I wanted to put over a billion floats in a numpy array just a few months ago. Making them 16-bit saved a lot of memory.
It doesn't matter how much resource limits increase, people are going to keep hitting them. And when they hit them, using a smaller data type will always help.
That's not relevant there are plenty of single precision float applications today (and many fixed point applications as well). It all depends on your workload.
It doesn't matter how much resource limits increase, people are going to keep hitting them. And when they hit them, using a smaller data type will always help.