AI Carbon Footprint Calculator
Estimate the carbon footprint of AI and machine learning operations. Calculate CO₂ emissions from model training, inference workloads, and data center energy consumption with regional carbon intensity factors.
Inference Configuration
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How Much CO₂ Does AI Actually Produce?
AI and machine learning workloads consume massive amounts of energy. Training GPT-3 emitted an estimated 552 tonnes of CO₂—equivalent to driving 1.2 million miles. Our AI Carbon Footprint Calculator helps you estimate emissions from training and running AI models based on GPU usage, energy consumption, and regional carbon intensity.
Understanding AI Carbon Emissions
AI carbon footprint comes from two main sources: training (initial model creation, typically done once) and inference (running the model for predictions, ongoing). Training large models can take weeks on hundreds of GPUs, while inference emissions accumulate over millions of daily queries.
Carbon Footprint Formula
CO₂ = Energy (kWh) × Carbon Intensity (gCO₂/kWh)Why Calculate AI Carbon Footprint?
ESG Reporting
Companies increasingly need to report Scope 3 emissions, including cloud computing and AI workloads.
Cost Correlation
Energy consumption directly correlates with cloud costs. Reducing carbon often means reducing expenses.
Region Selection
Carbon intensity varies 20x between coal-heavy grids and renewable data centers. Choose greener regions.
Model Selection
Smaller, efficient models may suffice. Compare carbon cost of different model sizes before committing.
How to Use This Calculator
Frequently Asked Questions
Training GPT-3 (175B parameters) produced an estimated 552 tonnes of CO₂—equivalent to 5 average Americans' annual emissions. GPT-4 is estimated at 10-50x more. Smaller models like LLaMA 7B produce roughly 50-100 tonnes depending on the training setup and data center location.