An interesting corollary of this is that if you want to reduce the model size you can compensate by training for longer to achieve the same accuracy. Depending on your training:inference ratio this may be more optimal globally to reduce your total compute costs or even just reduce your frontend latency.
Yeah, though I have not seen a formula which takes the number of expected inference runs into account for calculating the optimal data/parameter balance.
