Comment by int_19h
1 day ago
I don't think you can ever make a case for it on cost savings in general. Inference is very obviously the kind of problem where things are cheaper at scale, and this is still true for smaller models.
The only reason to run locally is privacy.
Right now the "subsidies" etc I think make the calculus really tough, but for general compute.... for example running CI just on a Mac Mini can get you real cost effective throughput compared to running CI on GH runners and whatnot.
Things get cheaper at scale but that's where the provider's margins come in!
I do think there's also an interesting idea: you buy a box like this and run it at a fixed-ish cost (well, electricity). Your demand goes up but your supply is fixed... and that back pressure means that you still have good cost control.
With cloud providers it's a _biiiiit_ too easy to just increase spend.
Sometimes it's OK for things to just be slow.
Privacy is a great reason, but independence is another. It’s very nice knowing that you’re going to get the same reliable product every time you call the model. Nothing is going to change unless you decide to change it.
That is not a counterargument to cloud inference though. You can also run open weight models in the cloud, and it's still cheaper. So privacy really is the only motivation to run on local hardware.
Ah, no, that’s not cheaper. Renting GPUs adds up quickly and leaves you with nothing in the end.
Renting tokens from open model providers is cheaper but it incurs the same issues: unexpected changes in model quality, inconsistent speeds, service outages.