I mapped estimated water use across 30 major AI/cloud data centers

westurner•about 4 hours ago

Metrics for this?

OPS/FLOPS/TOPS/QOPS: OPS/kWhr, OPS/liter_water,

From https://www.thegreengrid.org/ , whose board includes many industry folks:

WUE: Water Usage Effectiveness: https://en.wikipedia.org/wiki/Water_usage_effectiveness

GPUE: Green Power Usage Effectiveness: https://en.wikipedia.org/wiki/Green_Power_Usage_Effectivenes...

westurner•about 3 hours ago

Data sources; Data quality:

- Is there a suggested bibtex citation for this analysis?

- BibTeX in git for the data and the estimates can be referenced with citation identifiers with various static site build tools. Schema.org/Dataset and ScholarlyArticle JSON-LD is probably easier with React. It should be possible to generate BibTeX from JSON-LD (e.g. with citeproc-js and n3.js or rdflib.js or solidjs/react-solid-state or a different RDFJS solution that can template BibTeX).

- DVC is one way to check data into git, and to evaluate sensitivity to data quality and specificity

Additional features probably worth tracking:

- Zero Water facility?

- Types of thermal fluid in use: Water,

- Heat recovered : Heat and water forfeited to evaporative cooling

- Water egress: % purple pipe water, % steam

senazadeh•about 6 hours ago

Made this after getting curious how the water-use numbers thrown around in AI news articles actually stack up site-by-site. A few notes:

What it shows: a running estimate of global AI/data-center water use, a map of 30 real campuses (Google, Amazon, Microsoft, Meta, Oracle, Apple, Alibaba) sized by estimated annual water draw, and a comparison chart against things like golf courses, fast fashion, and fossil fuel plants on a log scale.

Data sources: per-site figures are triangulated from sustainability reports, utility/permit filings, and known cooling tech + climate where companies don't disclose (most don't). The global baseline is anchored to Lawrence Berkeley National Lab's 2024 Data Center Energy Usage Report, linked in the site's Methodology section.

Tools: React + D3.js for the map, all client-side, no backend.

Caveat I want to be upfront about: these are order-of-magnitude estimates, not audited numbers, happy to take corrections if anyone has better sourcing on specific sites!

https://www.thirstymachines.com/

Zie_Mordecai•about 6 hours ago

This is good. Have you sent this to non-profits or local community leaders who have this on their docket? The data will give more context and awareness to the citizens they are working with.

I mapped estimated water use across 30 major AI/cloud data centers

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