Unverified50% confidenceFactExact time
Mamba's memory footprint during inference remains constant and does not grow linearly with sequence length, narrowing the gap with Transformers on language modeling tasks.
1
Sources
50%
Confidence
Long-term
Relevance
10/6/2026
First Seen
Sources
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Related Claims
UnverifiedDuring inference, Mamba can run in pure recurrent mode with constant memory usage.79% similarVerifiedThe standard Transformer has no persistent state during inference; every call is a stateless forward computation, and the model itself has no memory capability.68% similarVerifiedState space models represented by Mamba compress historical information by maintaining a fixed-size hidden state, with both time and space complexity of O(1) during inference.67% similarUnverifiedTransformer-based LLMs perform an independent forward-pass computation for each inference, with model weights fixed during the inference phase; what is referred to as context memory is essentially the re-injection of historical conversation text into the prompt.66% similarUnverifiedIn 2023, Gu and Dao proposed Mamba, introducing an input-dependent selective scan mechanism that achieves language modeling performance comparable to or better than Transformers while maintaining linear time complexity.66% similar
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