Numpy Random Choice with Non-regular Array Size

I'm making an array of sums of random choices from a negative binomial distribution (nbd), with each sum being of non-regular length. Right now I implement it as follows:

import numpy
from numpy.random import default_rng
rng = default_rng()

nbd = rng.negative_binomial(1, 0.5, int(1e6))
gmc = [12, 35, 4, 67, 2]
n_pp = np.empty(len(gmc))
for i in range(len(gmc)):
    n_pp[i] = np.sum(rng.choice(nbd, gmc[i]))

This works, but when I perform it over my actual data it's very slow (gmc is of dimension 1e6), and I would like to vary this for multiple values of n and p in the nbd (in this example they're set to 1 and 0.5, respectively).

I'd like to work out a pythonic way to do this which eliminates the loop, but I'm not sure it's possible. I want to keep default_rng for the better random generation than the older way of doing it (np.random.choice), if possible.