Carbon Emissions and Large Neural Network Training

How big AI training makes a surprise climate bill

Training huge AI systems uses lots of power, and that cost can hide in plain sight.
Some big models burn far more energy than you'd expect, while others, that use smart tricks, can cut use down to a tiny fraction.
Where the work runs matters a lot — the same job in different places can make 5 to 10 times more carbon pollution.
Cloud centers often run cooler and use hardware that is much more efficient than older rooms, so picking the right datacenters and chips can lower emissions by hundreds or even thousands fold, if done right.
People building models can choose to train when the grid is cleaner, and move work to sites with greener power.
That choice alone will help to reduce the footprint.
We should ask for clear energy numbers when a big model is reported, so teams can compare and choose better.
Small steps in planning add up to big wins for the planet and for smarter, cleaner AI that everyone can use.

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Carbon Emissions and Large Neural Network Training

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