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arXiv · 2601.09451

Late Breaking Results: Quamba-SE: Soft-edge Quantizer for Activations in State Space Models

Yizhi Chen·Ahmed Hemani
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Abstract

We propose Quamba-SE, a soft-edge quantizer for State Space Model (SSM) activation quantization. Unlike existing methods, using standard INT8 operation, Quamba-SE employs three adaptive scales: high-precision for small values, standard scale for normal values, and low-precision for outliers. This preserves outlier information instead of hard clipping, while maintaining precision for other values. We evaluate on Mamba- 130M across 6 zero-shot benchmarks. Results show that Quamba- SE consistently outperforms Quamba, achieving up to +2.68% on individual benchmarks and up to +0.83% improvement in the average accuracy of 6 datasets.

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Yizhi Chen, Ahmed Hemani. 2026-01-14. Late Breaking Results: Quamba-SE: Soft-edge Quantizer for Activations in State Space Models. https://arxiv.org/abs/2601.09451

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