The scaling is brutal. If you have a 20k word vocabulary and want to do 3 grams, you need a 20000^3 matrix of elements (8 trillion). Most of which is going to be empty.
GPT and friends cheat by not modeling each word separately, but a large dimensional “embedding” (just a vector if you also find new vocabulary silly). The embedding represents similar words near each other in this space. The famous king-man-queen example. So even if your training set has never seen “The Queen ordered the traitor <blank>”, it might have previously seen, “The King ordered the traitor beheaded”. The vector representation lets the model use words that represent similar concepts without concrete examples.