VMware Private AI Foundation with NVIDIA is a joint AI platform from Broadcom (VMware) and NVIDIA that helps enterprises run generative AI safely in their own data centers. Built as an advanced service on top of VMware Cloud Foundation (VCF), it brings together: - **VCF Private AI Services** (vector databases, deep learning VMs, data indexing and retrieval, AI Agent Builder, and more) - **NVIDIA AI Enterprise** software - **NVIDIA NIM inference microservices** for the latest NVIDIA Nemotron models and leading community models - **NVIDIA Blueprints** for faster solution design and deployment With this stack, organizations can: - **Run retrieval-augmented generation (RAG) workflows** - **Fine-tune and customize LLMs** on their own data - **Run inference workloads** on-premises The platform is designed to address key enterprise concerns around **privacy, choice, cost, performance, and compliance**, while using the familiar VMware Cloud Foundation environment. NVIDIA AI Enterprise licenses are purchased separately from NVIDIA, and the solution is supported by major server OEMs such as Dell, Lenovo, HPE, Supermicro, Hitachi Vantara, and Fsas Technologies.

The platform is built specifically to let you use generative AI while keeping sensitive data and IP under your control. Key ways it supports privacy and compliance: 1. **On-premises deployment in your data center** Workloads run on VMware Cloud Foundation in your own environment, so enterprise data does not need to leave your controlled infrastructure. 2. **Protection of enterprise data and IP** Large language models are fine-tuned and used against your private data inside your organizational boundary. This helps reduce the risk of data leakage that can occur with open, public AI services. 3. **Access control and workload placement** VCF lets you create different workload domains and place AI workloads where they best fit security, performance, and compliance needs. You can tightly control who accesses which models and data. 4. **VCF Private AI Services for secure data handling** Services such as vector databases, data indexing and retrieval, and AI Agent Builder are designed to support secure data access patterns, helping you manage how data is stored, retrieved, and used by models. 5. **Audit readiness and governance** Because the platform runs on VMware Cloud Foundation, it can be integrated into your existing governance, logging, and audit processes, helping you demonstrate compliance with industry and regional regulations. In short, you get the benefits of generative AI and LLMs while keeping infrastructure, data access, and model usage under enterprise-grade controls.

VMware Private AI Foundation with NVIDIA is designed to help you adopt generative AI in a way that aligns with business, IT, and financial priorities. **1. Support for growing AI demand** Market data shows how quickly AI is becoming mainstream: - Generative AI is expected to drive up to **$4.4 trillion** in annual economic value for enterprises (McKinsey). - By **2028**, **95% of organizations** are expected to integrate generative AI into daily operations, up from **15% in 2025** (Gartner). - **95% of tech companies** are already integrating AI features into new apps (VMware FY24 Q2 Executive Pulse, N=450 executives). This platform is built to help you participate in that shift with a controlled, enterprise-ready approach. **2. Lower total cost of ownership (TCO) for AI infrastructure** Running on VMware Cloud Foundation, the solution aims to deliver: - Public cloud–like scale and agility - Private cloud–level security, resilience, and performance - More efficient use of GPU resources through workload domains and optimized placement This can help reduce the complexity and cost of building and maintaining AI infrastructure from scratch. **3. Simplified deployment and operations** The platform includes: - **Intuitive automation tools** for GenAI deployments - **Deep learning VM images** to speed up environment setup - **Vector database** services for RAG and semantic search - **GPU monitoring** to track utilization and performance These capabilities help IT teams deploy, configure, and reconfigure compute, storage, and networking for GenAI workloads more easily. **4. Performance and scalability for LLMs and RAG** Fine-tuning, customizing, and querying LLMs can be resource-intensive. The combination of VCF and NVIDIA AI Enterprise, plus NVIDIA NIM inference microservices, is designed to: - Improve GPU utilization - Reduce latency for inference - Scale up or out as demand grows **5. Flexibility and choice of models** Enterprises can: - Use NVIDIA Nemotron models - Leverage leading community models - Retain the ability to shift to other LLMs as needs evolve This flexibility helps you align model choice with specific use cases, industry requirements, and future strategy. Overall, VMware Private AI Foundation with NVIDIA helps you reimagine how AI is deployed across departments—boosting productivity while keeping a close eye on privacy, cost, and operational control.