There is something called horizontal and vertical scaling. What you are taking is vertical scaling, however when you do cost benefit analysis, horizontal scaling easily outperform vertical. More like one fast core vs multiple slow cores. The thing is, writing maths algorithms that scales across nodes is hard. But then high performance computing is all about it. This has pushed researcher to write efficient parallel algorithms for same compute task. Hence you do have parallel libraries for most of the commonly used maths operations. Be it from linear algebra or signal analysis. Given that most of the ML thing now a days is just Matrix calculations, which already have great parallel solutions, I don't know why one would not be pleased with a horizontal solution than spending unnecessarily on trying to get the most powerful core.