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

Transformers process all tokens in a sentence at once, using attention to link each token with every other token, so the model can compute many operations in parallel. In contrast, an RNN must handle tokens one after another, waiting for each previous step before moving on. Because Transformers avoid this sequential chain, GPUs can work on all tokens simultaneously, speeding up training and inference. For example, if you have a 10‑word sentence, a Transformer can calculate the relationships among all words in one pass, while an RNN would need ten separate passes, one after the other. This parallel ability makes Transformers much faster on modern hardware.