Yes you can save it in torch format to make sure that you don’t need the extra hop when loading between numpy and torch.
Reading many small files from disk is slow (especially for spinning disks) there is no way around that I’m afraid. You can increase the number of workers until it starts slowing down. But beyond that there isn’t much you can do if you dataset doesn’t fit in memory.