I'd love to see credible numbers on the energy usage of thousands of people running models on their own devices compared to sharing data center resources to run big models that serve many different people at the same time.

My hunch is that the energy/water usage of the data centers is a whole lot more efficient than everyone running at home, but I'd be interested in seeing real data on that.

Water usage goes up with data centers because more cooling is needed when you run the hardware harder.

So: if you're running the models on your own machine, presumably you're not running them as often, and air cooling is sufficient. But, at the same time, this is less efficient in terms of hardware use; the data centers need water cooling specifically because they're getting more bang from their buck from their hardware, by running their hardware harder.

So that's the tradeoff: more hardware-use efficiency means more water usage.

With hardware like the Spark and Strix, the water usage is known to be zero, yea?

On the energy front, I assume less efficient, but I also think there is a tradeoff in efficiency versus freedom, that's why I have my own hardware.

the comparison misses that local LLM usage covers tasks you'd never send to an API — private code, offline work, medical notes. the baseline is 'local vs not-doing-it', not 'local vs cloud'