Overview
RNNs process sequences one token at a time, carrying context forward through a hidden state that updates at each step. Transformers instead process every token simultaneously, letting each position directly attend to every other via self-attention. The difference reshapes everything from training speed to how well long-range context survives.
Comparison Diagram
Comparison Table
| Aspect | RNN | Transformer |
|---|---|---|
| Input processing order | Tokens consumed one at a time, in sequence | All tokens consumed simultaneously |
| Context propagation | Hidden state carried forward step to step | Self-attention lets each position read all others directly |
| Long-range dependencies | Signal weakens over distance (vanishing/exploding gradients) | Direct connection between any two positions regardless of distance |
| Positional information | Implicit, from the order tokens are fed in | Explicit, via added positional encodings |
| Training parallelization | Limited — must unroll and step through time | Fully parallel across the sequence dimension |
| Computational cost | O(n) sequential steps, O(1) state per step | O(n^2) attention cost over sequence length |
| Inference/generation | Constant memory, naturally one step at a time | Requires KV caching to avoid recomputing past attention |
| Typical use cases | LSTM/GRU for streaming or small-scale sequence tasks | BERT/GPT-style models for large-scale language and vision tasks |
Key Differences
- Transformer processes all tokens in parallel via self-attention; RNN processes tokens sequentially through a hidden state
- RNN suffers from vanishing gradients over long sequences; Transformer links any two positions directly
- Transformer needs explicit positional encodings since attention has no inherent order; RNN gets order for free
- Transformer training scales with quadratic complexity in sequence length; RNN training is linear but hard to parallelize
When to Use Each
RNN
- Streaming/online signals: Constant per-step memory suits real-time sensor or audio streams processed incrementally
- Small or short-sequence datasets: Fewer parameters make RNNs less prone to overfitting when data or context length is limited
- Resource-constrained inference: O(1) memory per step fits low-power or embedded devices better than caching a full attention context
Transformer
- Long-document modeling: Direct attention across positions avoids the gradient decay that limits RNNs on long inputs
- Large-scale pretraining: Full parallelism across the sequence lets training exploit modern GPU/TPU clusters efficiently
- Tasks needing global context: Translation and summarization benefit when every token can weigh every other token directly