// TOPIC STREAM
Transformer architectures and attention-based models.
Following Tri Dao and Albert Gu's Mamba-2 paper, this article explains the connection between state space models and attention, the hardware implications of the SSD algorithm, and the claims that remain unverified.
This article reads the competition between state space models and Transformers as a question of division of labor rather than structural replacement. Based on the abstracts and publicly stated claims of Mamba, Griffin, Jamba, and Mamba-3, it organizes the trade-offs among long contexts, generation, and hardware efficiency.