Trainable Smooth-Rotation Transforms with Learned Channel Scales for LLM Quantization

Researchers propose improved post-training quantization techniques for large language models using quantile-robust scaling policies and learned channel scales, demonstrating 18.5% error reduction on LLaMA-3.2-1B under W4A4 quantization. The work addresses activation quantization challenges caused by outlier-dominated channels, offering practical efficiency improvements for LLM deployment without requiring full model retraining.

This research tackles a fundamental challenge in making large language models more efficient to deploy. Post-training quantization has emerged as a critical technique for reducing inference costs, but activation quantization—converting high-precision weights to lower-precision integers—introduces errors that degrade model performance. The paper identifies that existing SmoothRot transformation approaches may overestimate necessary scaling, causing larger quantization errors than necessary.

The proposed solution replaces fixed maximum-based statistics with adaptive quantile-based scaling and adds constrained gradient optimization of channel scales. Testing on LLaMA-3.2-1B reveals substantial improvements: 11.1% error reduction with quantile-only policies and 18.5% when combining learned scales. When applied across decoder blocks, full-layer mean error drops from 97.51 to 78.08, a 19.9% improvement.

For the AI infrastructure industry, this work has meaningful implications. LLM inference costs directly impact deployment profitability for cloud providers and smaller organizations. More effective quantization techniques enable broader accessibility to capable models while maintaining quality. The approach preserves existing transformation frameworks rather than requiring architectural changes, making adoption straightforward for implementations already using quantization pipelines.

The methodology's practical focus—using lightweight training rather than expensive full retraining—makes it particularly valuable for resource-constrained scenarios. Future research should explore how these techniques generalize across different model architectures and larger models, and whether combining this approach with other optimization techniques yields further gains. The consistency of improvements across different search strategies suggests the underlying principles are robust.

• →Quantile-based scaling policies reduce LLaMA-3.2-1B quantization error by 18.5% compared to fixed max-based approaches
• →The technique preserves existing equivalent-transform frameworks, enabling easy integration into current quantization pipelines
• →Lightweight gradient-based channel scale optimization provides consistent improvements without full model retraining
• →Full-layer error reduction of 19.9% demonstrates practical efficiency gains for deployed language models
• →Robust migration control addresses outlier-dominated channels that traditionally cause large quantization errors