A dictionary of n-grams does not change its probabilities based on "text seen so far", so you'd need an unbounded n. In practice, n-gram dictionaries don't encode for more than very low values of n, so while yes, abstractly we can say that GPT-2 is equivalent to an n-gram dictionary with some high n, those dictionary cannot be constructed, let alone used, leaving the equivalency is purely theoretical.

GPT-2 is not a dictionary.

GPT is way better at next-word predictions than n-grams. However, I agree with OP that the resulting compression scheme is probably not very practical all things considered.