AI Environmental Impact

Energy · Carbon · Water

The Environmental Cost of AI

AI is transforming every industry — but its environmental footprint is growing fast. Here are the numbers, in context.

Global Data Center Map

Company Reports(2025)

Major cloud data center locations where AI workloads run. Larger dots indicate higher capacity clusters.

Northern Virginia (Ashburn) (multi)Dallas/Fort Worth (multi)The Dalles, Oregon (google)Quincy, Washington (microsoft)Phoenix, Arizona (multi)Council Bluffs, Iowa (google)Dublin, Ireland (multi)Amsterdam, Netherlands (multi)Frankfurt, Germany (multi)London, UK (multi)Singapore (multi)Tokyo, Japan (multi)Sydney, Australia (multi)Mumbai, India (multi)São Paulo, Brazil (multi)Stockholm, Sweden (multi)
Largest hub
Large
Medium
Northern Virginia (Ashburn) is the world's largest data center cluster — over 300 data centers within a 30-mile radius
5
Europe
4
Asia Pacific
3
US West
2
US Central
1
US East
1
South America

Model Energy Efficiency

HuggingFace AI Energy Score(2025)

Energy consumption per 1,000 queries on NVIDIA H100. Smaller models are dramatically more efficient — a 10x+ difference between the most and least efficient.

★★★★★
★★★★★
★★★★★
★★★★★
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Key Insight

Choosing the right model for the task can reduce energy consumption by 10-200x. A Mistral 7B query uses just 15 Wh/1K — while DALL-E 3 image generation uses 3,200 Wh/1K. Model selection is one of the most impactful decisions for reducing AI's environmental footprint.