AI Model Training water consumption estimate
GPT-3 training ≈ 5.4 million liters of water (combined on-site cooling + off-site electricity), based on Li et al. (2023) assumptions for Microsoft U.S. data centers.
Source: Li et al. 2023 — Making AI Less “Thirsty”
AI Water Impact Calculator
Estimate water used for a single LLM query (cooling + electricity)
Enter your typical ChatGPT question
Assumptions (editable)
Results
Estimated tokens
62 tokens
Off-site electricity
1.153 mL
Total water for this query
1.463 mL
≈ 0.003x 500 mL bottles
If 1 million users ran similar query (as of 2025 chatGPT projected to have 700 Million users)
Total water
1,463.2 L (1.463 m³)
≈ 2,926.4x 500 mL bottles
Use AI responsibly
Practical ways to reduce the water footprint of your AI usage:
• Choose smaller/faster models when they meet your needs.
• Batch requests and avoid repeated runs of the same query.
• Refine prompts to be concise and to the point.
• Cache and reuse results where possible.
• Turn off streaming or long outputs unless necessary.
Notes, references, and assumptions
This is a rough estimate combining on-site cooling water and off-site electricity water. Real values vary by data center cooling type, weather, grid mix, and model size.
Li et al. (2023) Making AI Less “Thirsty” — methodology and example figures
HTML version with details on WUE/EWIF and per-request estimates
Meta Sustainability: data center water usage and WUE figures
Google Environmental Reports: water metrics and WUE context
IEA Water–Energy Nexus (context on electricity water intensity)