Every organization running AI workloads faces the same infrastructure challenge. GPU resources demand the same operational discipline IT teams already apply to CPU-based compute — snapshots, replication, isolation, lifecycle management, and disaster recovery. Most platforms treat GPU support as an afterthought, bolting it onto existing stacks without native integration.
VergeOS takes a different approach. The platform treats GPU workloads as a first-class infrastructure function, managing pass-through, vGPU, and MIG through the same unified control plane as compute, storage, and networking. NVIDIA introduced VergeOS as a supported vGPU platform, and the result is an infrastructure model where deploying an AI workload follows the same process as deploying any other application.
This campaign explores the operational, architectural, and economic dimensions of GPU virtualization — from the infrastructure gaps that kill AI prototypes to the evaluation criteria that separate platforms built for GPU workloads from those treating GPU support as an add-on.
Resources
Featured
A Beginner’s Guide to GPU Virtualization: Passthrough, vGPU, and MIG
A vendor-neutral explainer covering all three GPU virtualization models — what they are, where CLI complexity stops most teams, use cases by type, and how VergeOS eliminates the operational barrier. Published in The Register.
Read the Article →Datasheet & White Paper
Abstracted GPU Infrastructure
A technical overview of how VergeOS abstracts GPU resources into a unified infrastructure layer, delivering pass-through, vGPU, and MIG through the same control plane as compute, storage, and networking.
View the Datasheet →GPU Virtualization White Paper
A deep-dive analysis of GPU virtualization architectures, comparing pass-through, vGPU, and MIG approaches across operational complexity, resource efficiency, and production readiness.
Download the White Paper →Webinar
GPU Virtualization Without the Complexity
An on-demand webinar demonstrating how VergeOS delivers vGPU, pass-through, and MIG in a unified private cloud environment on existing hardware.
Register for the Webinar →Presentation: GPU Infrastructure Without Complexity
The full scrolling presentation deck from the GPU virtualization webinar, covering architecture, deployment models, and the VergeOS unified management approach.
View the Deck →Blog Posts
GPU Infrastructure Without the Complexity
How VergeOS removes the operational complexity from GPU infrastructure by treating GPU workloads as a native platform function rather than an afterthought. Published on verge.io.
Read the Post →From Zero to RAG in a GPU Virtual Workstation
A step-by-step walkthrough of building a retrieval-augmented generation application inside a VergeOS GPU virtual workstation, from environment setup to working pipeline. Published on verge.io.
Read the Post →GPU Virtualization — Passthrough, vGPU, and MIG
A StorageSwiss analysis of the three GPU virtualization approaches, comparing trade-offs in isolation, density, and operational overhead for production AI workloads.
Read the Post →What to Look For in GPU Support When Evaluating VMware Alternatives
A VMblog byline examining the GPU infrastructure criteria that matter when evaluating VMware alternatives, from provisioning models to lifecycle management.
Read the Post →