Together AI has released Mamba-3, a new state space model (SSM) prioritizing inference efficiency over training speed. This model features a more expressive recurrence formula, complex-valued state tracking, and a multi-input, multi-output (MIMO) variant that enhances accuracy without sacrificing decoding speed. Mamba-3 SISO has demonstrated superior performance in prefill and decode latency compared to previous Mamba versions and even the Llama-3.2-1B Transformer model at the 1.5B parameter scale. The team has also open-sourced the model's kernels, developed collaboratively with researchers from Carnegie Mellon University, Princeton University, and Cartesia AI. AI
IMPACT Sets a new benchmark for inference efficiency in state space models, potentially influencing future LLM architectures and deployment strategies.
RANK_REASON New model release from a frontier AI lab (Together AI) with performance claims. [lever_c_demoted from frontier_release: ic=1 ai=1.0]
- Carnegie Mellon University
- Cartesia AI
- Llama-3.2-1B
- Mamba-2
- Mamba-3
- Princeton University
- Together AI
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