Traditional virtualization design habits do not automatically translate to inference-heavy AI platforms.

That is not because virtualization suddenly stopped being useful. It is because the constraint model changed.

For years, many virtualization clusters were designed around CPU consolidation, memory overcommit, shared storage resilience, and generalized mobility. That model worked well for mixed enterprise workloads where the scheduler could balance demand across a relatively fungible resource pool.

AI inference is different.

Inference-heavy platforms introduce harder placement constraints, larger memory footprints, accelerator dependency, model-serving latency targets, and new lifecycle coupling between host drivers, guest drivers, firmware, runtime images, and model-serving frameworks. Deloitte’s 2026 Tech Trends work frames this shift clearly: as AI moves from proof-of-concept into production, recurring inference workloads can create near-constant demand, and infrastructure decisions become tied to cost, latency, sovereignty, resilience, and IP protection.

Deloitte also predicts that inference will account for roughly two-thirds of AI compute demand by 2026, with a significant amount of that inference running in data centers and enterprise server environments rather than only at the edge.

That matters for vSphere, VCF, VxRail, and vSAN architects.

The AI-ready cluster is not just a virtualization cluster with GPUs installed. It is a policy-driven platform where GPU placement, memory tiering, storage policy, and lifecycle windows have to be designed together.

Current State Versus Target State

A traditional virtualization cluster usually starts with a few familiar questions:

How many hosts do we need?
How much CPU and memory do we need?
What storage policy protects the workload?
How do we keep maintenance non-disruptive?
How much overcommit is acceptable?

Those questions still matter, but they are no longer enough.

For inference-heavy platforms, the design conversation shifts toward a different set of constraints:

What GPU profile does the workload need?
Can the model tolerate GPU sharing, or does it require pass-through?
Where will hot model memory live?
Can memory tiering help the host density target without hurting latency-sensitive endpoints?
Which vSAN policy applies to model files, vector indexes, logs, scratch data, and control-plane services?
When can we patch ESXi, GPU host drivers, guest drivers, firmware, model-serving runtimes, and workload images without breaking compatibility?

The target state is not a generic cluster.

It is an AI-ready cluster contract.

Traditional Cluster Habit	Why It Breaks Down for AI Inference	AI-Ready Design Replacement
Treat hosts as mostly interchangeable	GPU model, GPU memory, PCIe layout, NVLink/NVSwitch topology, and profile availability can make hosts materially different	Define GPU-aware host groups, workload classes, and placement rules
Use DRS primarily for general load balancing	vGPU profile fragmentation can prevent new workloads from powering on even when aggregate capacity looks available	Standardize GPU profiles and decide whether to consolidate or spread vGPU workloads
Assume CPU and memory are the main capacity levers	GPU memory, model size, context length, and concurrency often become the limiting constraints	Size by model footprint, GPU profile, concurrency target, and latency SLO
Use one default storage policy for most workloads	Model artifacts, vector indexes, logs, scratch/cache, and control-plane data have different IO and resilience patterns	Create a storage policy catalog by AI data class
Treat lifecycle as an ESXi patching activity	GPU host drivers, guest drivers, CUDA/runtime images, firmware, and model-serving containers have compatibility relationships	Maintain a version matrix and lifecycle windows for the full AI stack

Architecture at a Glance

The most important thing to notice in the diagram below is that workload admission is not controlled by one resource. The inference service only lands cleanly when placement, memory, storage, lifecycle, observability, and cost controls all line up.

This is why the AI-ready cluster has to be designed as a system. A GPU decision changes placement. A memory tiering decision changes host layout and device allocation. A vSAN policy decision changes failure domains and rebuild behavior. A lifecycle decision changes when workloads can safely move, restart, or be upgraded.

GPU Placement Becomes a First-Class Design Problem

In a general-purpose virtualization cluster, placement can often be abstracted away. You can usually let DRS optimize the environment and intervene only when there is contention, licensing scope, affinity, or maintenance pressure.

With AI inference, placement has to be explicit.

NVIDIA vGPU allows multiple virtual machines to access a physical NVIDIA GPU, while pass-through assigns the entire physical GPU to a single VM. That distinction drives the first major design decision.

Use vGPU when you want controlled sharing, profile-based allocation, better platform flexibility, and higher aggregate utilization across multiple inference services.

Use pass-through when the workload needs exclusive GPU access, has strict vendor support requirements, or cannot tolerate the abstraction layer. But be careful: VMDirectPath I/O introduces operational tradeoffs. Broadcom documents that enabling direct PCI device assignment can make features such as suspend/resume, snapshots, Fault Tolerance, and vMotion unavailable for that VM.

That is not a minor detail. It changes lifecycle design.

A pass-through inference VM may be fast and simple from the application owner’s perspective, but it may be operationally awkward from the platform owner’s perspective. Maintenance windows become harder. Workload mobility changes. Restart planning matters more. Recovery design has to be explicit.

The vGPU Fragmentation Problem

vGPU placement can also create an issue that looks strange to teams used to CPU and memory scheduling.

The cluster may appear to have enough aggregate GPU capacity, but a new VM can still fail to power on because the right profile is not available on the right host. Broadcom documents cases where DRS distributes vGPU VMs broadly and fractional profile allocation can run into profile compatibility and placement issues. vSphere 8 Update 2 introduced an opt-in cluster-level consolidation option for certain vGPU placement scenarios using VgpuVmConsolidation=1.

Broadcom’s engineering guidance also explains that this feature can help place similar-sized vGPU profile VMs together and that DRS can consider vGPU profile and sizing when making recommendations.

The practical design point is simple:

Do not treat GPU capacity as a generic pool.

Treat it as a profile catalog.

GPU Placement Design Principles

For an AI-ready vSAN cluster, GPU placement should be designed around a few practical rules:

1. Standardize profiles where possible.
   Too many vGPU profiles increase fragmentation and make capacity harder to forecast.
2. Separate pass-through and shared GPU patterns when needed.
   Mixing pass-through workloads and shared vGPU workloads in the same cluster can create operational tension.
3. Decide whether the goal is spread or consolidation.
   Spreading can help fault isolation. Consolidation can reduce fragmentation and preserve contiguous capacity for larger profiles.
4. Document the placement contract.
   A workload request should specify GPU mode, GPU profile, GPU count, host constraints, expected concurrency, CPU reservation expectations, memory expectations, and storage policy.
5. Test failure and maintenance scenarios.
   The cluster is not AI-ready until you know what happens when one GPU host is removed from service.

Memory Tiering Changes Capacity Math, Not Latency Physics

Memory tiering is one of the more interesting design levers for AI-adjacent virtualization platforms, but it should not be treated as magic.

VMware’s memory tiering model allows NVMe devices to participate as a memory tier, and VMware has documented that vSAN and memory tiering can coexist. The key caveat is that the resources must remain separate: the NVMe devices used for memory tiering cannot also be used as vSAN capacity devices or as part of the vSAN datastore.

That one sentence has major design implications.

A host intended for both vSAN ESA and memory tiering needs enough local NVMe capacity for both purposes, with clear device separation. You cannot simply take an existing vSAN disk group or ESA device pool and assume it can double as the memory tier.

With VCF 9.1, VMware also introduced software-based NVMe mirroring for memory tiering directly in vSphere, reducing the need for hardware RAID or VROC-style approaches that were previously required in VCF 9.0 designs. VMware also notes that the 9.1 configuration experience can be handled through vCenter with automatic partitioning and maintenance mode rather than a host reboot.

That makes memory tiering more operationally attractive, but it does not change the design rule:

Do not use NVMe memory tiering as an excuse to undersize DRAM for latency-critical inference.

For AI workloads, memory tiering can be useful when:

• The working set has hot and cold memory behavior.
• The workload can tolerate some tiering latency.
• The goal is host consolidation for supporting services around the inference platform.
• You are supporting RAG services, orchestration services, control-plane services, or less latency-sensitive AI components.

Memory tiering is more dangerous when:

• The endpoint has strict p95 or p99 latency targets.
• The model-serving process is already memory pressure sensitive.
• The platform team has not measured active memory behavior under concurrency.
• The business assumes memory tiering is equivalent to buying more DRAM.

AI-ready memory design should start with measured workload behavior, not a consolidation target.

Storage Policy Is Not One Policy for All AI

AI platforms create several different storage personalities.

A model catalog is not the same thing as a vector index. A vector index is not the same thing as prompt/response audit logs. Audit logs are not the same thing as scratch space. Scratch space is not the same thing as the management plane.

A traditional VM storage policy might be good enough for general enterprise workloads, but an AI-ready vSAN cluster needs a policy catalog.

This is especially important with vSAN ESA. VMware documents that ESA uses a performance leg for incoming writes and a capacity leg based on the assigned storage policy, such as RAID-5, to combine performance behavior with the selected availability and efficiency policy. VMware has also positioned ESA as reducing the traditional performance-versus-space-efficiency tradeoff by using a log-structured file system and efficient RAID-5/6 behavior, though that should still be validated against the actual workload and host count.

The practical takeaway is not “always use one policy.”

The practical takeaway is “stop using one policy by accident.”

AI Storage Policy Catalog

Data Class	Example	Design Concern	Possible vSAN Policy Direction
Model artifacts	LLM weights, embedding models, runtime packages	Read performance, version control, availability	Resilient policy with validated read behavior
Vector database / index	RAG indexes, embeddings, metadata	Low latency, write consistency, rebuild behavior	Validate RAID choice under write and query load
Prompt and response logs	Audit trail, compliance logs, observability data	Capacity, retention, compliance, searchability	Capacity-efficient policy if latency allows
Scratch and cache	Temporary pipeline data, rebuildable cache	Performance and rebuildability	Lower resilience only if data is truly disposable
Control plane	Registry, orchestration, platform services	Availability and recovery	Conservative resilience policy
Backup/export	Model snapshots, dataset exports	Capacity and recoverability	Capacity-efficient policy with clear RPO/RTO

vSAN ESA policy behavior also depends on cluster size, host rebuild reserve, and cluster configuration. VMware’s auto-policy management for vSAN ESA can create an optimized cluster-specific default storage policy, but it is disabled by default and does not automatically change existing user-defined policies. VMware’s documented auto-policy logic changes based on host count and host rebuild reserve.

That matters because AI clusters often scale in phases.

A three-host cluster used for early inference testing may not support the same storage policy posture as a larger production cluster. A stretched cluster changes the decision again. A cluster with host rebuild reserve enabled changes the decision again.

Storage policy has to be reviewed whenever the cluster topology changes.

Do Not Over-Tune the Wrong Knobs

One older virtualization habit is to tune stripe width or storage layout settings before measuring the actual bottleneck. For vSAN ESA, VMware’s current performance guidance says to leave Number of Disk Stripes per Object at the default value, and notes that the setting has limited relevance in ESA and is no longer available for Auto-RAID enabled clusters in vSAN for VCF 9.1.

That is a useful reminder.

AI storage design should start with workload classes and performance testing, not inherited tuning habits from older vSAN OSA-era designs.

Lifecycle Windows Are No Longer Only ESXi Patch Windows

The AI-ready cluster has a bigger lifecycle surface area than a traditional virtualization cluster.

A normal virtualization lifecycle plan usually includes ESXi, vCenter, firmware, vendor add-ons, tools, and guest operating system patching. AI adds GPU host drivers, guest GPU drivers, CUDA libraries, inference runtimes, container images, model-serving frameworks, and sometimes NVIDIA AI Enterprise components.

NVIDIA documents that vGPU software release families include specific vGPU Manager and guest driver combinations, and that compatible host and guest driver versions are required or the vGPU fails to load. NVIDIA also distinguishes between Production Branch releases, supported for one year, and Long Term Support Branch releases, supported for three years.

That creates a lifecycle choice.

A lab or fast-moving feature environment may prefer a Production Branch to access newer functionality. A production inference platform may prefer a Long Term Support Branch to reduce change frequency and preserve operational stability.

Neither choice is automatically right.

The wrong choice is failing to choose.

The AI Lifecycle Matrix

At minimum, an AI-ready vSAN cluster should track these lifecycle layers together:

Layer	Why It Matters
Server firmware	GPU, PCIe, NIC, storage controller, and platform stability
ESXi / VCF / vCenter	Host compatibility, lifecycle automation, DRS, vSAN, and management functions
NVIDIA vGPU Manager	Host-side GPU virtualization support
Guest GPU driver	VM-side compatibility with vGPU Manager
CUDA / runtime libraries	Application and framework compatibility
Model-serving runtime	Triton, vLLM, NIM, or other serving stack behavior
Container or VM image	Repeatable deployment and rollback
vSAN disk format / policy	Storage compatibility, policy behavior, and resync windows
Memory tier configuration	Host maintenance mode and device allocation
Monitoring and cost telemetry	Operational validation and inference economics

NVIDIA GPU Manager for VMware vCenter can help operationalize some of this by integrating NVIDIA GPU driver lifecycle workflows into the vSphere Client and using a repository and plug-in model for downloaded drivers.

That does not eliminate the need for governance. It gives the platform team a better control point.

The lifecycle window should still be planned around service impact, rollback, compatibility testing, and model-serving validation.

Phased Strategy for Designing the AI-Ready Cluster

The safest way to build an AI-ready cluster is to avoid pretending it is a one-step hardware refresh.

Treat it as a phased design effort.

Phase 1: Classify the Inference Services

Start with the workload, not the hardware.

Capture:

• Model family and model size
• Expected concurrency
• Context length
• Latency targets
• Throughput targets
• GPU memory requirement
• CPU and DRAM requirement
• Storage data classes
• Data locality requirements
• Audit and retention requirements
• RTO and RPO
• Update frequency for model versions and runtime images

This is where inference economics becomes concrete. Deloitte’s framing is useful because it moves the conversation away from “cloud versus on-prem” as a generic debate and toward workload placement based on cost, latency, sovereignty, resilience, and IP protection. Deloitte also describes a hybrid strategy where cloud provides elasticity, on-premises infrastructure provides consistency, and edge locations provide immediacy.

For vSAN architects, that means the on-prem cluster has to earn its place with predictable service characteristics.

Phase 2: Design the Hardware Pod

Once workloads are classified, define the hardware pod.

For AI-ready vSAN clusters, the pod definition should include:

• GPU model and memory size
• GPU count per host
• vGPU profile catalog
• Pass-through use cases
• CPU socket and NUMA layout
• DRAM target
• Dedicated NVMe devices for memory tiering, if used
• Dedicated vSAN ESA devices
• Network bandwidth and latency assumptions
• vSAN policy support based on host count
• Host rebuild reserve posture
• Expansion unit size

This is also where the design needs to decide whether the cluster is a general AI platform, a dedicated inference pod, a RAG platform pod, or a special-purpose GPU island for one application.

Trying to make one cluster satisfy every AI pattern usually creates friction.

Phase 3: Build the Policy Catalog

The policy catalog should define how workloads are admitted and operated.

At a minimum, define:

• GPU placement policy
• vGPU profile catalog
• Pass-through exception policy
• Memory tier eligibility policy
• vSAN storage policy catalog
• Backup and recovery policy
• Lifecycle branch policy
• Change window policy
• Observability requirements
• Cost allocation tags or reporting boundaries

This is where platform teams can prevent the “everything is special” problem.

The goal is not to slow delivery. The goal is to make the platform repeatable.

Phase 4: Test Under Production-Like Concurrency

A single successful inference test does not prove the cluster design.

Test with concurrency.

Measure:

• Time to first token
• Tokens per second
• p95 and p99 latency
• GPU utilization
• GPU memory utilization
• CPU ready and co-stop behavior
• DRAM pressure
• Memory tier activity
• vSAN read/write latency
• vSAN resync impact
• Network throughput
• Queue depth
• Failed placement attempts
• Host maintenance evacuation behavior

VMware’s Private AI Foundation with NVIDIA reference architecture highlights the importance of GPU monitoring, GPU sharing, self-service deployment patterns, vSAN-backed storage, NSX security controls, and TCO planning using cost-per-token thinking.

That is the right direction. The practical implementation still has to be proven in your environment.

Phase 5: Run Lifecycle Drills Before Production

Before declaring the platform production-ready, run the uncomfortable tests.

Can you place a host in maintenance mode?
Can you update the GPU host driver?
Can you update the guest driver without breaking the model service?
Can you roll back a runtime image?
Can you tolerate one GPU host failure?
Can vSAN resync occur without violating inference latency targets?
Can the cluster continue operating during a policy change?
Can the platform team prove which versions are supported together?

An AI-ready cluster is not validated when the first model responds.

It is validated when the platform survives change.

AI-Ready Cluster Design Checklist

Use this checklist before approving an inference-heavy vSAN or VxRail cluster design.

Workload Classification

• Each inference service has a defined workload class.
• Model size and GPU memory requirements are documented.
• Expected concurrency is documented.
• Latency targets include p95 or p99, not just average response time.
• Throughput targets are documented.
• Data locality, sovereignty, and IP protection requirements are known.
• RTO and RPO are defined.
• Model update frequency is known.
• Runtime image update frequency is known.

GPU Placement

• GPU model, memory size, and count per host are standardized where possible.
• vGPU versus pass-through decision criteria are documented.
• Pass-through workloads have an explicit mobility and lifecycle plan.
• vGPU profile catalog is limited to a manageable set.
• Placement rules account for GPU profile fragmentation.
• DRS behavior is tested with real vGPU profiles.
• Any use of VgpuVmConsolidation=1 is documented and tested.
• Maintenance mode behavior is validated.
• One-host-loss behavior is validated.

CPU, NUMA, and Memory

• CPU reservations are defined where required.
• NUMA behavior is tested for large model-serving VMs.
• DRAM sizing is based on measured active memory, not only allocation.
• Memory tiering eligibility is defined by workload class.
• NVMe devices for memory tiering are dedicated and not used by vSAN.
• Memory tiering is not used to mask undersized DRAM for latency-critical endpoints.
• Memory tier observability is included in the operational dashboard.

vSAN Storage Policy

• vSAN ESA versus OSA assumptions are documented.
• Storage policies are defined by AI data class.
• Model artifacts have a defined policy.
• Vector databases and indexes have a defined policy.
• Prompt, response, and audit logs have a defined policy.
• Scratch and cache behavior is documented as rebuildable or protected.
• Control-plane services use a conservative resilience policy.
• Cluster host count supports the selected RAID and resilience policies.
• Host rebuild reserve impact is considered.
• Policy changes and resync impact are tested under inference load.

Network and Security

• Management, vSAN, vMotion, inference, storage-adjacent, and observability traffic patterns are understood.
• Network bandwidth is validated under model-serving concurrency.
• NSX or equivalent segmentation is defined for model services, data services, and management endpoints.
• External API access, model registry access, and data-source access are controlled.
• Logging and audit paths are defined.
• Load balancing strategy is documented for inference endpoints.

Lifecycle and Compatibility

• ESXi, vCenter, VCF, vendor add-ons, and firmware are tracked together.
• NVIDIA vGPU Manager version is tracked.
• Guest GPU driver version is tracked.
• CUDA/runtime image version is tracked.
• Model-serving framework version is tracked.
• NVIDIA release branch strategy is documented.
• A staging or canary cluster exists for lifecycle validation.
• Rollback procedure is documented.
• Driver compatibility is validated before production rollout.
• Lifecycle windows account for vSAN resync and memory tier maintenance requirements.

Observability and Inference Economics

• GPU utilization is monitored.
• GPU memory utilization is monitored.
• Time to first token is monitored.
• Tokens per second are monitored.
• p95 and p99 latency are monitored.
• Queue depth is monitored.
• vSAN latency and resync activity are monitored.
• Memory tier activity is monitored.
• Cost per token, cost per endpoint, or cost per workload class is tracked.
• Capacity reports distinguish CPU, DRAM, GPU, GPU memory, NVMe tier, and vSAN constraints.

Operational Gotchas

The most common mistake is assuming the AI-ready cluster is a capacity problem.

It is not only a capacity problem.

It is a constraint coordination problem.

Conclusion

The AI-ready cluster is different because the workload contract is different.

Traditional virtualization design still provides a strong foundation: abstraction, policy, lifecycle automation, availability, monitoring, and operational discipline all still matter. But inference-heavy platforms add constraints that cannot be hidden behind generic cluster capacity.

GPU placement becomes a design boundary.
Memory tiering becomes a capacity tool with latency caveats.
Storage policy becomes a workload-specific catalog.
Lifecycle becomes a full-stack compatibility exercise.
Observability becomes tied to inference economics, not just infrastructure health.

That is the practical shift Deloitte is pointing toward with its 2026 infrastructure and inference economics framing. The question is no longer whether AI can run somewhere. The question is whether the platform can run inference predictably, economically, securely, and repeatedly under production change.

For vSAN and VxRail teams, that means the design conversation has to move beyond “add GPUs to the cluster.”

The better question is:

Can this cluster admit, run, protect, measure, and lifecycle inference workloads without turning every deployment into an exception?

That is what makes it AI-ready.

External Sources

Deloitte, Tech Trends 2026: AI Infrastructure and Compute Strategy
https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends/2026/ai-infrastructure-compute-strategy.html

Deloitte, 2026 Technology, Media, and Telecommunications Predictions
https://www.deloitte.com/us/en/insights/industry/technology/technology-media-and-telecom-predictions.html

NVIDIA, Virtual GPU Software User Guide
https://docs.nvidia.com/vgpu/latest/grid-vgpu-user-guide/index.html

Broadcom, vSphere VMDirectPath I/O and Dynamic DirectPath I/O
https://knowledge.broadcom.com/external/article/312208/vsphere-vmdirectpath-io-and-dynamic-dire.html

Broadcom, vGPU VM Placement May Result in Suboptimal Placement
https://knowledge.broadcom.com/external/article/312021/vgpu-vm-placement-may-result-in-suboptim.html

VMware Cloud Foundation Blog, Improving VM Placement to Servers to Optimize Your GPU Usage in vSphere 8.0 Update 2
https://blogs.vmware.com/cloud-foundation/2023/09/01/improving-vm-placement-to-servers-to-optimize-your-gpu-usage-in-vsphere-8-0-update-2/

VMware Cloud Foundation Blog, NVMe Memory Tiering Design and Sizing on VMware Cloud Foundation 9
https://blogs.vmware.com/cloud-foundation/2025/12/16/nvme-memory-tiering-design-and-sizing-on-vmware-cloud-foundation-9-part-4/

VMware Cloud Foundation Blog, Advanced Memory Tiering Enhancements in VMware Cloud Foundation 9.1
https://blogs.vmware.com/cloud-foundation/2026/05/07/advanced-memory-tiering-enhancements-in-vmware-cloud-foundation-9-1/

Broadcom, vSAN ESA Objects Display RAID-1 and RAID-5 Components
https://knowledge.broadcom.com/external/article/434061/vsan-esa-objects-display-raid-1-and-raid.html

VMware Cloud Foundation Blog, RAID-5/6 with the Performance of RAID-1 Using the vSAN Express Storage Architecture
https://blogs.vmware.com/cloud-foundation/2022/09/02/raid-5-6-with-the-performance-of-raid-1-using-the-vsan-express-storage-architecture/

VMware Cloud Foundation Blog, Auto-Policy Management Capabilities with the ESA in vSAN 8 U1
https://blogs.vmware.com/cloud-foundation/2023/03/20/auto-policy-management-capabilities-with-the-esa-in-vsan-8-u1/

VMware Cloud Foundation Blog, Performance Recommendations for vSAN ESA
https://blogs.vmware.com/cloud-foundation/2023/01/01/performance-recommendations-for-vsan-esa/

NVIDIA, Virtual GPU Software Release Notes for VMware vSphere
https://docs.nvidia.com/vgpu/latest/grid-vgpu-release-notes-vmware-vsphere/index.html

NVIDIA, Virtual GPU Documentation
https://docs.nvidia.com/vgpu/index.html

NVIDIA, GPU Manager for VMware vCenter User Guide
https://docs.nvidia.com/vgpu/vmware-vcenter-gpu-manager/latest/user-guide/index.html

VMware Cloud Foundation Blog, VMware Private AI Foundation with NVIDIA on HGX Servers for Inference
https://blogs.vmware.com/cloud-foundation/2025/03/25/vmware-private-ai-foundation-with-nvidia-on-hgx-servers-for-inference/

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