LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers

• URL: http://arxiv.org/abs/2511.10004v2
• Date: Fri, 14 Nov 2025 02:47:11 GMT
• Title: LampQ: Towards Accurate Layer-wise Mixed Precision Quantization for Vision Transformers
• Authors: Minjun Kim, Jaeri Lee, Jongjin Kim, Jeongin Yun, Yongmo Kwon, U Kang,
• Abstract summary: Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computation demands.<n>Existing methods rely on uniform precision, ignoring the diverse sensitivity of ViT components to quantization.<n>We propose LampQ, an accurate metric-based MPQ method for ViTs to overcome these limitations.
• Score: 16.838508946926947
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: How can we accurately quantize a pre-trained Vision Transformer model? Quantization algorithms compress Vision Transformers (ViTs) into low-bit formats, reducing memory and computation demands with minimal accuracy degradation. However, existing methods rely on uniform precision, ignoring the diverse sensitivity of ViT components to quantization. Metric-based Mixed Precision Quantization (MPQ) is a promising alternative, but previous MPQ methods for ViTs suffer from three major limitations: 1) coarse granularity, 2) mismatch in metric scale across component types, and 3) quantization-unaware bit allocation. In this paper, we propose LampQ (Layer-wise Mixed Precision Quantization for Vision Transformers), an accurate metric-based MPQ method for ViTs to overcome these limitations. LampQ performs layer-wise quantization to achieve both fine-grained control and efficient acceleration, incorporating a type-aware Fisher-based metric to measure sensitivity. Then, LampQ assigns bit-widths optimally through integer linear programming and further updates them iteratively. Extensive experiments show that LampQ provides the state-of-the-art performance in quantizing ViTs pre-trained on various tasks such as image classification, object detection, and zero-shot quantization.

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