arXiv:2607.07964v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a widely adopted technique for compressing large language models (LLMs) without retraining. Existing second-order PTQ methods, including GPTQ, construct quantization objectives exclusively from input activation statistics, effectively assuming that all output channels contribute equally to the layer-wise reconstruction objective. We propose KronQ, a PTQ framework that challenges this assumption by introducing the gradient covariance into the quantization pipeline. Under the Kronecker-factored Hessian approximat
The continuous development in LLM technology necessitates more efficient deployment methods, making quantization a critical area of research as models grow larger.
This development could significantly reduce the computational and memory footprint of large language models, broadening their practical application across various devices and use cases.
Existing quantization methods are improved upon by incorporating gradient covariance, leading to more accurate and efficient compression of LLMs.
- · AI developers
- · Edge AI hardware manufacturers
- · Cloud computing providers
- · Companies deploying LLMs
- · None
More powerful LLMs can be deployed in resource-constrained environments, such as mobile devices or embedded systems.
The cost of running and deploying advanced AI models decreases, potentially increasing accessibility and fostering innovation in new applications.
Democratization of advanced AI capabilities could accelerate the development of autonomous AI systems, impacting white-collar workflows significantly.
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Read at arXiv cs.LG