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Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models

arXiv — cs.CL•Friday, November 7, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Quamba2: A Robust and Scalable Post-training Quantization Framework for Selective State Space Models

Quamba2 is a groundbreaking framework designed to enhance the scalability and efficiency of State Space Models (SSMs), which are gaining traction as a viable alternative to Transformers. This framework addresses the challenges of deploying SSMs on cloud platforms and resource-constrained devices by enabling low bit-width quantization. This innovation not only reduces the model size but also leverages hardware acceleration, making it a significant advancement in the field of machine learning. The implications of Quamba2 could lead to more accessible and efficient AI applications across various industries.
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