the way i see it, the expensive part should be to train the models via simulated architectures in GPUs or TPUs or whatever.

but once they are trained, is there a way to encode the base models into hardware where inference costs are basically negligible? hopefully somebody is seeing if this is possible, using structurally encoded hardware to make inference costs basically nil/constant.