Unverified50% confidenceFactExact time
Mamba2 maintains linear time complexity and constant state size, and therefore has a theoretical advantage in long-sequence scenarios.
1
Sources
50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
Sources
Mamba2-8B Compression Research: 27MB Cache and 3.14GB Model
redditr/deeplearning10/16/2026
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VerifiedMamba 等基于状态空间模型（SSM）的架构以线性复杂度实现序列建模，更适合实时推理79% 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.77% similarUnverifiedLinear-complexity models such as Mamba serve as a new architectural reference point targeting the O(n²) bottleneck75% similarUnverifiedMamba's memory footprint during inference remains constant and does not grow linearly with sequence length, narrowing the gap with Transformers on language modeling tasks.74% similarUnverifiedDuring inference, Mamba can run in pure recurrent mode with constant memory usage.74% similar
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