> I don’t know. After the model has been created (trained), I’m pretty sure that generating embeddings is much less computationally intensive than generating text. But it also seems to be the case that embedding models are trained in similar ways as text generation models2, with all the energy usage that implies. I’ll update this section when I find out more.
Although I do care about the environment, this question is completely the wrong one if you ask me. There is the public opinion (mainstream media?) some kind of idea that we should use less AI and somehow this would solve our climate problems.
As a counterexample, let's go to the extreme. Let's ban Google Maps because it does take computational resources from the phone. As a result more people will take wrong routes, and thus use more petrol. Say you use one gallon of petrol extra, that then wastes 34 kWh. This is of course the equivalent of running 34 powerful vacuum cleaners on full power for an hour. In contrast, say you downloaded your map, then the total "cost" is only the power used by the phone. A mobile phone has a battery of about 4 mAh, so 0,004 Ah * 4.2 V = 0.168 W, or 0.000168 kW. This means that the phone is about 200 000 times as efficient! And then we didn't even consider the time-saving for the human.
It's the same with running embeddings for doc generation. An Nvidia H100 consumes about 700 W, so say 1 kWh after an hour of running. 1 kWh should be enough to do a bunch of embedding runs. If this then saves, for example, one workday including the driving back and forth to the office, then again the tradeoff is highly in favor of the compute.