Currently, yes. But I don't find it hard to imagine that in a while we could get reasonably light open models with a level of reasoning similar to current opus, for instance. In such a scenario how many people would opt to pay for a way more expensive cloud subscription? Especially since lots of people are already not that interested in paying for frontier models nowadays where it makes sense. Unless keep on getting a constant, never ending stream of improvements we're basically bound to get to a point where unless you really need it you are ok with the basic, cheaper local alternative you don't have to pay for monthly.

I think average users are already okay with the reasoning level they'd get with current open models. But the big AI firms have pivoted their frontier models towards the enterprise: coding and research, as opposed to general chat. And scale is quite important for these uses, ordinary pro hardware is not enough.

This is really just a question of product design meeting the technology.

Today, lots of integer compute happens on local devices for some purposes, and in the cloud for others.

Same is already true for matmul, lots of FLOPS being spent locally on photo and video processing, speech to text, …

No obvious reason you wouldn’t want to specialize LLM tasks similarly, especially as long-running agents increasingly take over from chatbots as the dominant interaction architecture.

> If you want to run AI models at scale and with reasonably quick response, there's not many alternatives to datacenter hardware.

Right now, certainly. Things change. What was a datacenter rack yesterday could be a laptop tomorrow.