Inference isn’t flat 24x7, peak hours have more usage, but you buy/rent servers; not servers only for peak hours.

At their scale, you’d have to be setting money on fire if you’re not doing dynamic inference optimisations based on load.

API and consumer subscriptions are treated differently; all trackers measuring via API won’t notice this.

During peak hours requests queue and inference slows. During off-peak they can move systems over to training.

Where is the evidence they are "nerfing" the models due to request volume?

Edit: I don't know they do, I mean they could repurpose systems if they are idle. Inference demand is global, and providers like Azure have global routing options that are cheaper. Night time in the USA could be serving inference demand on the other side of the globe.