I have an old, slow GPU setup that has nearly 100gb of VRAM

I had been trying to fill this up with big models but it doesn’t seem like these give a good return per Gb

I’m looking at that and wondering would I be better off running multiple such models in parallel. It would probably be a better way to load balance across SLI.

My guess is the scaling will be more “mythical man month” than “no more free lunch” - the interaction of models resembling social dynamics moreso than multi-core setups.

Given that these actors are largely homogenous in culture and incentivising, and coordination overhead is drastically reduced.

Commonly we consider optimal team size to be between 3 and 7 and Brookes’ maximum team size is around 10 or so before the system fails. It should be possible to blow way past those numbers and still experience increased gains in productivity as long as you can keep all your instances stoked.