Deploy VCF Private AI Services in Minimal VMware Cloud Foundation Environments

Deployment Workflow Overview Prerequisites Deploy VCF Private AI Services 1. Install Private AI Services on the Supervisor 2. Create a namespace through the vSphere Client 3. Prepare NVIDIA configmap and secret 4. Prepare trust bundles for Private AI Services 5. Prepare Private AI Services configuration YAML file 6. Create a context for the namespace using VCF Consumption CLI 7. Activate Private AI Services on the namespace Use VCF Private AI Services Use Model Store Deploy Model Endpoints Deliver RAG Applications by Using VCF Private AI Services Discover more from VMware Cloud Foundation (VCF) Blog Related Articles Deploy VCF Private AI Services in Minimal VMware Cloud Foundation Environments Using Harbor as a Proxy Cache for Cloud-Based Registries The New Paradigm: MLPerf Inference 5.1 Confirms VCF is the Future of AI/ML Performance VMware Cloud Foundation (VCF) offers a comprehensive suite of software-defined services, enabling enterprises to build reliable and efficient cloud infrastructure with consistent operations across diverse environments. The latest addition to this platform is VCF Private AI Services , a secure set of services for deploying AI applications using models and data sources. VCF Private AI Services integrates with VCF Automation to provide a simplified, cloud-like experience that allows users to deploy models into production easily, often within minutes. For instance, the workflow for deploying model endpoints is available in the VCF Automation UI, as shown below. Without VCF Automation, deploying model endpoints to a namespace would require using VCF Consumption Command Line Interface (VCF Consumption CLI) and kubectl.