SigmaScale: LLM Compression with SVD-based Low-Rank Decomposition and Learned Scaling Matrices
arXiv:2606.07098v1 Announce Type: cross Abstract: We present SigmaScale, a method for learning auxiliary scaling matrices $S$ to aid truncated Singular Value Decomposition (SVD) based Large Language Model (LLM) compression. Instead of deriving scaling matrices analytically, SigmaScale optimizes two sets of vectors that define diagonal row and column scaling transformations under an activation-aware compression loss. We show that learned scaling lowers the effective intrinsic rank of weight matrices, as reflected by reductions in effective-rank entropy, and that this reduction is strongly corre
This development emerges as the computational demands for Large Language Models continue to escalate, making efficient compression techniques critical for broader adoption and deployment.
Efficient LLM compression is essential for reducing memory footprint and accelerating inference, making advanced AI models more accessible and cost-effective across various applications.
The ability to significantly compress LLMs while maintaining performance will lead to wider deployment opportunities and potentially lower barriers to entry for model development and use.
- · AI model developers
- · Cloud computing providers
- · Edge AI hardware manufacturers
- · Companies deploying LLMs
- · Inefficient LLM architectures
Reduced operational costs for running large language models.
Democratization of advanced AI capabilities due to lower resource requirements.
Acceleration of research and development in AI, as more models can be iterated and deployed faster.
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Read at arXiv cs.LG