AWhat changed
Applied equivalent offline rescaling to shift activation outliers into easier-to-quantize weights, enabling eight-bit weight and activation inference.
Scientific scope
Applied equivalent offline rescaling to shift activation outliers into easier-to-quantize weights, enabling eight-bit weight and activation inference. Year follows the 2022 preprint; ICML publication was 2023. Accuracy and acceleration are empirical for evaluated models and implementations, not a universal lossless guarantee.
Evidence
The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. Year follows the 2022 preprint; ICML publication was 2023. Accuracy and acceleration are empirical for evaluated models and implementations, not a universal lossless guarantee.
Contributors
- Source-listed authors and technical contributors · SCIENCE: Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
- SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
- MIT Han Lab: SmoothQuant implementation
Provisional WRS assessment, edition 2026-10-16.1. Independent expert calibration is pending. This grade does not certify correctness or assign ownership.
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