HyperVL: An Efficient and Dynamic Multimodal Large Language Model for Edge Devices
Authors: name: HyperAI Team, name: Yuchen Liu, name: Kaiyang Han, name: Zhiqiang Xia, name: Yuhang Dong, name: Chen Song, name: Kangyu Tang, name: Jiaming Xu, name: Xiushi Feng, name: WenXuan Yu, name: Li Peng, name: Mingyang Wang, name: Kai Wang, name: Changpeng Yang, name: Yang Li, name: Haoyu Lu, name: Hao Wang, name: Bingna Xu, name: Guangyao Liu, name: Long Huang, name: Kaibin Guo, name: Jinyang Wu, name: Dan Wu, name: Hongzhen Wang, name: Peng Zhou, name: Shuai Nie, name: Shande Wang, name: Runyu Shi, name: Ying Huang

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.