<?xml version="1.0" encoding="UTF-8"?>
<rss xmlns:dc="http://purl.org/dc/elements/1.1/" version="2.0">
  <channel>
    <title>InfoQ - Internet Of Things</title>
    <link>https://www.infoq.com</link>
    <description>InfoQ Internet Of Things feed</description>
    <item>
      <title>Article: Efficient Resource Management with Small Language Models (SLMs) in Edge Computing</title>
      <link>https://www.infoq.com/articles/efficient-resource-management-small-language-models/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Internet+Of+Things</link>
      <description>&lt;img src="https://res.infoq.com/articles/efficient-resource-management-small-language-models/en/headerimage/efficient-resource-management-header-1730888361067.jpg"/&gt;&lt;p&gt;Small Language Models (SLMs) bring AI inference to the edge without overwhelming the resource-constrained devices. In this article, author Suruchi Shah dives into how SLMs can be used in edge computing applications for learning and adapting to patterns in real-time, reducing the computational burden and making edge devices smarter.&lt;/p&gt; &lt;i&gt;By Suruchi Shah&lt;/i&gt;</description>
      <category>Infrastructure</category>
      <category>Edge Computing</category>
      <category>Generative AI</category>
      <category>Python</category>
      <category>Internet Of Things</category>
      <category>TensorFlow</category>
      <category>Large language models</category>
      <category>AI, ML &amp; Data Engineering</category>
      <category>article</category>
      <pubDate>Mon, 11 Nov 2024 11:00:00 GMT</pubDate>
      <guid>https://www.infoq.com/articles/efficient-resource-management-small-language-models/?utm_campaign=infoq_content&amp;utm_source=infoq&amp;utm_medium=feed&amp;utm_term=Internet+Of+Things</guid>
      <dc:creator>Suruchi Shah</dc:creator>
      <dc:date>2024-11-11T11:00:00Z</dc:date>
      <dc:identifier>/articles/efficient-resource-management-small-language-models/en</dc:identifier>
    </item>
  </channel>
</rss>
