Density Field State Space Models: 1-Bit Distillation, Efficient Inference, and Knowledge Organization in Mamba-2
arXiv:2606.10932v1 Announce Type: new Abstract: We present Density Field State Space Models (DF-SSM), a framework for compressing SSMs to a 1-bit scaffold with int8 low-rank correction. Applied to Mamba-2 1.3B, we achieve a 278 MB model (9.7x smaller than the 2.7 GB FP16 teacher) that runs at 21.4x faster inference on GPU (batch=1, relative to the mamba-ssm reference implementation) while maintaining downstream task performance within 2-4 percentage points of BitMamba-2, a 1.58-bit model trained from scratch on 150B tokens. The distillation itself requires only 32M tokens and 6 hours on a sing
The continuous push for more efficient and smaller AI models is critical for deploying advanced AI on a wider range of edge devices and constrained environments.
This breakthrough significantly reduces the computational and memory footprint of large language models, enabling faster and cheaper inference, which democratizes access and expands deployment possibilities.
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