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This is a Literature Review study (also classified as: Quantitative Study).

October 8, 2025Open Access

A Comprehensive Evaluation on Quantization Techniques for Large Language Models

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Comprehensive review assesses model performance through quantization error mitigation and pre-quantization transformation.

Post-training quantization significantly reduces memory footprint and computational overhead in large language models.
Our experimental results show that optimized rotation and scaling yield the best performance in pre-quantization transformation.
This analysis involved a comprehensive evaluation of quantization methods to ensure fair comparisons across the techniques.
The study reveals that the optimal pre-quantization strategy for INT4 does not generalize well to the new MXFP4 data format.

Liu et al. (2025) studied this question.

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October 8, 2025Open Access

View Full Paper

Ask AI

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Authors

Comprehensive review assesses model performance through quantization error mitigation and pre-quantization transformation.

Post-training quantization significantly reduces memory footprint and computational overhead in large language models.
Our experimental results show that optimized rotation and scaling yield the best performance in pre-quantization transformation.
This analysis involved a comprehensive evaluation of quantization methods to ensure fair comparisons across the techniques.
The study reveals that the optimal pre-quantization strategy for INT4 does not generalize well to the new MXFP4 data format.

Liu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark