HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices
HyperAI Team, Yuchen Liu, Kaiyang Han, Zhiqiang Xia, Yuhang Dong, Chen Song, Kangyu Tang, Jiaming Xu, Xiushi Feng, WenXuan Yu, Li Peng, Mingyang Wang, Kai Wang, Changpeng Yang, Yang Li, Haoyu Lu, Hao Wang, Bingna Xu, Guangyao Liu, Long Huang, Kaibin Guo, Jinyang Wu
2025-12-18
Summary
This paper introduces HyperVL, a new multimodal AI model designed to work efficiently on devices like smartphones, combining image and text understanding.
What's the problem?
Current powerful AI models that can understand both images and text require a lot of computing power and memory, making them difficult to use directly on phones or other small devices. A key part of the problem is the Vision Transformer, which is slow and uses a lot of memory when processing detailed images.
What's the solution?
The researchers developed HyperVL, which tackles this issue in two main ways. First, it breaks down images into smaller tiles to manage memory usage. Second, it uses a 'Visual Resolution Compressor' to intelligently decide how much detail is needed in an image, avoiding unnecessary calculations. Finally, it uses 'Dual Consistency Learning' to seamlessly switch between different levels of image detail, all while working with a large language model.
Why it matters?
This work is important because it allows complex AI tasks involving images and text to be performed directly on your phone without needing a constant internet connection to a powerful server. This means faster response times, increased privacy, and the ability to use these AI features even without Wi-Fi or cellular data, opening up possibilities for more accessible and practical AI applications.
Abstract
Current multimodal large lanauge models possess strong perceptual and reasoning capabilities, however high computational and memory requirements make them difficult to deploy directly on on-device environments. While small-parameter models are progressively endowed with strong general capabilities, standard Vision Transformer (ViT) encoders remain a critical bottleneck, suffering from excessive latency and memory consumption when processing high-resolution inputs.To address these challenges, we introduce HyperVL, an efficient multimodal large language model tailored for on-device inference. HyperVL adopts an image-tiling strategy to cap peak memory usage and incorporates two novel techniques: (1) a Visual Resolution Compressor (VRC) that adaptively predicts optimal encoding resolutions to eliminate redundant computation, and (2) Dual Consistency Learning (DCL), which aligns multi-scale ViT encoders within a unified framework, enabling dynamic switching between visual branches under a shared LLM. Extensive experiments demonstrate that HyperVL achieves state-of-the-art performance among models of comparable size across multiple benchmarks. Furthermore, it significantly significantly reduces latency and power consumption on real mobile devices, demonstrating its practicality for on-device multimodal inference.