Right, all this overhead is very pytorch specific and doesn't have to be present in all cases. If you resolve everything at compile-time for a limited class of

Right, all this overhead is very pytorch specific and doesn't have to be present in all cases. If you resolve everything at compile-time for a limited class of cases (which is what vectorflow does), it's possible to have a solution where none of this overhead exists at runtime (no dispatch, no indirections, no memory copies nor allocations...), neither during training nor serving. I think pytorch has a genius design and makes excellent tradeoffs. But it's not the perfect solution to every problem. Genericity vs specialization: hard to win on all fronts.