The math. It’s pretty brutal. You have to multiply insanely large matrices in memory. A dense model has to have and multiply across every parameter basically. MoE models work around it, sure, but you still have to hold a lot in memory and density of activated parameters matters. There is not much of a way around it yet. We are potentially getting there, but its not obvious yet.
And scaling intelligence is seemingly only achievable by making these matrices larger?
What makes an etched LLM into silicon better at big matrix multiplication