Comparison

RNN vs LSTM vs Transformer

Comparing sequential token processing with parallel global attention mechanisms.

RNN/LSTMvsTransformer

Verdict: Transformers are the default for sequence modeling due to their parallelization and long-context capabilities; use RNNs/LSTMs only on strictly constrained hardware or extremely long streams where constant-memory inference is required.

RNNs process sequences step-by-step, passing hidden states forward, while Transformers process all tokens in parallel using global attention.

The Short Answer

RNNs and LSTMs read sequences one token at a time, bottlenecking training and losing information over long contexts. Transformers read the entire sequence in parallel using self-attention, allowing them to train vastly faster on modern GPUs and directly connect distant tokens.

Where They Differ

Feature	RNN / LSTM	Transformer
Processing Style	Sequential (must wait for step $t-1$ to compute step $t$ )	Parallel (computes all steps simultaneously during training)
Dependency Path	$O(N)$ path length between token $1$ and token $N$	$O(1)$ path length (any token attends directly to any other)
Memory at Inference	$O(1)$ (fixed-size hidden state)	$O(N)$ (must store the KV cache for all past tokens)
Long-Range Context	Poor (RNNs suffer vanishing gradients; LSTMs delay it but eventually forget)	Excellent (limited only by the size of the attention window)

Choose A When

You are constrained by inference memory: LSTMs maintain a fixed-size state regardless of how many tokens they process. This makes them ideal for endless streams (like sensor data) where a Transformer's KV cache would inevitably run out of memory.
You are modeling strict state machines: If the problem relies heavily on knowing the exact sequential order without skipping, recurrent architectures can sometimes capture stateful dynamics more naturally.

Choose B When

You need to train on a lot of data: The sequential bottleneck of RNNs means they cannot fully utilize modern GPUs during training. Transformers were designed specifically to be parallelizable.
You need long-range recall: In translation or code generation, the model must perfectly recall a word from thousands of tokens ago. Transformers retrieve this in $O(1)$ steps; LSTMs degrade geometrically.

What People Get Wrong

People assume that because Transformers process data in parallel, they have solved the sequence problem forever. In reality, Transformers struggle with the $O(N^2)$ compute cost of self-attention at inference time for very long contexts, leading to the resurgence of modern RNN-like State Space Models (e.g., Mamba) that attempt to combine parallel training with constant-time inference.