You said there’s no reason to think you can just keep making the model bigger and keep getting improved capability.

there’s the structure of the neural network itself. Fundamentally, adding nodes and layers increases the ability of the model to handle more complex input.

Then there’s the actual models we see in use. They are literally as large as the hardware allows. The only reason to use smaller models are to fit some constraint.

So both by the book and in practice bigger is always better.

Now we can’t always go big. I can’t afford to purchase a dgx or even upgrade my wiring to power it, let alone pay the power bill it would rack up or all the other utilities alone when my wife leaves me because of the sound.

My computer can only fit so many expansion cards and pcie is so slow compared to hbm that I’m better off running a small model quickly that fits on one card as opposed to a larger one slowly across several cards.

But those are all constraints. When I replace my motherboard with supermicro gpu host fabric I no longer am limited by the pcie bandwidth and can quickly use models that fit across several cards.

I do agree with you that the future is smaller models, not because of the fundamental nature of the concepts involved but because of the complex constraints that are coming into play.