That just feels like such a pessimistic forecast to me. Of course, the current trajectory of improvements in model efficiency and better commercial GPUs / ML-accelerators may hit a wall.

But I would not be surprised if this was trainable on a commercial GPU at home within that time. But I think another important trend that we are seeing is that you don't need to train these models from scratch.

Open-source "foundation models" means that you can usually get away with the much easier task of fine-tuning, as to not throw away / re-learn everything that these large models have already fit.

Edit: I initially said 2-5 years, but on more reflection this does seem optimistic (for training from scratch).