p.s. from experience instruct-finetuning falcon180b, it's not worth using over llama2-70b as it's significantly undertrained.
We developed Petals for people who have less GPU memory than needed. Also, there's still a chance of larger open models being released in the future.
And the inference is pretty inefficient. Pooling the hardware would achieve much better GPU utilization and (theoretically) faster responses for the host's requests
Here I'm assuming that Petal uses a large number of small, heterogenous nodes like consumer gpus. It might as well be something much simpler.
For inference? Yeah, but its still better than nothing if your hardware can't run the full model, or run it extremely slowly.
I think frameworks like MLC-LLM and llama.cpp kinda throw a wrench in this though, as you can get very acceptable throughput on an IGP or split across a CPU/dGPU, without that huge networking penalty. And pooling complete hosts (like AI Horde) is much cheaper.
I'm not sure what the training requirements are, but ultimately throughput is all that matters for training, especially if you can "buy" training time with otherwise idle GPU time.