Structured state space models (SSMs) and attention mechanisms have evolved largely as separate sequence modeling paradigms. This work introduces Structured State Space Duality (SSD), establishing theoretical connections between structured SSMs and attention variants through semiseparable matrices and dual contraction representations. Exploiting this duality, the authors develop a block-decomposed matrix multiplication algorithm that computes 1-semiseparable selective SSMs utilizing matrix multiplication units on modern accelerators. Incorporating SSD into a refined parallel neural network block yields Mamba-2, which achieves substantial compute throughput improvements over Mamba-1 while matching or outperforming Transformers on autoregressive language modeling benchmarks.