Probably a combination of (a) ML framework people not paying much attention to CPU inference due to already having GPUs/TPUs already lying around for training - CPU inference is just for very quick experiments (b) research code has never been the best optimized for performance (c) ML people are not generally systems programmers, and a lot of systems programmers are afraid to mess with the ML code outside of low-level computation kernels (doesn't help that ML code is notoriously unreproducible).
It's indeed a very different world. This model was trained on thousands of GPUs. The weird file format corresponds to the train time sharding of the weights. And really nobody is doing CPU inference with all the GPU we have. And also the "CLI" use case seems contrieved to me. If you plan to interact several times with the model and want to keep the weights in RAM, why don't you start a REPL or spin up a server?
