Note that the minimal loss is reached when the parameters make neural network predictions the closest to the real labels

Before that, the gradient is non zero generally (except for an very very unlucky local minimum)

You could see the case of the linear regression with least square error as loss to understand better the underlying optimization problem (in one dimension, it is a square function to minimize, so no local minimum)