<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:content="http://purl.org/rss/1.0/modules/content/"><channel><title>VKS on Virtualization Gurus</title><link>http://mohamedamrrabiee.github.io/virtualizationgurus/tags/vks/</link><description>Recent content in VKS on Virtualization Gurus</description><generator>Hugo</generator><language>en-us</language><copyright>2026 All rights reserved.</copyright><lastBuildDate>Sun, 11 Oct 2026 00:00:00 +0000</lastBuildDate><atom:link href="http://mohamedamrrabiee.github.io/virtualizationgurus/tags/vks/index.xml" rel="self" type="application/rss+xml"/><item><title>Private AI Workload Domain: GPU Nodes, AI Kubernetes, and Private AI Services</title><link>http://mohamedamrrabiee.github.io/virtualizationgurus/posts/vcf9-private-ai-workload-domain/</link><pubDate>Sun, 11 Oct 2026 00:00:00 +0000</pubDate><guid>http://mohamedamrrabiee.github.io/virtualizationgurus/posts/vcf9-private-ai-workload-domain/</guid><description>Running AI on your own infrastructure sounds like one project. With VMware Private AI Foundation with NVIDIA it is really three different ways to use your GPUs, plus a licensing model that spans two vendors, and the choices you make early shape everything you build and buy.</description></item></channel></rss>