It depends on the context length. Since Attention scales n^2, and rnn scales in n, based on document length the speed up is a factor of n. Now, there are also some slow downs. I am certain his RNN solution here has to do some tricks which are more complex than just a simple rnn. But the longer the context, the faster the speed up relative to a transformer. So 100x on a large doc is not necessarily impossible (at least at inference time).

I have a hard time believing the memory claims as well though. Again, I really wish the author would write a paper about it. Because as far as I can see, if he’s using standard back propagation through time to train, the memory requirements should likely be quite dramatic. But again, I think he’s doing something special with his RNN, I just don’t know what it is.