<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Nvidia on Netdata</title><link>https://www.netdata.cloud/tags/nvidia/</link><description>Recent content in Nvidia on Netdata</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 04 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://www.netdata.cloud/tags/nvidia/index.xml" rel="self" type="application/rss+xml"/><item><title>NVIDIA DCGM Collector: Deep GPU Monitoring For AI</title><link>https://www.netdata.cloud/blog/nvidia-dcgm-monitoring/</link><pubDate>Mon, 04 May 2026 00:00:00 +0000</pubDate><guid>https://www.netdata.cloud/blog/nvidia-dcgm-monitoring/</guid><description>&lt;p>&lt;img src="../images/dcgm-collector.svg" alt="NVIDIA DCGM Collector: Deep GPU Monitoring for Data Center and AI Infrastructure">&lt;/p>
&lt;p>GPU infrastructure is expensive and increasingly central to production workloads. Whether you&amp;rsquo;re running ML training jobs, inference serving, video transcoding, or HPC workloads, understanding what your GPUs are actually doing, and what&amp;rsquo;s going wrong when performance degrades, is not optional. The problem is that NVIDIA&amp;rsquo;s Data Center GPU Manager (DCGM) exposes an enormous amount of telemetry, but getting that data into a monitoring system in a useful, organized way has traditionally required significant setup and custom dashboarding work.&lt;/p></description></item></channel></rss>