Week 1: Energy & Data Center Infrastructure

The physical foundation of AI – power, cooling, and data center design

Time Estimate: 3-4 hours

Topics Covered

Power Usage Effectiveness (PUE) and data center efficiency metrics
Liquid cooling vs. air cooling for high-density compute
Energy requirements for training large models
Rack-scale architectures for AI workloads
Sustainable AI infrastructure design

Featured Speaker

AV

Amin Vahdat

Google Fellow, Chief Technologist for AI Infrastructure

Learn from industry leaders who are building the future of AI infrastructure and applications.

Video Resources

📹 Video content will be added here by Agent 2

Videos include keynotes, technical talks, and tutorials from industry leaders.

Reading Materials

📚 Reading list will be added here by Agent 3

Research papers, blog posts, and technical documentation.

🛠️ Hands-On Lab

Infrastructure Mapping & Power Estimation

Beginner 2 hours

Objective

Understand the full AI infrastructure stack from power generation to model deployment. Learn to calculate datacenter power and cooling requirements.

Prerequisites

Basic understanding of computing systems
Spreadsheet software (Google Sheets or Excel)
No programming required

Setup Instructions

Open Google Sheets or Excel
Review lecture notes on datacenter power density
Have a calculator ready

Tasks

Draw a diagram of the full AI stack (power → network → GPU → model → application)
Calculate power consumption for 1,000 H100 GPUs (700W TDP each)
Estimate cooling requirements (assume 1.2 PUE)
Research real datacenter specs and compare your estimates
Calculate monthly electricity cost at $0.10/kWh

Resources