VMware Private AI Foundation with NVIDIA is a way to reimagine how you set up and manage AI infrastructure on VMware Cloud Foundation. It focuses on making it easier to run AI and machine learning (ML) workloads in a private cloud environment.

Using VMware Cloud Foundation’s Private AI Automation Services, your teams can:

• Automate the provisioning of GPU-enabled machines for ML workloads.
• Use dedicated catalog items specifically designed for GPU-enabled AI workloads.
• Accelerate AI model development without having to retrain models from scratch.

In practice, this means you can standardize and streamline how AI infrastructure is delivered, while keeping it within your own private cloud for control, governance, and security.

Private AI Automation Services in VMware Cloud Foundation are designed to simplify and automate the lifecycle of GPU-based infrastructure for AI and ML.

Key capabilities include:

• Automated provisioning of GPU machines: Instead of manually configuring GPU hosts for each project, you can automatically spin up GPU-enabled machines tailored to ML workloads.
• Catalog-driven deployment: Teams can select from dedicated catalog items for GPU-enabled AI workloads, so they get consistent, pre-approved configurations every time.
• Faster iteration on Kubernetes changes: The platform helps organizations iterate more quickly on AI-enabled Kubernetes infrastructure, reducing the time it takes to roll out or adjust AI environments.

This automation helps IT and data science teams reduce manual work, cut configuration errors, and deliver AI-ready infrastructure on demand.

The solution is built to accelerate AI model development by removing common infrastructure bottlenecks.

It supports faster AI work in several ways:

• On-demand GPU resources: Automated provisioning of GPU-enabled machines means data scientists spend less time waiting for hardware and more time training and testing models.
• Predefined AI workload templates: Dedicated catalog items for GPU-enabled AI workloads give teams a quick starting point with known-good configurations.
• No need to constantly retrain from scratch: The platform is designed to help you accelerate AI model development without retraining models every time you adjust infrastructure, which can save both time and compute resources.
• Faster iteration on Kubernetes-based AI stacks: Because it supports rapid changes to AI-enabled Kubernetes infrastructure, teams can experiment and refine their environments more frequently.

Together, these capabilities help organizations move from AI ideas to running workloads more quickly, while keeping infrastructure consistent and manageable.