Unverified50% confidenceFactExact time
In recurrent architectures, each loop independently generates a KV cache at inference time, so memory usage grows linearly with loop depth.
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10/23/2026
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VerifiedThe autoregressive generation process of reasoning models is strictly sequential; KV Cache VRAM usage grows linearly with the length of the thinking token sequence, making memory bandwidth the performance bottleneck.77% similarUnverifiedRecurrent architectures internalize additional computation inside the model's forward pass, whereas chain-of-thought or reasoning models generate more tokens externally.72% similarUnverifiedArchitectures with recurrence can theoretically express iterative algorithms by repeatedly applying the same transformation, whereas fixed-depth feedforward networks have difficulty naturally expressing this type of computation.69% similarUnverifiedThe VRAM occupied by the context window is calculated separately from the model weights, and KV cache VRAM usage grows linearly with context length.69% similarUnverifiedRecurrent architectures improve model quality by feeding activations repeatedly through the same set of layers without increasing the parameter count.69% similar
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