LATMiX: Learnable Affine Transformations for Microscaling Quantization of LLMs
arXiv:2602.17681v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent studies have shown that applying invertible transformations to activations can significantly improve quantization robustness by reducing activation outliers; however, existing approaches are largely restricted to rotation or Hadamard-based transformations. Moreover, most studies focused primarily on traditional quantization schemes, whereas modern hardware increasingly supports the microscaling (MX) data format. Attempts to combine both showed severe performance degradation, leading prior work to introduce assumptions on the transformations. In this work, we take a complementary perspective. First, we provide a theoretical analysis of transformations under MX quantization by deriving a bound on the quantization error. Our analysis emphasizes the importance of accounting for both the activation dis
arXiv:2602.17681v1 Announce Type: cross Abstract: Post-training quantization (PTQ) is a widely used approach for reducing the memory and compute costs of large language models (LLMs). Recent studies have shown that applying invertible transformations to activations can significantly improve quantization robustness by reducing activation outliers; however, existing approaches are largely restricted to rotation or Hadamard-based transformations. Moreover, most studies focused primarily on traditional quantization schemes, whereas modern hardware increasingly supports the microscaling (MX) data format. Attempts to combine both showed severe performance degradation, leading prior work to introduce assumptions on the transformations. In this work, we take a complementary perspective. First, we provide a theoretical analysis of transformations under MX quantization by deriving a bound on the quantization error. Our analysis emphasizes the importance of accounting for both the activation distribution and the underlying quantization structure. Building on this analysis, we propose LATMiX, a method that generalizes outlier reduction to learnable invertible affine transformations optimized using standard deep learning tools. Experiments show consistent improvements in average accuracy for MX low-bit quantization over strong baselines on a wide range of zero-shot benchmarks, across multiple model sizes.
Executive Summary
This study proposes LATMiX, a novel method for microscaling quantization of large language models (LLMs) that leverages learnable affine transformations to reduce activation outliers. The authors provide a theoretical analysis of transformations under MX quantization, deriving a bound on the quantization error that emphasizes the importance of accounting for both the activation distribution and the underlying quantization structure. Experiments demonstrate consistent improvements in average accuracy for MX low-bit quantization over strong baselines on a range of zero-shot benchmarks. The study's findings contribute to the development of more efficient and accurate LLMs, which is critical for the widespread adoption of AI in various applications.
Key Points
- ▸ LATMiX proposes learnable affine transformations for microscaling quantization of LLMs.
- ▸ The authors provide a theoretical analysis of transformations under MX quantization.
- ▸ Experiments show consistent improvements in average accuracy for MX low-bit quantization.
Merits
Strength in Theoretical Analysis
The study provides a rigorous theoretical analysis of transformations under MX quantization, which is essential for understanding the implications of different transformation types on the quantization error.
Improved Accuracy
The LATMiX method demonstrates consistent improvements in average accuracy for MX low-bit quantization over strong baselines, indicating its potential for real-world applications.
Demerits
Limited Evaluation on Traditional Quantization Schemes
The study focuses primarily on microscaling quantization and does not provide a comprehensive evaluation of LATMiX's performance on traditional quantization schemes.
Assumptions on Model Architectures
The authors assume that the model architectures used in the experiments are suitable for the LATMiX method, which may not be the case for all models and applications.
Expert Commentary
The study's contributions are significant, as it provides a novel method for microscaling quantization of LLMs that leverages learnable affine transformations. The theoretical analysis of transformations under MX quantization is rigorous and essential for understanding the implications of different transformation types on the quantization error. While the study has limitations, such as the lack of comprehensive evaluation on traditional quantization schemes and assumptions on model architectures, the findings are promising and warrant further investigation. The LATMiX method has the potential to improve the efficiency and accuracy of LLMs, which is critical for various real-world applications.
Recommendations
- ✓ Future studies should investigate the performance of LATMiX on traditional quantization schemes and explore its applicability to different model architectures.
- ✓ The LATMiX method should be further evaluated in real-world applications, such as natural language processing and machine translation, to demonstrate its practical value.
