I'm really concerned that some of the providers are using quantized versions of the models so they can run more models per card and larger batches of inference.
I'm really concerned that some of the providers are using quantized versions of the models so they can run more models per card and larger batches of inference.
We are heavily incentivized to prioritize/make transparent high-quality inference and have no incentive to offer quantized/poorly-performing alternatives. We certainly hear plenty of anecdotal reports like this, but when we dig in we generally don't see it.
An exception is when a model is first released -- for example this terrific work by artificial analysis: https://x.com/ArtificialAnlys/status/1955102409044398415
It does take providers time to learn how to run the models in a high quality way; my expectation is that the difference in quality will be (or already is) minimal over time. The large variance in that case was because GPT OSS had only been out for a couple of weeks.
For well-established models, our (admittedly limited) testing has not revealed much variance between providers in terms of quality. There is some but it's not like we see a couple of providers 'cheating' by secretly quantizing and clearly serving less intelligence versions of the model. We're going to get more systematic about it though and perhaps will uncover some surprises.