Private AI Foundation

Enterprise AI on your own infrastructure with NVIDIA GPU virtualization, vector databases and model runtimes, fully integrated into VCF 9.

NVIDIA

AI Enterprise

vGPU

Partitioning

On-prem

Data sovereignty

RAG

Pipeline-ready

Features

Enterprise AI on your own infrastructure

GPU pooling, model runtimes and vector DB, native on VCF 9

NVIDIA GPU Virtualization

vGPU partitioning across multiple VMs or namespaces, shared or dedicated per workload.

GPU vMotion

Live migration of GPU workloads without interruption, including GPU state.

Model Runtime

Deploy LLMs and inference endpoints on-premises with NVIDIA AI Enterprise.

Vector Database

Embedded vector store for RAG pipelines, integrated with VCF data services.

Data Sovereignty

On-prem AI for sensitive or regulated data, no cloud egress of embeddings or prompts.

vSphere Supervisor

Kubernetes-native AI workloads via VKS Supervisor and GPU-aware scheduling.

Introduction

What is Private AI Foundation?

Private AI Foundation is VMware’s on-premises AI platform, native on VCF 9. It combines NVIDIA GPU virtualization, a vector database for RAG and model runtimes in one integrated platform. Ideal for organizations that want to use AI without sending sensitive data to public clouds.

Hardware layer

GPU Architecture

The core of Private AI Foundation is NVIDIA GPU virtualization on vSphere 9. Physical GPUs are pooled and distributed across VMs and Kubernetes namespaces via vGPU partitioning. GPU vMotion enables live migration with GPU state, including training jobs that continue during host maintenance.

vGPU partitioning for shared or dedicated GPU allocation
GPU vMotion for live migration with GPU state
NVIDIA AI Enterprise stack certified on VCF 9
Multi-tenant GPU pooling with per-namespace quotas

Platform

AI Pipeline on VCF

An end-to-end AI pipeline on VCF 9: data sources (on-prem databases, file shares) feed the vector database. A model runtime runs LLMs or custom models. The inference API serves results to applications, all within your own data center, with data sovereignty and NIS2/GDPR compliance.

RAG pipeline: data -> vector DB -> model -> inference
On-prem embeddings, no data egress to public cloud
Integration with existing data systems via VCF Automation
GDPR/NIS2-compliant AI for regulated sectors

Services

Our Private AI Services

We support architecture, GPU sizing, deployment and integration of Private AI Foundation. We also combine Private AI with our AI Agent service for business-specific applications, LLM integrations, chatbots and knowledge assistants on your own infrastructure.

GPU sizing and capacity planning for AI workloads
NVIDIA AI Enterprise deployment on VCF 9
RAG pipelines and vector DB integration
Cross-link: [AI Agent services](/diensten/artificial-intelligence/ai-agent/)

Benefits

Why Private AI Foundation?

Data Sovereignty

AI workloads on your own infrastructure, sensitive data stays inside your data center.

GPU Efficiency

vGPU partitioning and GPU vMotion for maximum GPU utilization across workloads.

Cost Control

Predictable CAPEX/OPEX compared to per-token cloud AI pricing.

Compliance

GDPR, NIS2 and sector-specific compliance without public-cloud data flows.

Use Cases

Private AI Use Cases

On-prem LLMs

Hosting Llama, Mistral and other open models for internal use.

RAG Applications

Knowledge assistants that search on-prem documentation with vector search.

ML Training

GPU-virtualized training jobs with multi-tenant quotas.

Sensitive AI Data

AI for medical, legal or financial data without cloud egress.

Plan a Private AI workshop

We support architecture, GPU sizing and deployment of Private AI on VCF 9. Plan a no-obligation workshop.

Schedule workshop Bekijk klantcases