AI workloads are arriving in enterprise data centers faster than infrastructure teams anticipated. For SVPs and VPs of Infrastructure already fielding AI requests from their application teams, this is a capacity planning problem with real consequences: a Nutanix cluster sized purely for VMware migration automation may not be sized for the AI workloads that follow it, and getting this wrong means a hardware refresh cycle, not a configuration change.
VirtualReady's IT migration planning framework solves this by surfacing AI workload requirements from application development teams during pre-migration dependency mapping and wave planning, ensuring the Nutanix target cluster is designed for the complete workload picture from the start. ReadyAI strengthens this further: its context-aware engine tailors insights to the specific page and filters a user is viewing within the VM Accelerator, surfacing VMware estate management gaps and AI workload signals that static dashboards miss, with results scoped to the signed-in user's own permissions.
Gartner's March 2026 research on LLM observability forecasts that by 2028, LLM observability investments will reach 50 percent of GenAI deployments, driven by the need to monitor model accuracy and detect hallucinations at scale. Senior Principal Analyst Pankaj Prasad states: “Without robust explainable AI and observability foundations, GenAI initiatives will be restricted to low-risk, internal, or noncritical tasks.” The infrastructure that supports AI observability at this scale is exactly the infrastructure organizations are building during their VMware migration programs today.
What Inference Workloads Actually Require
Generative AI inference at production scale has consistent infrastructure requirements regardless of model generation or vendor. Every production inference workload requires dedicated GPU compute for model execution, high-bandwidth NVMe storage to load model weights from disk into GPU memory at startup, and high-throughput networking for the data pipelines that feed inference requests. These requirements are materially different from what traditional enterprise VM workloads consume, and they apply whether an organization is running a proprietary hyperscaler model, an open-source foundation model, or an AI capability embedded within an enterprise platform it already uses.
The GPU Specification Requirement
A common planning error is assuming that inference workloads can be served from existing compute infrastructure without modification. CPU-based inference is possible for lightweight use cases but produces latency and throughput characteristics inadequate for production user-facing applications. For Solutions Architects designing the target Nutanix cluster, GPU passthrough configuration must be specified at cluster design time, hardware ordered without GPU support cannot be retrofitted after deployment.
The Storage Bandwidth Requirement
The I/O demand of AI inference comes primarily from model loading: transferring model weights from storage into GPU memory at inference startup. This process is I/O-intensive regardless of the specific model in use, and the time it takes directly determines how quickly inference can begin serving requests. AOS storage with NVMe devices is designed to support the bandwidth requirements of enterprise AI workloads: but NVMe configuration must be specified in the cluster storage design before hardware is ordered, not added after deployment.
The BYOM Reality and Infrastructure Flexibility
Enterprise AI strategy is converging on an important principle that CIOs are increasingly raising at the board level: organizations want to bring their own models rather than be locked into a single vendor’s selection. Whether evaluating open-source foundation models, purpose-built vertical models, or AI capabilities embedded in operational tools they already use, the infrastructure requirement is consistent: a platform capable of running diverse inference workloads with predictable performance. Nutanix AHV’s GPU passthrough capabilities and AOS NVMe storage provide this foundation, giving CIOs the vendor flexibility their boards are demanding, regardless of which models the organization ultimately deploys.
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Frequently Asked Questions
Does Nutanix AHV support GPU workloads?
Yes. Nutanix AHV supports GPU passthrough and virtual GPU configurations, but these must be specified at cluster design time — GPU capability cannot be added after hardware is deployed.
Why do inference workload infrastructure requirements apply regardless of which model an organization uses?
The demands of AI inference are architectural requirements driven by how inference works, not by any specific model’s design. As model generations evolve and organizations gain flexibility to select or switch models, the underlying GPU compute, NVMe storage, and networking requirements remain consistent.
Can CPU-based inference support production AI workloads?
CPU-based inference is possible for lightweight use cases but typically produces latency and throughput characteristics inadequate for production user-facing applications. GPU compute is required for production-grade inference at enterprise scale.
When should AI workload requirements be incorporated into IT migration planning?
Before hardware is ordered for the Nutanix target cluster. Including AI infrastructure requirements in the original cluster design alongside VM migration requirements is materially more cost-effective than addressing them separately.
