arXiv:2601.22347v2 Announce Type: replace Abstract: Recent post-training quantization (PTQ) methods have adopted block rotations to diffuse outliers prior to rounding. While this reduces the overhead of online full-vector rotations, the effect of block structure on outlier suppression remains poorly understood. To fill this gap, we present the first systematic, non-asymptotic analysis of outlier suppression for block Hadamard rotations. Our analysis reveals that outlier suppression is fundamentally limited by the geometry of the input vector. In particular, in the deterministic worst case, pos
This research provides a timely theoretical foundation for understanding limitations in post-training quantization techniques, which are becoming critical for deploying large AI models efficiently.
Improving post-training quantization is crucial for reducing the computational and memory demands of AI, directly impacting the feasibility and cost-effectiveness of deploying advanced AI across various applications.
The understanding of block rotation limitations in PTQ identifies specific geometric constraints, which will guide future research and development towards more robust and efficient quantization methods.
- · AI hardware manufacturers
- · Edge AI developers
- · Companies deploying large language models
- · AI researchers
- · Inefficient AI quantization methods
- · Cloud computing providers (if edge AI becomes more prevalent)
More efficient and compact AI models will be developed, leading to broader AI adoption.
Reduced compute requirements for AI could decentralize AI deployment, impacting traditional cloud infrastructure dependencies.
Ubiquitous, low-cost AI could accelerate the development of autonomous systems and agents in diverse environments.
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Read at arXiv cs.LG