In the scientific computing environments that I have in mind, hardware is often fixed: you have your several-million-dollar supercomputer on site, or a fixed compute budget at a supercomputer center. Now, do you want your result in one day or 100 days? Because that's the compute time ratio we're talking about.
Not really, no, at least not without some context. Lots of people use python and numpy on very large computers. Also, running time is not the interesting metric: dev + runtime is. The tradeoffs depend on your team, the problem, etc...
Hardware time is often cheaper than engineer time, so Python may be faster/cheaper if you consider total time to value.