GPU virtualization and private AI infrastructure deserve a place on your evaluation checklist — before you sign.
IT teams evaluating VMware alternatives are doing rigorous comparisons: licensing costs, migration complexity, feature parity, support quality. Most checklists cover compute, storage, networking, and HA. Almost none put GPU virtualization on the list — until someone asks what happens to the VMs running GPU-accelerated workloads, or raises the question of where private AI infrastructure fits in the new platform. By then, the platform decision is already made.
Adding GPU support to your evaluation criteria before you commit costs nothing. Retrofitting it after the fact can cost you the platform decision itself.
“Supports NVIDIA GPUs” Is a Low Bar
Nearly every VMware alternative will tell you it supports NVIDIA GPUs. That statement is almost always true and almost always incomplete. The question is not whether the platform supports NVIDIA hardware — the question is which GPU virtualization modes it supports and how well.
GPU virtualization takes three forms: passthrough, which dedicates an entire physical GPU to a single VM; NVIDIA vGPU, which allows multiple VMs to share one GPU through NVIDIA’s virtualization software; and Multi-Instance GPU (MIG), which partitions a GPU into hardware-isolated instances enforced in silicon. Each mode fits different workloads. Passthrough suits high-intensity single-workload jobs. vGPU suits VDI and shared development environments. MIG suits multi-tenant scenarios where workload isolation is a hard requirement — including private AI inference.
Most alternatives support one or two of these modes. Very few support all three natively in a unified management interface. If a platform only supports passthrough, you lose density. If it lacks MIG, you lose the capability that makes private AI infrastructure practical for most organizations. Ask every vendor specifically: which modes, and through which interface?
The NVIDIA Certification Question
There is a meaningful difference between a platform that works with NVIDIA GPUs and a platform that NVIDIA has introduced as a validated, jointly-supported vGPU platform. The difference is accountability.
When GPU issues arise in production — and they do — you need a clear escalation path. On a non-validated platform, you are managing two separate vendor support threads with no shared accountability. Each vendor points to the other. On a validated platform, both vendors stand behind the deployment and collaborate on resolution.
Certification also tells you something about depth of testing. NVIDIA does not validate platforms lightly. A listing on NVIDIA’s supported platform list means the configuration has been tested against NVIDIA’s enterprise requirements, not just verified to boot.
When evaluating alternatives, ask: Is this platform on NVIDIA’s validated vGPU platform list? Which NVIDIA vGPU software products are supported — RTX Virtual Workstation (vWS), Virtual PC (vPC), Virtual Applications (vApps)? Which GPU hardware has been validated? The answers narrow the field quickly.
Driver Management: The Operational Question Most Evaluations Miss
Feature support is the easy part of the GPU evaluation. The harder question is operational: how does this platform handle driver management across your GPU fleet?
The fragmented model that plagued GPU deployments on vSphere involved managing driver versions separately across three layers: the hypervisor, the NVIDIA vGPU software stack, and each guest OS. Version mismatches across those layers were the leading cause of GPU support tickets. It was not a VMware problem specifically — it is what GPU driver management looks like when a platform treats GPUs as an add-on rather than a first-class resource.
When evaluating alternatives, ask each vendor: how many manual steps does it take to deploy a driver update across all GPU-enabled VMs? A platform with a unified driver deployment workflow — ideally one where you upload a driver once and the platform handles distribution automatically — is meaningfully different from one that requires per-VM CLI steps. This is the operational question that separates platforms with GPU support from platforms built for GPU operations.
MIG Accessibility: The Private AI Question
MIG is the capability that makes private AI practical for most organizations. It lets you split an expensive data center GPU across multiple AI workloads with hardware-enforced isolation — no noisy neighbor problems, no contention, guaranteed resources per workload. For regulated industries, the hardware-level isolation is often a compliance requirement, not just a preference.
Historically, MIG required CLI expertise that stopped most IT teams before they started. Partition sizes, memory allocation, profile selection — all managed through nvidia-smi, all requiring GPU specialist knowledge. The result was that MIG stayed on the roadmap indefinitely for most organizations.
The right question for your evaluation: can your sysadmins configure MIG without touching a command line? A platform where MIG is a point-and-click operation in the same interface your team already uses for compute and storage turns a specialist capability into a day-one operation.
An Example of What This Looks Like in Practice
One platform that satisfies all four of these criteria is VergeOS. NVIDIA introduced VergeOS as a validated vGPU platform with joint support across RTX Virtual Workstation (vWS), Virtual PC (vPC), and Virtual Applications (vApps). Validated hardware includes the A100, A30, A40, and L40 series, plus the RTX PRO 6000 Blackwell Server Edition for MIG vGPU. All three GPU modes are managed through the same interface used for compute, storage, and networking. Driver deployment is a single upload — the platform builds the ISO and auto-deploys to every GPU-enabled VM. MIG configuration is point-and-click.
That is the benchmark. Use it as a reference point when evaluating where other alternatives land on each criterion.
If GPU workloads or private AI infrastructure are on your roadmap — now or in the next 18 months — the platform you choose for your VMware migration is also the platform you will use to run them. Make GPU virtualization a first-class criterion before the decision is made, not an integration project after the contract is signed.
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Watch the GPU Virtualization Without the Complexity on-demand webinar to see all three GPU modes — passthrough, vGPU, and MIG — demonstrated live in the VergeOS interface. Download the GPU Virtualization Without the Complexity white paper for a full technical breakdown of GPU modes, driver management, and deployment scenarios.
