Sep 9, 2025
Transformers vs RNNs: What Changed and Why It Matters
Transformers replaced RNNs as the dominant sequence model because they solve two major RNN limits: long‑range dependency handling and sequential computation.
This post explains the core differences and why transformers became the default.
How RNNs process sequences
RNNs (Elman, LSTM, GRU) read sequences one step at a time:
- Each step depends on the previous hidden state.
- This makes them sequential, so training and inference are slower.
- Long‑range memory fades due to vanishing gradients (even with LSTM/GRU, though it’s better).
How transformers process sequences
Transformers process all tokens in parallel and use self‑attention to connect each token to all others:
- Each token can “attend” to any other token.
- Long‑range dependencies are captured directly.
- Training scales efficiently on GPUs/TPUs.
Key differences
| Dimension | RNNs (Elman/LSTM/GRU) | Transformers |
|---|---|---|
| Computation | Sequential | Parallel |
| Long‑range memory | Limited | Strong |
| Training speed | Slower | Faster |
| Interpretability | Moderate | Harder |
| Compute cost | Lower | Higher |
Where transformers shine
- Language models (chatbots, translation, summarization)
- Vision (ViT, detection)
- Multimodal systems (text + image + audio)
- Long‑context tasks (code, documents)
When RNNs still make sense
- Small embedded systems with tight compute limits.
- Short sequences where parallelism doesn’t matter.
- Low‑latency streaming scenarios.
Summary
Transformers replace RNNs by removing sequential bottlenecks and making long‑range dependencies easier to model. RNNs still have niche uses, but transformers are the default for most modern sequence tasks.