It would be interesting to see an analysis of this. I see your point - otoh is there a reason to believe that more computation is being "banked" than say matrix inversion, or other optimizations that aren't gradient descent based?

The large datasets involved let us usefully (for some value of useful) bank lots of compute, but it's not obvious to me that it's done particularly efficiently compared to other things you might precompute.

For converged model training, training is often quite inefficient because the weight updates decay to zero and most epochs are having a very small individual effect. I think for e.g. stable diffusion, they dont train to anywhere near convergence so weight updates have a bigger average effect. Not sure if that applies to llms