Jamba — Hybrid SSM-Transformer

State space models (SSMs) and transformers want different things. Transformers buy quality via attention at quadratic cost. SSMs buy linear-time inference and constant memory via a recurrence but lag quality. AI21's Jamba (March 2024) and Jamba 1.5 (August 2024) put them in the same model: 1 Transformer layer for every 7 Mamba layers, MoE on every other block, and a 256k context window that fits on a single 80GB GPU. Mamba-3 (ICLR 2026) tightens the SSM side with complex-valued state spaces and MIMO projections. This lesson reads both architectures end to end and explains why the hybrid recipe has survived three years of scaling when pure-SSM and pure-Transformer long-context attempts have not.