Lesson 1
What is AI compute? Chips, power, and cost explained
The usable capacity stack behind AI: accelerators, memory, networking, power, data centers, and access.
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Compute College track
AI Compute 101
Start here if you are new to AI compute. Learn the basic units, chips, costs, and constraints behind training and running AI models.
7 free lessons, no account required. Who this is for: Founders, analysts, operators, investors, product teams, and curious readers trying to understand the AI compute market.
Lesson order
Recommended lesson order
Work through these lessons in sequence to build a usable understanding of this AI compute topic.
Lesson 2
Why AI compute matters for cost, capacity, and markets
Why chips, power, data centers, and GPU access now shape AI product economics.
Lesson 3
GPU hours meaning: what is a GPU-hour?
The core unit for calculating AI GPU rental cost, utilization, and workload budgets.
Lesson 4
H100 vs H200 vs B200: price, memory, and performance
How NVIDIA accelerator generations compare on workload fit, current GPU-hour pricing, memory, and availability.
Lesson 5
What is Model Training Cost?
Model training cost is the compute expense required to teach or improve an AI model from data.
Lesson 6
What is Frontier Model Serving Cost?
Frontier model serving cost is the estimated expense of running a leading AI model for users after training.
Lesson 7
Why power matters for AI compute capacity
Why electricity, interconnection, and site readiness now constrain GPU deployment.
Market signal
How this track helps you read the AI compute market
This track helps you understand why GPU-hours, accelerator generations, power limits, and capacity constraints show up in AI compute pricing and infrastructure news.
Put it to work
Estimate AI compute costs
Use your own workload assumptions to turn this track into a practical cost estimate.
Try the GPU-Hour Cost Calculator
Open the calculator and adjust inputs for your own workload, quote, or budget scenario.
Keep up with the market
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