3,000-4,000 acres per GW of production capacity in the US Southwest. According to AI :)

Considering how little use there is for most of that land anyways, it seems like a good option to me.

Also AI training seems like the perfect fit for solar. Run it when the sun is shining. Inference is significantly less power hungry, so it can run base load 24/7.

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creato 2 days ago | parent | next [-]

> Also AI training seems like the perfect fit for solar. Run it when the sun is shining. Inference is significantly less power hungry, so it can run base load 24/7.

If you're talking about just not running your data center when the sun isn't out, that effectively triples the cost of the building+ hardware. It would require a hell of a carbon tax to make the economics of this make sense.

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rapsey 2 days ago | parent | prev | next [-]

> Inference is significantly less power hungry, so it can run base load 24/7.

All major AI providers need to throttle usage because their GPU clusters are at capacity. There is absolutely no way inference is less power hungry when you have many thousands of users hammering your servers at all times.

	▲	blitzar 2 days ago \| parent [-]
		Furthermore NVIDIAs 80% profit margin makes idling your biggest capital expense a huge ROI problem. Google and Apple should have a big advantage in this regard. If the balance between capital outlay and running costs was more balanced - then optimising the running cost becomes a big line item on the accounts.

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sim7c00 2 days ago | parent | prev [-]

the sun is always shining.