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Private AI

On-prem generative AI with NVIDIA on VCF: models and GPUs governed inside the private cloud — the data doesn't leave.

FOCUS · AI IN THE PRIVATE CLOUDVirtualized GPUs, models at home: GenAI with the platform's governance
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01 · What it is

Private AI Foundation, made clear.

Private AI Foundation (with NVIDIA) brings generative AI inside VCF: GPUs virtualized and shared between the teams, NVIDIA microservices (NIM) and the models served on-prem, AI environments delivered self-service by the platform. For those who can't (or won't) send their data into the cloud models, it's the serious way.

On-prem
prompts and documents stay in the perimeter: privacy by architecture
vGPU
the GPUs sliced and shared: the investment exploited, not parked
NVIDIA AI
NIM and enterprise models: the validated stack, not the experiment
Private AI Foundation
OFFICIAL VMWARE BRANDING · PRIVATE AI
CONSOLE REALE · PRIVATE AI SU VCF · FONTE: VMWARE BLOG
REAL CONSOLE · PRIVATE AI ON VCF · SOURCE: VMWARE BLOG
02 · How to use it well

The things that make the difference.

Lo stack

Internal copilots and RAGthe use cases on the sensitive data
Models (NIM)
Vector DB & RAG
Self-service environments
engine · context · consumption
GPUs virtualized & governedquotas, sharing, monitoring
VCF · the private cloudthe platform you already govern
AI as a platform service

The use case on the closed data

Legal documents, industrial recipes, clinical data: where the cloud can't go, you start here.

GPUs with rules

vGPU profiles, quotas and scheduling: the scarce resource without wars between projects.

Governed RAG

The indexes with the corporate permissions: the AI answers only what the user is allowed to know.

Compared TCO

On-prem vs cloud APIs on YOUR volume: the break-even is calculated, not presumed.

03 · In depth

GenAI in the perimeter: GPUs, models and governance

Private AI Foundation (with NVIDIA) runs GenAI on VCF: the vGPUs slice the GPUs (MIG included) with profiles for training and inference, the deep learning VM templates bring the CUDA stack ready, the model store governs the approved models, the vector database (managed pgvector) holds the RAG, NVIDIA NIM serves the optimized LLMs; the data never leaves: the inference next to the ERP, with the usual security and operations.

  • vGPU + MIG — the GPUs shared by profiles: the utilization that justifies the investment
  • DL VM template — CUDA, drivers and frameworks ready: the data scientist starts right away
  • Model store — the models approved and versioned: AI governance
  • NIM — the LLMs served optimized: more tokens from the same GPU
  • RAG nel perimetro — pgvector next to the documents: the context without exfiltrating
  • Stessa operations — the usual backup, DR and monitoring: AI as a normal workload
04 · Numbers and lifecycle

The numbers that matter.

100%
of the data in the perimeter: the DPO breathes
7
MIG instances per GPU: inference consolidated
min
from the template to the ready workbench
GPU
the asset to make pay: the utilization gets designed
Private GenAI is a platform project: GPUs, models and RAG in your data center — AI on the data that can't leave.
05 · Use cases

Where it really pays off.

Intellectual property

The know-how queryable without leaving.

Regulated sectors

GenAI with residency and audit.

High, constant volumes

When the pay-per-use API costs more than the iron.

AI on the data that can't leave: you do it at home — on the platform we already manage.