These are exactly the kinds of models that you can easily run locally by repurposing existing hardware. Depending on how much you're willing to wait for the answer, running local even gives you strictly better outcomes for simple Q&A queries.

(Long-context and agentic use cases are admittedly much harder to fit under that model, since non-AI uses for the high-end hardware you'd realistically need for those are rather more limited, and they're hit by the ongoing hardware shortage.)

I expect that this kind of burned-in model is also very difficult to verify (how do you know if some of the weights are off), and not amenable to partial disablement to increase yield. For CPUs, you just laser disable bad cores. Can't forego part of a neural net.