Overall, when tested on 40 prompts, DeepSeek was found to have a similar energy efficiency to the Meta model, but DeepSeek tended to generate much longer responses and therefore was found to use 87% more energy.
Overall, when tested on 40 prompts, DeepSeek was found to have a similar energy efficiency to the Meta model, but DeepSeek tended to generate much longer responses and therefore was found to use 87% more energy.
It’s more like comparing them while they use the same fuel (as the article directly compares them in joules): Let’s say the train also uses gasoline. The car is a far more “independent”, controllable, and “doesn’t waste fuel driving to places you don’t want to go” and thus seen as “better” and more appealing, but that wide appeal and thus wide usage creates far more demand for gasoline, dries up the planet, and clogs up the streets, wasting fuel idling at traffic stops.
Yeah, I was thinking diesel powered trains
The AI models use the same fuel for energy.
Yes, sorry, where I live it’s pretty normal for cars to be diesel powered. What I meant by my comparison was that a train, when measured uncritically, uses more energy to run than a car due to it’s size and behavior, but that when compared fairly, the train has obvious gains and tradeoffs.
Deepseek as a 600b model is more efficient than the 400b llama model (a more fair size comparison), because it’s a mixed experts model with less active parameters, and when run in the R1 reasoning configuration, it is probably still more efficient than a dense model of comparable intelligence.