Speculative decoding does this to an extent - using a smaller model to generate its own predictions and putting them in the batch of the bigger model until they diverge
It doesn’t. It simply trades compute efficiency by transposing matrix multiplications into “the future.” It doesn’t actually save FLOPs (uses more) and doesn’t work at large batch size
Does anyone even care? Really, who cares? The truth is nobody cares. Saving FLOPs does nothing if you have to load the entire model anyway. Going from two flops per parameter to 0.5 or whatever might sound cool on paper but you're loading those parameters anyway and gained nothing.