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      <title>Legare Kerrison and Cedric Clyburn on LLM Performance and Evaluations</title>
      <link>https://www.infoq.com/news/2026/04/kerrison-clyburn-llm-performance/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Hugging+Face-news</link>
      <description>&lt;img src="https://res.infoq.com/news/2026/04/kerrison-clyburn-llm-performance/en/headerimage/kerrison-clyburn-llm-performance-header--1777288853060.jpg"/&gt;&lt;p&gt;Effectively measuring the performance of applications that are leveraging Large Language Models (LLM) is critical to the adoption of AI technologies in organizations. Legare Kerrison and Cedric Clyburn from RedHat team recently spoke at Arc of AI 2026 Conference about practical methods to evaluate and optimize LLM inference.&lt;/p&gt; &lt;i&gt;By Srini Penchikala&lt;/i&gt;</description>
      <category>Benchmark</category>
      <category>GPU</category>
      <category>Large language models</category>
      <category>Cost Optimization</category>
      <category>Hugging Face</category>
      <category>Performance</category>
      <category>Performance Evaluation</category>
      <category>Machine Learning</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>news</category>
      <pubDate>Tue, 28 Apr 2026 09:05:00 GMT</pubDate>
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      <dc:creator>Srini Penchikala</dc:creator>
      <dc:date>2026-04-28T09:05:00Z</dc:date>
      <dc:identifier>/news/2026/04/kerrison-clyburn-llm-performance/en</dc:identifier>
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