Another possible explanation is speculative decoding, where you trade unused GPU memory for speed (via a drafting model).
But my money is on the exact two mechanisms the OP proposes.
It is worth noting that consumers are completely and totally incapable of detecting quality degradation with any accuracy. Which is a given since the models are already effectively random, but there is a strong bent to hallucinate degradations. Having done frontend work for an AI startup, complaints of degrading the model were by far the most common, despite the fact that not only did our model not change, users could easily verify that it didn't change because we expose seeds. A significant portion of complainers continue to complain about model degradation even when shown they could regenerate from the same seed+input and get the exact same output. Humans, at scale, are essentially incapable of comprehending the concept of randomness.
Yeah they can’t tell, but also there’s lots of incentive for major LLM providers to lie about not doing something that would massively save their inference costs if they did.