Building AI Infrastructure

AI infrastructure in 2026 extends far beyond GPUs, racks, power, and cooling. Organizations must determine which AI capabilities to consume as services, which workloads can run locally, which should use shared or hosted infrastructure, and when cost, security, data control, performance, or scale justify operating private AI infrastructure.

This two-day course prepares technical leaders and architects to set that direction. Participants examine the workloads driving modern AI infrastructure and how each creates different requirements for compute, memory, storage, networking, identity, security, and operations.

The course covers the full deployment spectrum, from managed model APIs and powerful AI workstations to departmental inference servers, hosted accelerators, private AI platforms, and large-scale GPU infrastructure. Participants compare NVIDIA and AMD accelerator options, CUDA and ROCm software ecosystems, local and server-class inference, and the infrastructure required as workloads move from a workstation to shared enterprise platforms.

Particular attention is given to AI economics, including API and token consumption, hardware utilization, capacity, power, cooling, staffing, and lifecycle costs. Directional knowledge checks help participants evaluate and refine their organizations' AI infrastructure assumptions throughout the course.

By the end of the course, participants will be able to identify what AI infrastructure their organization needs, what it should consume versus own, and where local, cloud, hosted, hybrid, or private infrastructure makes technical and economic sense.