Unverified50% confidenceFactExact time
State space models (SSMs) have O(1) memory footprint and computation during inference, whereas the KV cache of Transformers grows linearly with sequence length.
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50%
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Long-term
Relevance
9/23/2026
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UnverifiedTransformer models accelerate inference through the KV Cache mechanism, but its memory usage is proportional to context length.73% similarUnverifiedIn the Transformer architecture, there is an approximately linear relationship between model parameter count and inference latency.71% similarUnverifiedEach additional token generated by an autoregressive model provides one more opportunity for a Transformer forward pass computation, trading sequence length for computational depth.70% similarVerifiedState space models (SSMs) such as Mamba abandon the attention mechanism and use recurrent structures to achieve linear time complexity sequence modeling.69% similarVerifiedThe self-attention mechanism of the Transformer allows the model to directly compute dependencies between any two positions in a sequence, breaking through the parallelization bottleneck caused by the sequential computation of RNNs/LSTMs.69% similar
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