Mamba-3 is a new state space model (SSM) designed with inference efficiency as the primary goal — a departure from Mamba-2, which optimized for training speed. The key upgrades are a more expressive recurrence formula, complex-valued state tracking, and a MIMO (multi-input, multi-output) variant that boosts accuracy without slowing down decoding. The result: Mamba-3 SISO beats Mamba-2, Gated DeltaNet, and even Llama-3.2-1B (Transformer) on prefill+decode latency across all sequence lengths at th...