Don't forget to account for all the costs. It's not just that CPUs are X times slower. Memory is X times smaller, too, and networks are X time slower. And all this hardware is many times more expensive.
If I'm getting my mental estimation right, training a 2026-frontier-class LLM in 2005 would be somewhere on the order of all the computation power in the world at the time. It's not that many more factors of magnitude before you end up at "all the computation power in the world up to that point".
"Distillation" literally means to separate and take some components out of something. You can distill how a model works from a model. You cant distill a model from information because the information does not contain the model.
People are happy to conflate distilling with building because they dont like how the information was used. You distill how the model works from the model, and you build a model with information. Both could be morally good or bad but its not the same thing.
Not really, what the information actually is, matters a great deal. It's harder to get good results going from "nothing > model+weights" than "nothing + traces from known good sessions of other good model > model+weights", this is what the "distillation" part is referring to. If "information is information", you wouldn't even need to separate good from bad sessions while doing the training, which leads to somewhat obvious results if you don't.
To succinctly restate my point, you cannot distill a model from information because the model is not contained within that information. You can distill a model from another model.
1: That's the "T" in GPT fyi, even though Google is the author of the research paper that changed everything
So the initial models arent just distilled from information. We’ve always had the information.
The first "L" in LLM does the work. In 2005 you had no Github, Stackoverflow, Youtube, common crawl and no archive of digital ebooks.