How does the Transformer architecture enhance parallelization compared to traditional RNNs?

RNNs process tokens one after another, so each step must wait for the previous one to finish, which limits parallel work. The Transformer uses self‑attention, letting every token look at all others at the same time, so each token can be computed in parallel. This independence lets GPUs perform all token computations in a single batch, dramatically speeding up training and inference. For example, translating a 50‑word sentence with a Transformer can compute all 50 positions in one pass, while an RNN would need 50 sequential steps. As a result, Transformers achieve far greater parallelization and efficiency than traditional RNNs.