The models are too large to fit on a desktop GPU's VRAM. Progress would either require smaller models (MoE might help here? not sure) or bigger VRAM. For example training a 70 billion parameter model would require at least 140GB of VRAM in each system, whereas a large desktop GPU (4090) has only 24GB.

You need enough memory to run the unquantized model for training, then stream the training data through - that part is what is done in parallel, farming out different bits of training data to each machine.