LQA: A Lightweight Quantized-Adaptive Framework for Vision-Language Models on the Edge

• URL: http://arxiv.org/abs/2602.07849v3
• Date: Tue, 17 Feb 2026 11:38:26 GMT
• Title: LQA: A Lightweight Quantized-Adaptive Framework for Vision-Language Models on the Edge
• Authors: Xin Wang, Hong Jia, Hualin Zhou, Sheng Guang Wang, Yu Zhang, Ting Dang, Tao Gu,
• Abstract summary: We propose a lightweight, quantized-adaptive framework for Vision-Language Models (VLMs)<n>We introduce Selective Hybrid Quantization (SHQ) and a quantized, gradient-free adaptation mechanism to enable robust and efficient VLM deployment on resource-constrained hardware.<n> Experiments show that LQA improves overall adaptation performance by 4.5%, uses less memory, and significantly outperforms gradient-based TTA methods.
• Score: 12.772499009055194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying Vision-Language Models (VLMs) on edge devices is challenged by resource constraints and performance degradation under distribution shifts. While test-time adaptation (TTA) can counteract such shifts, existing methods are too resource-intensive for on-device deployment. To address this challenge, we propose LQA, a lightweight, quantized-adaptive framework for VLMs that combines a modality-aware quantization strategy with gradient-free test-time adaptation. We introduce Selective Hybrid Quantization (SHQ) and a quantized, gradient-free adaptation mechanism to enable robust and efficient VLM deployment on resource-constrained hardware. Experiments across both synthetic and real-world distribution shifts show that LQA improves overall adaptation performance by 4.5\%, uses less memory than full-precision models, and significantly outperforms gradient-based TTA methods, achieving up to 19.9$\times$ lower memory usage across seven open-source datasets. These results demonstrate that LQA offers a practical pathway for robust, privacy-preserving, and efficient VLM deployment on edge devices.

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