Beacon: Post-Training Quantization with Integrated Grid Selection

• URL: http://arxiv.org/abs/2508.20293v2
• Date: Thu, 04 Sep 2025 05:03:16 GMT
• Title: Beacon: Post-Training Quantization with Integrated Grid Selection
• Authors: Shihao Zhang, Rayan Saab,
• Abstract summary: Key challenge in per-channel post-training quantization is selecting appropriate scaling factors.<n>We propose Beacon, a simple and effective algorithm that eliminates the need for such manual tuning.<n>Beacon achieves competitive performance compared to state-of-the-art methods.
• Score: 5.886065213861507
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Quantization is a widely used compression technique for reducing the memory and computation costs of large pre-trained models. A key challenge in per-channel post-training quantization (PTQ) is selecting appropriate scaling factors to replace weight values with values from a scaled integer grid. Existing methods typically fix the scale at the outset via heuristic tuning or grid search. We propose Beacon, a simple and effective algorithm that eliminates the need for such manual tuning. Beacon performs per-channel PTQ directly using an unscaled grid and automatically determines the optimal scaling factors by exploiting the geometry of scalar quantization. It does not rely on back-propagation or large calibration sets. Despite its simplicity and tuning-free nature, Beacon achieves competitive performance compared to state-of-the-art methods, making it a practical solution for efficient model deployment.

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