Run generative AI on your private infrastructure without sending sensitive data to public clouds. VMware Private AI Foundation with NVIDIA brings enterprise AI capabilities to VMware Cloud Foundation, giving organizations full control over data privacy, model selection, and GPU resources.
Best for
Generative AI creates enormous value, but sending proprietary data and intellectual property to public cloud AI services introduces risk. Organizations need AI capabilities that keep sensitive data under their control while still delivering enterprise-grade performance and model flexibility.
Public AI services require sending enterprise data off-premises. For organizations handling financial records, patient data, trade secrets, or classified information, that exposure is unacceptable.
Private AI Foundation keeps all data, models, and inference on infrastructure you control. No data leaves your environment.
GPU hardware is expensive and often underutilized when dedicated to single workloads. Organizations need to maximize GPU investment across multiple AI projects and teams.
DRS for GPUs and vGPU profile visibility enable intelligent GPU allocation across workloads, improving utilization and reducing the total GPU hardware required.
No single AI model is right for every use case. Organizations need the ability to evaluate, deploy, and switch between models without rebuilding their infrastructure.
ModelStore provides a curated catalog of LLMs including NVIDIA Nemotron and community models. Role-based access controls let teams select the right model for each project.
Annual economic value expected from generative AI
Of organizations integrating AI by 2028 (up from 15% in 2025, Gartner)
Of tech companies integrating AI features into new applications
"Our members are our priority, and AI is enabling us to provide personalized, real-time financial solutions."
— US Senate Federal Credit Union
A curated catalog of large language models including NVIDIA Nemotron and community models. IT teams control which models are available to which teams through role-based access controls.
Model Runtime manages model endpoints so developers can consume AI services through standard APIs without managing infrastructure.
DRS for GPUs extends VMware's resource scheduling to GPU workloads, enabling intelligent allocation and rebalancing across hosts. vGPU profile visibility lets administrators see and manage GPU resources alongside compute and storage.
This turns GPU hardware into a shared, optimized resource pool rather than dedicated silos.
Built-in vector databases (pgvector on PostgreSQL) and data indexing services enable retrieval-augmented generation (RAG). Connect AI models to your enterprise data without fine-tuning or retraining.
This allows AI applications to reference current, organization-specific information while keeping that data on-premises.
Deploy and operate AI workloads in fully air-gapped environments. Organizations in defense, government, and regulated industries can run private AI without any external network connectivity.
Models and services are deployed locally with no dependency on cloud-based inference endpoints.
Build agentic AI applications that can reason, plan, and take action using enterprise data. The Agent Builder provides a framework for creating AI agents that integrate with existing business processes.
NVIDIA Blueprints provide pre-built reference architectures to accelerate deployment of common AI use cases.
NVIDIA NIM inference microservices deliver optimized model serving with an API gateway for secure, managed access. Developers interact with AI models through standard APIs without managing GPU infrastructure.
Deep learning VMs provide pre-configured environments for model training and experimentation.
GPU hardware is expensive and limited. When individual teams own dedicated GPU servers, utilization is low and scaling requires new hardware purchases for every project.
Private AI Foundation turns GPU infrastructure into a shared resource pool. DRS for GPUs allocates and rebalances GPU resources across teams based on workload demand. IT provides GPU capacity as a service while maintaining visibility and control.
Public AI coding assistants require sending proprietary source code to external services. For organizations with sensitive codebases, this creates intellectual property risk.
Private AI Foundation enables code generation models to run entirely on-premises. Developers get AI-assisted coding without any source code leaving the organization. Models can be connected to internal repositories and documentation through RAG.
Agentic AI goes beyond simple question-and-answer. AI agents can reason through multi-step tasks, access enterprise systems, and take actions on behalf of users.
The Agent Builder Service and NVIDIA Blueprints provide a framework for creating AI agents that integrate with existing business processes. Contact center agents, IT operations agents, and workflow automation agents can be built and deployed on private infrastructure.
Enterprise knowledge is trapped in documents, emails, and databases. Employees spend hours searching for information that AI could surface in seconds.
Private AI Foundation's data indexing and retrieval services with vector databases (pgvector on PostgreSQL) enable retrieval-augmented generation. AI models reference current enterprise data to answer questions, summarize documents, and generate content without that data leaving your infrastructure.
Understanding the trade-offs between enterprise AI deployment models helps organizations choose the right approach for their requirements.
VMware Private AI Foundation with NVIDIA is a joint platform from Broadcom and NVIDIA that allows organizations to run generative AI workloads on their existing VMware Cloud Foundation infrastructure. It provides model management, GPU optimization, inference services, and data retrieval capabilities while keeping all data and models on private infrastructure.
It was named AI Solution of the Year 2025.
Yes. Private AI Foundation is an advanced service built on VMware Cloud Foundation. It leverages VCF compute, storage, and networking and extends it with AI-specific services including GPU resource management, model runtime, vector databases, and the Agent Builder Service.
Yes. NVIDIA AI Enterprise licenses are purchased separately directly from NVIDIA. VMware Private AI Foundation provides the infrastructure and orchestration layer, while NVIDIA AI Enterprise provides the AI software stack including NIM inference microservices, Nemotron models, and optimized GPU runtime.
Our team can help coordinate the conversation with NVIDIA to ensure your licensing is aligned with your infrastructure deployment.
Private AI Foundation supports GPU-equipped servers from Dell, Lenovo, HPE, Supermicro, and Hitachi Vantara. Organizations can use their existing VMware-certified hardware with supported NVIDIA GPUs.
Contact our team for guidance on hardware sizing and GPU selection based on your AI workload requirements.
Yes. Private AI Foundation includes full air-gap support. Organizations in defense, government, financial services, and other regulated industries can deploy and run AI workloads without any external network connectivity.
Models, inference services, and data retrieval all operate locally with no dependency on external endpoints.
VirtualizationWorks helps organizations evaluate Private AI Foundation with NVIDIA, plan GPU infrastructure, and coordinate VMware and NVIDIA licensing.
Have questions about this product, VMware licensing, or deployment options? Fill out the form below and a VirtualizationWorks specialist will follow up.
