Jamba2 is a hybrid architecture model blending Mamba state space layers with Transformer attention, giving it an unusual efficiency profile — it handles long contexts without the memory costs typical of pure-attention models. It tends to be faster and leaner at inference than comparably-sized transformers, though its hybrid nature can mean less predictable behavior on tasks where standard transformers have been heavily optimized.