If you have independent copies of the network learning gradients, then you’re effectively making the batch size smaller— unless you’re doing an all collect and making them sync, in which case there’s a lot of overhead

When you take a batch and calculate gradients, you’re effectively calculating a direction the weights should move in, and then taking a step in that direction. You can do more steps at once by doing what you say, but they might not all be exactly in the right direction, so overall efficiency is hard to compare

I am not an expert, but if I understand correctly I think this is the answer.

Batch size is just averaging the gradients from multiple calculations.