From Watts to Tokens: Understanding AI Data Center Infrastructure

This free course, From Watts to Tokens, explains what it takes to make an AI data center work by following electricity from the grid to GPUs and heat back outside. It focuses on the power, cooling, and data paths that turn raw megawatts into generated tokens.

■ Fundamentals and Electrical Primer
- Circuit basics, Ohm's law, heat loss, power, energy units, AC vs DC, three-phase power, power factor, transformers, rectifiers, inverters, UPS systems, and redundancy
- Data center naming, power sources, transmission, substations, generators, and rack-level power distribution

■ Compute, Networking, and Cooling
- Rack hierarchy, GPUs, memory, KV cache, model loading, and network speed
- Scale-up and scale-out domains, optical circuit switches, liquid cooling loops, CDUs, and PUE
- Workloads: training vs inference, prefill and decode, batching, interactivity, throughput, latency, and power variability

■ Real-World Application
- Construction, operations, economics, and the Abilene/Stargate site for OpenAI in Texas

It is intended for viewers with basic arithmetic and graph-reading skills who want a structured overview of AI data center infrastructure. The course includes slides, written explanations, and a glossary, and uses case studies to show why adding more GPUs or electrical capacity does not automatically yield more usable compute. Viewers can use it to build vocabulary and a mental model of power, cooling, and networking paths before exploring specialized topics further.