LSTMs versus Transformers
Transformers dominate offline large-scale sequence modeling because self-attention parallelizes over the whole sequence during training, but an LSTM's per-step cost is O(1) in sequence length with constant memory, versus attention's growing KV cache. LSTMs and their modern linear-recurrent descendants (xLSTM, state-space models like Mamba) still win in streaming inference, low-latency and on-device settings, and small-data time-series tasks.
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Transformers dominate offline large-scale sequence modeling because self-attention parallelizes over the whole sequence during training, but an LSTM's per-step cost is O(1) in sequence length with constant memory, versus attention's growing KV cache. LSTMs and their modern linear-recurrent descendants (xLSTM, state-space models like Mamba) still win in streaming inference, low-latency and on-device settings, and small-data time-series tasks.
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