[hdkrgr]

Sure... but maybe the GPU is sitting idle 40% of the time while still consuming 200W. Should I have to break this idle energy consumption down onto actual use (assuming the server/gpu is only used for this one model)? I guess it would make sense, but... WHO should do this and then continually update the model documentation when idle rates or the hardware changes?

[senko]

The organizations that release the models already provide (brag about) their model performance. They could simply include in the same report the info about the energy spent doing the training/finetuning/inference, per X tokens.

This doesn't necessarily measure every use, just "manufacturer's spec", the same you get for eg energy class for house appliances (at least in the EU). Nobody goes around measuring refrigerator power usage, but when you're buying one, you get a rough indication of how "green" (or not) it is.

[hdkrgr]

I agree, that seems reasonable!

I was referring more to the users of such a system (what the AI Act would call a 'deployer'). They may have significantly less expertise but could still be required to track real-time energy use. Of course, simply referring to the 'energy label' by the provider could be a viable solution.

[Dylan16807]

Listing it per server design (with groups) makes sense to me.

It wouldn't make sense to include measured idle time in the energy numbers you'd include in model documentation. Maybe that could go in a monthly report somewhere, but that's a different topic.