I would say that is highly unlikely if by SOTA models you are not just referring to coding benchmarks but more general purpose ability and domain-specific knowledge. For example Kimi 2.6, which is comparable to Opus 4.6, is roughly 500+GB large, and I don't see how that would run on consumer hardware anytime soon. Besides, this is not just about the technical feasibility, but also economically not viable whatsoever. Why should consumer laptops be capable of running such models, when they would be massively underutilized most of the time, when inference providers can produce the same results faster, cheaper and a lot more viable economically?
It runs right now on 512gb RAM Macs and PCs.
It runs like shit though in terms of tokens/second and still has a reduced context window. Vs a single claude prompt can easily get into 300k tokens without breaking a sweat.
I want local AI to be a thing but the hardware isn’t here yet, because the only options are a Mac Studio or DGX machines strapped together. RAM prices needs to crash before local AI has a chance at actually competing.
The more recent Chinese models are no longer heavily limited by context size. It can easily fit in RAM on a prosumer laptop. (You can also use swap space to extemd that, since context is only written to once per inference, thus a relatively mild wear-and-tear concern.)
Because privacy has perceived value.