I haven't touched this in a while, but you can train NNs in a distributed fashion and what GP described is roughly the most basic version of model parallelism, where there is a copy of the model on each node, each node receives a batch of data, and the gradients get synchronized after each batch (so they again start from the same point like you mention).
Most modern large models cannot be trained on one instance of anything (GPU, accelerators, whatever), so there's no alternative to distributed training. They also wouldn't even fit in the memory of one GPU/accelerator, so there are even more complex ways to split the model across instances.
Most modern large models cannot be trained on one instance of anything (GPU, accelerators, whatever), so there's no alternative to distributed training. They also wouldn't even fit in the memory of one GPU/accelerator, so there are even more complex ways to split the model across instances.