Transformers have revolutionized almost allnatural language processing (NLP) tasks butsuffer from memory and computational com-plexity that scales quadratically with sequencelength. In contrast, recurrent neural networks(RNNs) exhibit linear scaling in memory andcomputational requirements but struggle tomatch the same performance as Transformersdue to limitations in parallelization and scala-
Transformers' memory and compute scale quadratically with sequence length. RNNs' memory and compute scale linearly, but performance is not as good as Transformers due to parallelization and scalability limits.