* Servers in a datacenter are much more reliable than a network of PCs (power goes off, someone decides to play Crysis, etc)
* People will find ways to scam you (pretend like they’re doing the calculation while not actually doing it)
* Economies of scale means a datacenter will probably be cheaper than what you’d have to pay the PC owners (power consumption, wear, etc)
I’ve pitched this idea more than a decade ago and quickly realized it doesn’t work in practice.
I'd contribute my GPU time to a Folding@Home style project if it meant that we had powerful, open LLMs that were free to use. I'm positive many others would as well.
As far as worrying about scammers, could you send the same compute task and training data to multiple clients and validate the results against each other? If they differed, you could throw the results and try again, or send it to a 3rd to break the tie.
By sending the work to 3 people each time, you’re effectively cutting your (already limited) resource pool by 66%.
It's not especially useful though because most companies (who are actually going to pay for this service) aren't going to want to send their training data to random people, and ML training needs high performance links between the cards. Plus you'd have to deal with the fact that you're running on 100 different GPU models.