That is an interesting question and would be interesting to research. I do not have an answer myself.
I use nvidia-smi -l to see what my GPU is up to but this only gives you basic information. I think that newer versions of keras started preallocating all the GPU memory, so it doesn’t tell you much, but with torch you can see how much GPU memory is utilized by your process, that can be quite handy.
There are also more considerations that go into the batch size, so making it a heuristic to always stick as many training examples into a batch might not be ideal. Still, I think for this purpose - to have a cursory glance at model size in GPU given batch size and other parameters - nvida-smi seems to work quite nicely for torch at least based on my 2 hrs or so of experience with torch thus far 