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
Published: 2025-12-16 03:36:41

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.