arXiv:2606.18114v1 Announce Type: cross 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) -- approa
The rapid development of smaller, more efficient AI models is a direct response to the increasing demand for edge AI deployment and the current computational and energy constraints.
This research demonstrates a significant reduction in the memory footprint and training costs for powerful AI models, making advanced AI more accessible and deployable on resource-constrained hardware.
The ability to compress complex State Space Models like Mamba-2 by over 3x with minimal data further lowers the barrier to entry for developing and deploying performant AI on edge devices.
- · Edge AI hardware manufacturers
- · Developers of embedded AI applications
- · Regions with limited compute infrastructure
- · Startups developing specialized AI
- · Cloud-centric AI model providers
- · Manufacturers of solely high-end AI accelerators
Further proliferation of sophisticated AI models on local devices without significant cloud dependency.
Increased competition for cloud providers as more specialized AI tasks can be run on-device or locally.
Potential acceleration of sovereign AI capabilities as less compute is needed for advanced models, reducing reliance on global supply chains for supercomputing infrastructure.
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Read at arXiv cs.AI