PAIF – Private AI Foundation

AI Infrastructure that doesn’t sacrifice data sovereignty

AI infrastructure in your own data center. Data sovereignty, predictable costs and production-ready operations for enterprise AI.

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Run AI in your own infrastructure, not someone else’s

AI workloads in the public cloud are fast to deploy, but they introduce data protection risks, long-term dependencies and unpredictable costs.

Private AI Foundation (PAIF) on VMware Cloud Foundation brings AI infrastructure back under your control. Dedicated workload domains, GPU integration and native AI framework connectivity create a production-ready platform inside your own data center.

evoila designs and implements PAIF as part of your VCF stack, from architecture to productive operation.

Business benefits at a glance:

  • Data sovereignty: AI models and training data remain in your own data center, without public cloud dependency and with full regulatory control
  • Predictable AI costs: There are no variable GPU hourly costs from the public cloud. Instead, there are calculable infrastructure costs based on VCF.
  • Strategic AI autonomy: Your own AI infrastructure creates independence from hyperscaler ecosystems and proprietary AI platforms

AI without infrastructure control does not scale

Data, cost and architecture decisions move outside your organization.

The Challenge

Run AI where your business stays in control

Artificial intelligence is now a core capability. The opportunity is clear, but infrastructure often slows down the next step. Public cloud AI enables fast experiments. Production requires control over data, costs and compliance.
At the same time, building AI infrastructure internally demands expertise across multiple domains.

GPU infrastructure needs more than hardware

Sizing, integration and operation require experience across GPU architecture, workloads and platform design. Without this, systems remain underutilised or unstable.

Data sovereignty must be enforced, not assumed

Public cloud services move training data and models outside your organization. Regulatory control becomes difficult to prove.

AI workloads compete with production systems

Without isolation, AI workloads consume shared resources. Performance becomes unpredictable across both environments.

AI frameworks need infrastructure integration

Disconnected tooling slows development. Engineers need direct access to frameworks like NVIDIA AI Enterprise inside the platform.

Many AI initiatives stop before production

Without a clear infrastructure strategy, pilot workloads do not transition into productive services.

The good News: You do not need to build a separate AI platform

You extend the one you already run.

Our Solution

PAIF as a Production-Ready AI Platform

From AI Concept to Production-Ready Infrastructure

Private AI Foundation extends VCF with AI-specific infrastructure capabilities. evoila integrates PAIF directly into your environment. We define architecture, workload domains, GPU setup and operations upfront. This prevents AI from remaining an isolated experiment. What we deliver:

AI workload domain design & implementation

Design and deployment of dedicated AI workload domains within VCF. Isolated from production workload domains, with dedicated compute, storage and network resources for AI workloads and GPU hosts.

GPU infrastructure integration

Planning and integration of GPU-capable hosts into the VCF environment, including GPU passthrough and vGPU configuration, to support a range of AI workloads, from inference to distributed model training.

AI framework integration

Integration of well-established AI frameworks and platforms into the PAIF infrastructure. This includes NVIDIA AI Enterprise and CUDA-based workload environments, as well as container-based ML pipelines via VKS.

AI-optimized storage design

We configure vSAN Storage Classes for AI-specific requirements. This enables high sequential throughput for training data, low latency for inference workloads and scalable capacity for large model artifacts.

Network design for distributed training

Design of high-speed networks for GPU-to-GPU communication during distributed model training with NSX-based isolation and dedicated network paths for AI traffic.

Operational model & lifecycle management

We define an operational model for AI infrastructure, including the management of GPU resources, scheduling of workloads, planning of capacity, and integration into the VCF monitoring framework.

Tech-Deep-Dive

The architecture behind PAIF

PAIF as a native VCF-AddOn

The Private AI Foundation is not a separate product. Rather, it is a structured add-on framework that extends VCF with AI-specific infrastructure concepts. At its core is the concept of the AI Workload Domain: a dedicated VCF domain comprising GPU-capable hosts, AI-optimized storage classes and isolated network paths. It is fully managed via the VCF management framework and integrated into existing lifecycle management.

GPU integration: passthrough and vGPU

PAIF supports two GPU virtualization models, which Evoila deploys depending on workload requirements: The GPU passthrough model dedicates a physical GPU entirely to a VM to maximize performance during computationally intensive model training, while the vGPU model partitions GPU resources across multiple VMs to enable efficient utilization in inference workloads and development environments. Evoila provisions GPU hosts, configures driver stacks and integrates the NVIDIA AI Enterprise software platform to support production-ready AI workloads.

AI frameworks via Kubernetes

Container-based AI workloads are run via VMware Kubernetes Services on the PAIF infrastructure. Machine learning (ML) pipelines, training jobs and inference services are deployed as Kubernetes workloads. GPU resource requests are made via the NVIDIA Device Plugin. Harbor is used as a private model and image registry, and Velero is used for checkpoint backup during long training jobs.

Storage architecture for AI workloads

AI workloads have specific storage requirements. Training datasets require high sequential read throughput, checkpoint storage requires low latency and model artifacts require scalable capacity. evoila configures differentiated vSAN Storage Classes to meet these requirements. This includes ESA architecture for performance-critical paths and cost-optimized classes for bulk data storage.

Network isolation and GPU interconnect

AI workload domains are isolated from the network via NSX. AI workloads do not have uncontrolled access to production systems. For distributed model training, evoila configures dedicated, high-speed network paths for GPU interconnect traffic, which are separate from the regular VM data network. This reduces network overhead for distributed training jobs and prevents production workloads from experiencing resource contention.

Technical Advantages

Operate AI without infrastructure trade-offs

Dedicated AI workload domains

Complete isolation of AI and production workloads at the infrastructure level without resource contention

Flexible GPU virtualization

GPU passthrough for maximum training performance, vGPUs for efficient inference and development environments

AI-optimized storage profiles

Differentiated vSAN profiles for training data, checkpoints and model artifacts with ESA performance

Container-native ML pipelines

AI workloads as Kubernetes jobs via VKS with GPU resource requests and Harbor as a model registry

NSX workload isolation

Dedicated network paths for GPU interconnect traffic, isolated from production network segments

Unified lifecycle management

The PAIF infrastructure is fully managed via VCF management, so there is no need for separate AI infrastructure management.

Your partner of choice

You need one partner for the full stack

Running AI infrastructure on your own hardware requires experience across VCF, GPU systems and AI workloads.
That combination is rare. evoila brings platform expertise, GPU integration and operational experience together. PAIF is implemented as part of your environment, not beside it. We take you from infrastructure design to your first productive AI workload.

We build on VMware Cloud Foundation

Running AI infrastructure on your own hardware requires a combination of VCF platform expertise, GPU infrastructure know-how and an understanding of AI workload characteristics. That combination is rare. evoila brings all three of these elements together.

We design GPU infrastructure for real workloads

At evoila, PAIF is implemented as part of the VCF stack, not as a separate platform. With NSX network design, vSAN storage architecture, VKS integration and an operational model that ensures long-term manageability.

We bring AI workloads into production

For companies moving AI from public cloud pilots to production-ready on-premises infrastructure, evoila acts as a structured partner. From infrastructure strategy to the first productive AI workload.

Partnerships

Technologies & Partner

Built on proven enterprise platforms and ecosystem partners.

PAIF
Broadcom

GPU
NVIDIA

Talk to our AI infrastructure team

We help you design and implement AI infrastructure that works in production.

Christopher Mack

Christopher Mack

Cloud Engineer Associate

FAQs

Commonly asked questions about PAIF

Private AI Foundation is a VCF add-on that builds AI infrastructure inside your data center. Data and models stay under your control, while costs and compliance remain predictable.

PAIF supports NVIDIA GPUs as the primary accelerator. Qualification is via the VMware-NVIDIA compatibility matrix. As part of the PAIF Readiness Assessment, evoila handles hardware qualification, sizing and driver stack configuration, and recommends either GPU passthrough or vGPU based on specific workload requirements.

Yes. PAIF is integrated into existing VCF environments as an add-on by adding GPU-capable hosts and setting up dedicated AI Workload Domains. During the assessment, evoila analyzes the existing environment and develops an expansion plan without impacting production workloads.

AI Workload Domains are dedicated VCF domains with their own compute, storage, and network resources. NSX ensures network-level isolation. AI workloads do not have uncontrolled access to production systems. Resource contention is structurally prevented, not just through scheduling policies.

Container-based ML frameworks run via VKS on the PAIF infrastructure—using GPU resource requests via the NVIDIA Device Plugin, Harbor as a private model registry, and Velero for checkpoint backup. NVIDIA AI Enterprise provides optimized framework versions for VMware environments.