I find that when I use Python's concurrent.futures.ThreadPoolExecutor the vms memory use (as reported by psutil) increases dramatically.
In [1]: import psutil
In [2]: psutil.Process().memory_info().vms / 1e6
Out[2]: 360.636416
In [3]: from concurrent.futures import ThreadPoolExecutor
In [4]: e = ThreadPoolExecutor(20)
In [5]: psutil.Process().memory_info().vms / 1e6
Out[5]: 363.15136
In [6]: futures = e.map(lambda x: x + 1, range(100))
In [7]: psutil.Process().memory_info().vms / 1e6
Out[7]: 1873.580032
In [8]: e.shutdown()
In [9]: psutil.Process().memory_info().vms / 1e6
Out[9]: 1722.51136
This seems to be somewhat proportional to the number of threads.