arXiv:2601.21626v2 Announce Type: replace-cross Abstract: Post Training Quantization (PTQ), a mainstream model compression technique, often leads to the paradoxical 'low error, high loss' phenomenon because it focuses solely on minimizing quantization error. The root cause lies in the Hessian matrix of the LLM loss landscape: a few high curvature directions are extremely sensitive to perturbations. To address this, we propose the Hessian Robust Quantization (HeRo Q) algorithm, which applies a lightweight, learnable rotation-compression matrix to the weight space prior to quantization. This joi
The increasing scale of LLMs and the need for efficient deployment on edge devices and in cost-sensitive environments are driving intense research into effective quantization techniques.
Improving the efficiency of large language models through stable low-bit quantization can significantly reduce computational costs and energy consumption while maintaining performance, broadening their application.
This research introduces a novel approach to tackle the 'low error, high loss' paradox in Post Training Quantization, potentially enabling more stable and reliable deployment of highly compressed LLMs.
- · AI developers
- · Edge AI hardware manufacturers
- · Cloud computing providers
- · Consumers of AI-powered services
- · Developers relying solely on high-precision models
- · Less efficient quantization methods
More widespread and cost-effective deployment of advanced AI models across various sectors.
Reduced barriers to entry for AI model development and deployment, fostering innovation outside major tech hubs.
Increased adoption of AI in areas previously constrained by compute and energy budgets, potentially accelerating general AI development.
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Read at arXiv cs.AI