AI infrastructure requirements are arriving at the same moment as VMware migration decisions. Organizations that plan these two programs separately will build the wrong platform twice.
Enterprise AI adoption is creating infrastructure requirements that did not exist in meaningful volume when most VMware migration business cases were written. GPU-accelerated compute, high-bandwidth storage I/O, and the operational complexity of managing inference and training workloads alongside traditional enterprise applications are reshaping what a target infrastructure platform needs to deliver.
The Timing Problem
Enterprise AI infrastructure investments are accelerating at a rate most infrastructure organizations had not anticipated. HPE and Futurum's February 2026 research found that AI readiness, not licensing cost, was the primary driver for enterprises reassessing their virtualization strategy. Organizations evaluating Nutanix as a VMware replacement are simultaneously being asked by application development teams whether Nutanix can support GPU workloads.
What AI Workloads Require
GPU-accelerated inference requires PCIe passthrough or virtual GPU configurations that are not standard in most VMware environments. High-throughput model training requires NVMe storage I/O at bandwidths that standard VMware storage configurations may not deliver without architectural changes.
Nutanix AHV supports GPU passthrough and virtual GPU configurations, but these capabilities must be explicitly planned for during infrastructure sizing. Organizations that size their Nutanix cluster deployments for current VMware workload profiles without accounting for upcoming GPU requirements will find themselves constrained when AI workloads arrive.
The Hybrid Cloud AI Question
The economics of on-premises versus cloud AI have shifted significantly. Cloud GPU compute costs have remained high while on-premises GPU hardware prices have declined as manufacturing scale increased. Organizations with consistent, predictable AI workload volumes are finding on-premises GPU infrastructure economically attractive compared to cloud rates for long-running workloads.
Planning for AI Infrastructure in the Migration Program
The practical implication is that AI infrastructure requirements should be surfaced and incorporated before platform sizing decisions are finalized. That means engaging with application development and data science teams to understand what AI workloads are in development or planned for production within the next 12 to 24 months, and incorporating GPU node sizing into Nutanix cluster architecture discussions before initial deployment.
ReadyWorks supports this broader infrastructure view by providing the unified observability platform that can span both traditional workloads and AI infrastructure components.
READY TO ACT?
Build your target platform for the workloads you have and the AI workloads that are coming. Explore VirtualReady and ensure your migration program accounts for the full infrastructure requirement. Learn more about VirtualReady
FREQUENTLY ASKED QUESTIONS
Does Nutanix AHV support GPU workloads?
Yes. Nutanix AHV supports both PCIe passthrough for dedicated GPU access and virtual GPU configurations for shared GPU resources. These capabilities must be explicitly planned for in cluster sizing and architecture.
Should AI inference workloads be hosted on-premises or in the cloud?
The answer depends on workload volume, consistency, and data sovereignty requirements. On-premises GPU infrastructure has become more economically attractive for consistent, high-volume inference workloads as hardware costs have declined.
How does AI infrastructure planning interact with VMware migration timelines?
AI infrastructure requirements affect platform sizing decisions that must be made before Nutanix cluster deployment begins. Organizations that defer AI infrastructure planning until after initial cluster deployment may find themselves constrained when AI workloads arrive.
What operational skills are required to support AI workloads on Nutanix?
Managing GPU workloads requires skills in GPU driver management, virtual GPU configuration, model serving framework deployment, and GPU cluster networking, typically different from traditional infrastructure management skills.
