<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>FPGA on Support Tools</title><link>https://support.tools/tags/fpga/</link><description>Recent content in FPGA on Support Tools</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Tue, 17 Jun 2025 09:00:00 -0500</lastBuildDate><atom:link href="https://support.tools/tags/fpga/index.xml" rel="self" type="application/rss+xml"/><item><title>Kubernetes v1.33 Dynamic Resource Allocation: Advanced Hardware Management for AI, ML, and HPC Workloads</title><link>https://support.tools/kubernetes-v1-33-dynamic-resource-allocation/</link><pubDate>Tue, 17 Jun 2025 09:00:00 -0500</pubDate><guid>https://support.tools/kubernetes-v1-33-dynamic-resource-allocation/</guid><description>&lt;h1 id="kubernetes-v133-dynamic-resource-allocation-advanced-hardware-management-for-ai-ml-and-hpc-workloads"&gt;Kubernetes v1.33 Dynamic Resource Allocation: Advanced Hardware Management for AI, ML, and HPC Workloads&lt;/h1&gt;&#10;&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;&#10;&lt;p&gt;Kubernetes v1.33 brings significant enhancements to Dynamic Resource Allocation (DRA), a feature quietly evolving into one of the most crucial capabilities for workloads that depend on specialized hardware. Whether you&amp;rsquo;re running AI/ML training jobs, high-performance computing, or applications needing hardware acceleration, DRA&amp;rsquo;s improvements address critical gaps in how Kubernetes manages specialized resources like GPUs, FPGAs, high-performance NICs, and vendor-specific devices.&lt;/p&gt;</description></item></channel></rss>