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Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation
Learn how innovative distillation slashes latency and boosts scalability
Introduction
In today’s world of high-resolution data and long-form reasoning, even the most advanced Transformer models are hitting a hard computational wall. Despite their revolutionary impact on language tasks, these models struggle under the weight of quadratic complexity — making them impractical for processing extended sequences or high-resolution images and videos.
Multimodal Mamba (mmMamba) tackles this challenge head-on by reengineering the very foundation of multimodal AI. Using a meticulously crafted three-stage progressive distillation process, mmMamba transforms a heavyweight Transformer into a streamlined, linear-complexity state space model based on Mamba-2. By directly transferring key parameters (like W_Q, W_K, W_V, W_O ) and precisely tuning newly introduced SSM components, mmMamba achieves dramatic improvements in efficiency — delivering up to a 20.6× speedup and significantly reduced memory usage, all without compromising the model’s robust multimodal reasoning capabilities. This breakthrough not only overcomes the computational bottlenecks of traditional models but also sets a new standard for scalable, real-world AI applications.
