Transformers are more parallelizable than RNNs because:
Answer options
A
They remove recurrence and process sequences simultaneous
B
They use smaller models
C
They use GPUs only
D
They use SVMs internally
Correct answer: They remove recurrence and process sequences simultaneous
Explanation
Quick AnswerThe correct answer is They remove recurrence and process sequences simultaneous because it directly addresses the core logic of Generative AI.
Transformers process all tokens in parallel using self-attention (unlike sequential RNNs), making them faster to train and better at capturing long-range dependencies.