026/09/nvidia-pair-ai-task-router/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=GPU</guid>
      <dc:creator>Sergio De Simone</dc:creator>
      <dc:date>2026-09-11T15:00:00Z</dc:date>
      <dc:identifier>/news/2026/09/nvidia-pair-ai-task-router/en</dc:identifier>
    </item>
    <item>
      <title>Presentation: From S3 to GPU in One Copy: Rethinking Data Loading for ML Training</title>
      <link>https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=GPU</link>
      <description>&lt;img src="https://res.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/en/mediumimage/onur-satici-medium-1787813397611.jpeg"/&gt;&lt;p&gt;Onur Satici explains how Vortex, an open-source columnar file format under the Linux Foundation, revolutionizes high-throughput data loading. He details how cascading lightweight encodings, layout-based segment pruning, and zero-copy memory pipelines eliminate CPU/NVMe bottlenecks to stream S3 data straight to GPUs at speeds up to 60 Gbps without requiring upfront data reprocessing.&lt;/p&gt; &lt;i&gt;By Onur Satici&lt;/i&gt;</description>
      <category>Rust</category>
      <category>Data Lake</category>
      <category>GPU</category>
      <category>CUDA</category>
      <category>Transcripts</category>
      <category>QCon London 2026</category>
      <category>Columnar Databases</category>
      <category>S3</category>
      <category>Machine Learning</category>
      <category>Streaming</category>
      <category>Architecture</category>
      <category>Data Pipelines</category>
      <category>Performance</category>
      <category>Development</category>
      <category>Architecture &amp; Design</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>presentation</category>
      <pubDate>Fri, 04 Sep 2026 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/presentations/vortex-columnar-file-format-gpu-streaming/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=GPU</guid>
      <dc:creator>Onur Satici</dc:creator>
      <dc:date>2026-09-04T11:00:00Z</dc:date>
      <dc:identifier>/presentations/vortex-columnar-file-format-gpu-streaming/en</dc:identifier>
    </item>
  </channel>
</rss>
