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arXiv · 2606.18114

Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models

Ramprasath Ganesaraja·Sahil Dilip Panse·Swathika N
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Abstract

State Space Models (SSMs) such as Mamba-2 offer linear-time inference but their memory footprint limits edge deployment. Prior ternary SSM work (Slender-Mamba) trains from scratch on 150B tokens; we show a pretrained checkpoint suffices, reducing the marginal token budget by 1,000x. Using grouped quantization-aware training (QAT) with knowledge distillation from a frozen FP16 teacher, we compress Mamba-2 1.3B to 3.61x (2,687 to 744 MB) and achieve 48.1% zero-shot accuracy (7-task average) in just 102M tokens (4 GPU-hours, single H100) -- approaching Bi-Mamba's 48.4% (within +/-0.9pp CI). This QAT-from-pretrained setting reveals zero-ratio collapse, a novel instability caused by learnable quantization scales that does not arise in from-scratch training. We further show that post-hoc correction strategies effective for Transformers fail for SSMs due to error accumulation through the recurrence. These results demonstrate that ternary SSMs do not require expensive from-scratch training: QAT from pretrained checkpoints with KD is a data-efficient alternative.

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Ramprasath Ganesaraja, Sahil Dilip Panse, Swathika N. 2026-06-16. Ternary Mamba: Grouped Quantization-Aware Training of W1.58A16 State Space Models. https://arxiv.org/abs/2606.18114

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