Comment by anon373839
1 day ago
It is costly, especially right now. I don’t think you can make a case for it on cost savings!
The throughput in a single stream is about 50 tokens/sec (a bit less for prose, a bit more for code due to speculative draft acceptance rates) and about 2,000 tokens/sec for prefill. Both numbers are flat and stable as context accumulates. That’s what finally tilted me away from the Mac Studio despite its much superior memory bandwidth.
I think these numbers may improve because the model is pretty new and optimizations aren’t done.
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.
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