AI is moving quickly from research labs into everyday campus operations, and cloud-only strategies can strain budgets, bandwidth, and security models. Many higher education teams are now looking for a more flexible approach that brings high performance AI compute closer to where research and work actually happen.

This on-demand webinar explores how institutions are shifting from cloud dependency to distributed AI infrastructure that supports both innovation and operational needs. You will hear practical guidance on building an AI ready foundation that scales across departments, enables faster iteration, and helps teams keep control of cost and performance as demand grows.

You will learn how to plan and prioritize distributed AI infrastructure, including:

• How to balance cost, performance, and scalability as AI adoption expands across campus

• Where on-prem and edge AI can reduce bottlenecks for data intensive research and experimentation

• How to support secure collaboration for faculty, staff, and students across AI workflows

• How to design modular, space-efficient systems that can scale from lab deployments to shared campus resources

• How to align infrastructure choices with research, teaching, and administrative use cases

• What deployment patterns and real-world examples can help you move from planning to implementation

• These approaches to Distributed AI Infrastructure for Higher Education highlight how institutions can reduce dependency on cloud while improving performance and scalability.

Get instant access to the on-demand webinar and use it to shape an AI infrastructure roadmap that fits your institution’s goals, constraints, and timeline.