<?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 Support Tools</title><link>https://support.tools/tags/nvidia/</link><description>Recent content in NVIDIA on Support Tools</description><generator>Hugo</generator><language>en-US</language><lastBuildDate>Sat, 25 Jul 2026 00:00:00 -0500</lastBuildDate><atom:link href="https://support.tools/tags/nvidia/index.xml" rel="self" type="application/rss+xml"/><item><title>GPU Scheduling with NVIDIA Operator: Enterprise Kubernetes Implementation Guide</title><link>https://support.tools/gpu-scheduling-nvidia-operator-kubernetes-enterprise-guide/</link><pubDate>Sat, 25 Jul 2026 00:00:00 -0500</pubDate><guid>https://support.tools/gpu-scheduling-nvidia-operator-kubernetes-enterprise-guide/</guid><description>&lt;p&gt;Master GPU scheduling in Kubernetes with NVIDIA GPU Operator for high-performance AI/ML workloads. This comprehensive guide covers GPU resource management, Multi-Instance GPU (MIG), time-slicing, scheduling strategies, and production optimization for enterprise environments.&lt;/p&gt;</description></item><item><title>GPU Cluster Management for AI Workloads with NVIDIA Operator: Complete Production Guide</title><link>https://support.tools/gpu-cluster-management-ai-workloads-nvidia-operator/</link><pubDate>Thu, 23 Jul 2026 00:00:00 -0500</pubDate><guid>https://support.tools/gpu-cluster-management-ai-workloads-nvidia-operator/</guid><description>&lt;p&gt;Managing GPU clusters for AI workloads requires sophisticated orchestration, resource sharing, and monitoring capabilities. The NVIDIA GPU Operator provides a comprehensive solution for deploying and managing GPU-enabled Kubernetes clusters with enterprise-grade features. This comprehensive guide covers setting up production-ready GPU infrastructure with advanced multi-tenancy, monitoring, and optimization strategies.&lt;/p&gt;</description></item><item><title>High-Performance Computing and GPU Programming on Linux: CUDA, OpenCL, and Parallel Computing Mastery</title><link>https://support.tools/high-performance-computing-gpu-programming-linux/</link><pubDate>Sun, 13 Apr 2025 10:00:00 -0500</pubDate><guid>https://support.tools/high-performance-computing-gpu-programming-linux/</guid><description>&lt;p&gt;High-performance computing (HPC) on Linux platforms requires sophisticated understanding of parallel programming paradigms, GPU architectures, and distributed computing frameworks. This comprehensive guide explores advanced HPC techniques, from CUDA and OpenCL programming to building scalable GPU clusters and optimizing computational workloads.&lt;/p&gt;</description></item><item><title>Setting up Nvidia drivers for k3s</title><link>https://support.tools/post/nvidia-gpus-on-k3s/</link><pubDate>Tue, 05 Jul 2022 03:24:00 -0500</pubDate><guid>https://support.tools/post/nvidia-gpus-on-k3s/</guid><description>&lt;p&gt;Rather than telling you how great Kubernetes is, this article aims to show you how to enhance it for workloads requiring GPUs so you can get the most out of it. A few steps need to be followed if you wish to run Kubernetes Pods on K3S that use NVIDIA GPUs for CUDA workloads, and this process is straightforward. Despite this, it has come to my attention that there doesn&amp;rsquo;t appear to be any set of instructions clearly showing the steps to follow, which components are needed, how to enable them, etc. As a result, I have decided to write this short article that outlines the necessary steps to be taken.&lt;/p&gt;</description></item></channel></rss>