On paper. There's huge financial incentive to quantize the crap out of a good model to save cash after you've hooked in subscriptions.
On paper. There's huge financial incentive to quantize the crap out of a good model to save cash after you've hooked in subscriptions.
And there’s an incentive to publish evidence of this to discourage it, do you have any?
Models aren't just big bags of floats you imagine them to be. Those bags are there, but there's a whole layer of runtimes, caches, timers, load balancers, classifiers/sanitizers, etc. around them, all of which have tunable parameters that affect the user-perceptible output.
There really always is a man behind the curtain eh?
Often it's literally just that:
https://www.msn.com/en-us/money/other/ai-startup-backed-by-m...
It's still engineering. Even magic alien tech from outer space would end up with an interface layer to manage it :).
ETA: reminds me of biology, too. In life, it turns out the more simple some functional component looks like, the more stupidly overcomplicated it is if you look at it under microscope.
There's this[1]. Model providers have a strong incentive to switch (a part of) their inference fleet to quantized models during peak loads. From a systems perspective, it's just another lever. Better to have slightly nerfed models than complete downtime.
[1]: https://marginlab.ai/trackers/claude-code/
So - as the charts say - no statistical difference?
Isn't this link am argument against the point you are making?
The chart doesn't cover the 4.6 release which was in the end of December/early January time frame. So, it's hard to tell from existing data.
Anybody with more than five years in the tech industry has seen this done in all domains time and again. What evidence you have AI is different, which is the extraordinary claim in this case...
Or just change the reasoning levels.