Lesson 1
What is Model Training Cost?
Model training cost is the compute expense required to teach or improve an AI model from data.
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Model Costs
Learn what it costs to train and serve AI models, why utilization matters, and how recurring inference demand becomes compute spend.
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
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 3
What is Cost per Million Tokens?
Cost per million tokens is how hosted AI APIs price inference — usually with input and output tokens priced separately.
Lesson 4
What is GPU Utilization?
GPU utilization measures how much paid accelerator capacity is actively doing useful work.
Lesson 5
What is Model FLOPs Utilization (MFU)?
Model FLOPs Utilization (MFU) measures how much of a GPU's theoretical compute a job actually uses; goodput counts useful work delivered.
Lesson 6
How Fast Does an H100 Depreciate?
GPU depreciation spreads an accelerator's purchase cost over its useful life, which drives the real cost of every GPU-hour.
Lesson 7
How to Estimate Monthly AI Compute Burn
Monthly AI compute burn measures recurring spending on the capacity used to train, fine-tune, experiment, and serve models.
Market signal
How this track helps you read the AI compute market
This track helps you connect training runs, inference demand, utilization, and provider quotes to the recurring spend that drives AI compute demand.
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