Transformers have become the dominant architecture in modern AI, largely replacing RNNs in most applications. The key question is: what makes Transformers so much better? The answer lies in their fundamental approach to processing sequential data. While RNNs process information step by step, Transformers can handle entire sequences simultaneously.
The first major advantage of Transformers is parallel processing. In RNNs, each token must be processed sequentially - you can't compute the hidden state for token 3 until you've finished processing tokens 1 and 2. This creates a computational bottleneck. Transformers eliminate this limitation through self-attention, where every token can attend to every other token simultaneously, making training much faster on modern hardware.
The second major advantage is handling long-range dependencies. RNNs struggle with the vanishing gradient problem - as information flows through many time steps, the gradients become exponentially smaller, making it difficult to learn relationships between distant tokens. Transformers solve this with self-attention, where every position can directly attend to every other position with equal computational distance, enabling strong connections across the entire sequence.
The third advantage is computational efficiency. RNNs require sequential processing, which underutilizes modern parallel computing hardware like GPUs and TPUs. Each time step must wait for the previous one to complete, creating idle compute resources. Transformers, with their parallel architecture, can fully utilize all available cores simultaneously, leading to dramatically faster training times and more efficient inference, especially for large-scale models.
In summary, Transformers have become the dominant architecture because they solve the fundamental limitations of RNNs. Through parallel processing, they train faster on modern hardware. With self-attention, they capture long-range dependencies more effectively. Their computational efficiency enables scaling to massive models. And they provide richer contextual representations for each token. These advantages have made Transformers the foundation of modern AI breakthroughs like GPT, BERT, and beyond.