I agree with you, and I'm using claude and other models heavily, the idea is not in the soneshot - because actually as you see in the logs I posted on github, it took around 107 + 50-ish experiments to figure out which models to run which and push the speed.

I'm treating it as a search algorithm project, where I develop the methdology and search for pushing the limit of devices and searching in a big space of possible kernels and so for what works for a specific hardware.

so I'm looking at it from a different prespective, it takes a lot of brute forcing, and claude can do this well, the whole game (to me) is which directions you push the model into to reduce the search space. and how that learning from very small and tight devices you can scale these techniques.

But in essence, I don't disagree with your sentiment - the goal is not 0.6B models, these are extremely trivial as you mentioned and as I showed in the experiments, the goal is 16B+ models, and this as far as I've seen, requires smart methods to search for optimizations and customizations both to the model and inference engines.