The provided deepspeed config also activates CPU memory offloading, so chances are that if you
have a lot of available CPU memory and you don't mind a slowdown you should be able to load a
model that doesn't normally fit into a single GPU. If you have enough GPU memory the program will
run faster if you don't want offload to CPU - so disable that section then.

To deploy on 1 gpu:

deepspeed --num_gpus 1 t0.py
or:
python -m torch.distributed.run --nproc_per_node=1 t0.py

To deploy on 2 gpus: