Multimodal Mamba: Decoder-only Multimodal State Space Model via Quadratic to Linear Distillation

TL;DR

This paper introduces mmMamba, a novel framework for converting large multimodal language models into efficient, linear-complexity architectures through progressive distillation, enabling faster inference and reduced memory usage while maintaining strong multimodal capabilities.

Contribution

It presents a new distillation method to transform trained decoder-only MLLMs into linear-complexity models without pre-trained RNNs or vision encoders, supporting hybrid architectures.

Findings

mmMamba-linear achieves 20.6× speedup and 75.8% GPU memory reduction.

mmMamba-hybrid significantly improves performance, nearing original model capabilities.

The approach enables efficient multimodal models with competitive performance.

Abstract

Recent Multimodal Large Language Models (MLLMs) have achieved remarkable performance but face deployment challenges due to their quadratic computational complexity, growing Key-Value cache requirements, and reliance on separate vision encoders. We propose mmMamba, a framework for developing linear-complexity native multimodal state space models through progressive distillation from existing MLLMs using moderate academic computational resources. Our approach enables the direct conversion of trained decoder-only MLLMs to linear-complexity architectures without requiring pre-trained RNN-based LLM or vision encoders. We propose an seeding strategy to carve Mamba from trained Transformer and a three-stage distillation recipe, which can effectively transfer the knowledge from Transformer to Mamba while preserving multimodal capabilities. Our method also supports flexible hybrid architectures that combine Transformer and Mamba layers for customizable efficiency-performance trade-offs. Distilled from the Transformer-based decoder-only HoVLE, mmMamba-linear achieves competitive performance against existing linear and quadratic-complexity VLMs, while mmMamba-hybrid further improves performance significantly, approaching HoVLE's capabilities. At 103K tokens, mmMamba-linear demonstrates 20.6$\times$ speedup and 75.8% GPU memory reduction compared to HoVLE, while mmMamba-hybrid achieves 13.5$\times$ speedup and 60.2% memory savings. Code and models are released at https://github.com/hustvl/mmMamba