AI Resource Use Dashboard
Public-source reference estimates for AI-related energy, carbon, and water use, shown alongside comparable national, company, and per-query context.
This dashboard combines measured benchmarks, derived estimates, and company-reported disclosures. Use the methodology page for scope notes and comparability limits.
Dataset ages: 1.1y old · 4mo old · 4mo old
415 TWh total data centers (2024)
Range: 24-44 Mt (est. 2025)
Range: 312.5-764.6 B liters (est. 2025)
Per-User Reference Scale
Google Cloud(2025)· 9mo oldPer-query reference values
Search is included only as a rough comparison baseline from the source dataset; the per-query values shown below are specific to Google's 2025 AI inference study.
Annualized estimate (30 queries/day)
Scaling note
With an estimated 1 billion+ AI queries per day worldwide, small per-query values can aggregate into large system-wide totals. Switch to the national view for broader comparisons.
Energy Trends (2020-2030)
IEA(2025)· 1.1y oldGlobal Data Center Map
Company Reports(2025)· 0d oldMajor cloud data center locations where AI workloads run. Larger dots indicate higher capacity clusters.
Model Energy Efficiency
Benchmarks + normalized estimates(2025)· 5mo oldEnergy consumption per 1,000 queries on NVIDIA H100 where public benchmark data exists. Some entries are normalized estimates assembled from public disclosures and should be treated as directional rather than canonical provider measurements.
Visible rows in this view: 5 measured benchmarks and 9 normalized estimates.
Interpretation Note
Model choice can shift energy use materially across tasks, with visible benchmark gaps of 10-200x. A Mistral 7B normalized estimate is 15 Wh/1K, while the DALL-E 3 normalized estimate is 3,200 Wh/1K. Those differences are useful for comparison, but exact values should be read as directional unless marked as measured.
Company-Reported Operations Comparison
Sustainability Reports(2024)· 11mo oldValues below come from company sustainability disclosures. Reporting boundaries and accounting methods differ, so treat this section as directional comparison rather than a perfectly standardized ranking.