The university system chose VMware Private AI Foundation with NVIDIA to strengthen its AI and GenAI capabilities while keeping costs and data risk under control. Key reasons included: 1. **Use of existing VMware datacenter investments** The organization already ran a substantial VMware-based environment and had in-house VMware expertise. Building its GenAI platform on VMware allowed it to reuse existing infrastructure and skills instead of standing up a completely new stack. 2. **Cost optimization and predictability** By fractionally allocating GPU resources and efficiently using compute, storage, networking, and virtualization, the university expects **30–50% lower infrastructure costs** for GenAI. The IT director emphasized that VMware Private AI Foundation with NVIDIA helps them establish **predictable pricing models** for GenAI workloads. 3. **Security and data control** Keeping sensitive data on premises was a priority. The platform lets the university run RAG workflows, fine-tune LLMs, and perform inference in its own datacenters, reducing the risk of data leaving its environment. Built-in security features such as VPN access and two-factor authentication further support data protection. 4. **Scalability and multi-tenancy** The university system supports multiple institutions and health entities. VMware’s multi-tenancy capabilities allow them to provide a **shared GenAI service** where each institution gets a tailored experience on top of a common infrastructure. 5. **Operational efficiency and skills alignment** With strong internal VMware skills, the IT team could deploy and manage the GenAI environment with a **small team of about 3 FTEs**, who are not even fully dedicated to the project. This compares favorably with other organizations that need larger teams for similar AI environments. Overall, VMware Private AI Foundation with NVIDIA gave the university a way to reimagine its AI strategy using infrastructure and skills it already trusted, while improving cost control, security, and scalability.

The university system is already seeing tangible financial, operational, and risk-related benefits from VMware Private AI Foundation with NVIDIA, even though the deployment is relatively recent. Key outcomes include: 1. **Lower infrastructure costs** - Expected **30–50% reduction in infrastructure costs** for GenAI workloads. - Savings come from fractional GPU allocation, efficient use of compute, storage, and networking, and reuse of existing VMware-based datacenters. 2. **Higher IT infrastructure team efficiency** - The IT director expects about **50% efficiency gains** for the infrastructure team compared with running GenAI on an alternative platform. - Only **3 FTEs** are involved in managing the environment, and they are not fully dedicated to it, while also managing **over 1,000 VMs**. 3. **Faster GenAI model development** - The team has already built **four GenAI models**, each trained in just **1–2 days**. - VMware’s tooling lets the team focus on models and use cases instead of building and maintaining container, deployment, and GPU management tools from scratch. 4. **More efficient GenAI use case development** - Estimated **25% staff efficiency improvement** for GenAI use case development. - The platform supports rapid experimentation and deployment of new AI services. 5. **Improved governance and lower risk** - Governance teams are seeing **10–30% efficiency gains** thanks to built-in governance tools that integrate with existing processes. - The IT director estimates about a **70% reduction in GenAI-related risk**, as data stays within the university’s environment and access is tightly controlled. 6. **Operational impact across functions** - Early use cases include: - **Virtual AI-enabled tutors** for different subjects, helping students get explanations and answers on demand. - **Automation of administrative tasks**, such as HR benefits and budgeting analysis, reducing time spent on complex evaluations. - Support for teams like legal, where tasks such as drafting policies now take **minutes instead of hours**. Collectively, these benefits are helping the university system reimagine how it supports students, staff, and researchers, while keeping costs and risk at a manageable level.

VMware Private AI Foundation with NVIDIA gives the university system a structured way to scale GenAI while keeping governance, privacy, and security at the center. Here’s how it supports secure, governed growth: 1. **On-premises data control** - AI and GenAI workloads run in the university’s own datacenters, so sensitive data does not need to leave its environment. - This approach helps the organization manage privacy requirements and reduce exposure to external data breaches. 2. **Built-in governance capabilities** - The platform includes governance tools that let the university **automate model deployment** while enforcing policies and compliance rules. - Governance teams have seen **10–30% efficiency gains**, in part because they can plug GenAI into existing governance structures instead of creating entirely new frameworks. 3. **Security features for access and connectivity** - Capabilities such as **VPN connections** and **two-factor authentication** help ensure that only authorized users can access GenAI resources. - These controls support secure access for multiple institutions and user groups across the university system. 4. **Multi-tenancy for multiple institutions** - VMware’s multi-tenancy allows the IT team to provide a **shared GenAI service** to many universities and health institutions, while still giving each one a tailored environment. - This design supports consistent governance and security policies across the system, without each institution having to build its own separate stack. 5. **Risk reduction for GenAI use cases** - The IT director estimates about a **70% reduction in GenAI-related risk**, thanks to controlled data flows, centralized governance, and standardized tooling. - This lower risk profile helps the university expand GenAI use cases—such as AI tutors and administrative automation—without significantly increasing security or compliance exposure. By combining on-premises deployment, governance tooling, and strong access controls, VMware Private AI Foundation with NVIDIA enables the university system to scale GenAI in a way that is secure, governed, and aligned with its responsibilities to students, staff, and researchers.