Mamba
Model ● Covered in 4 stories + Follow
Mamba is a State Space Model architecture that replaces the attention mechanism in Transformers with a control-theory-inspired SSM, achieving linear scaling for inference instead of the quadratic complexity of standard transformers. Recent implementations include Mistral's Codestral Mamba, a 7.3B-parameter code model with 256K token context windows, while kernel optimization work has demonstrated significant speed improvements on Apple Silicon and other hardware platforms. The architecture has gained attention as part of a broader emergence of transformer alternatives, with demonstrated capabilities including 5x faster inference speeds and support for sequence lengths up to 1 million tokens.
Updated 7 August 2026
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Latest developments
July 2026
June 2026
July 2024
Mistral AI releases Codestral Mamba and NeMo models Model release