AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language Model Article Swipe
Zhou Jin
,
Xiaohui Song
,
Nan Wang
,
Yafei Liu
,
Chao Li
,
Xin Li
,
Ruichen Wang
,
Zhihao Li
,
Qi Qi
,
Long Cheng
,
Dongze Hao
,
Quanlong Zheng
,
Yanhao Zhang
,
Hongmei Ji
,
Jian Ma
,
Zhitong Zheng
,
Zhenyi Lin
,
Hongxia Deng
,
Xin Zou
,
Xiaojie Yin
,
Ran Wang
,
Liang Cai
,
Haijing Liu
,
Yuqing Qiu
,
Ke Chen
,
Zheng Li
,
Chi Xie
,
Huafei Li
,
Chenxing Li
,
Chuangchuang Wang
,
Kai Tang
,
Zhiguang Zhu
,
Weijun Gao
,
Rui Wang
,
Jun Wu
,
Chao Liu
,
Qin Xie
,
Chen Chen
,
Haonan Lu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.11496
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2510.11496
In recent years, while cloud-based MLLMs such as QwenVL, InternVL, GPT-4o, Gemini, and Claude Sonnet have demonstrated outstanding performance with enormous model sizes reaching hundreds of billions of parameters, they significantly surpass the limitations in memory, power consumption, and computing capacity of edge devices such as mobile phones. This paper introduces AndesVL, a suite of mobile-side MLLMs with 0.6B to 4B parameters based on Qwen3's LLM and various visual encoders. We comprehensively outline the model architectures, training pipeline, and training data of AndesVL, which achieves first-tier performance across a wide range of open-source benchmarks, including fields such as text-rich image understanding, reasoning and math, multi-image comprehension, general VQA, hallucination mitigation, multilingual understanding, and GUI-related tasks when compared with state-of-the-art models of a similar scale. Furthermore, we introduce a 1+N LoRA architecture alongside a Quantization-Aware LoRA Fine-Tuning (QALFT) framework to facilitate efficient task adaptation and model compression during mobile-side deployment of AndesVL. Moreover, utilizing our cache eviction algorithm -- OKV -- along with customized speculative decoding and compression strategies, we achieve a 6.7x peak decoding speedup ratio, up to 30.9% memory reduction, and 1.8 bits-per-weight when deploying AndesVL-4B on MediaTek Dimensity 9500 chips. We release all models on https://huggingface.co/OPPOer.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2510.11496
- https://arxiv.org/pdf/2510.11496
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W4415272427
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4415272427Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2510.11496Digital Object Identifier
- Title
-
AndesVL Technical Report: An Efficient Mobile-side Multimodal Large Language ModelWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-10-13Full publication date if available
- Authors
-
Zhou Jin, Xiaohui Song, Nan Wang, Yafei Liu, Chao Li, Xin Li, Ruichen Wang, Zhihao Li, Qi Qi, Long Cheng, Dongze Hao, Quanlong Zheng, Yanhao Zhang, Hongmei Ji, Jian Ma, Zhitong Zheng, Zhenyi Lin, Hongxia Deng, Xin Zou, Xiaojie Yin, Ran Wang, Liang Cai, Haijing Liu, Yuqing Qiu, Ke Chen, Zheng Li, Chi Xie, Huafei Li, Chenxing Li, Chuangchuang Wang, Kai Tang, Zhiguang Zhu, Weijun Gao, Rui Wang, Jun Wu, Chao Liu, Qin Xie, Chen Chen, Haonan LuList of authors in order
- Landing page
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https://arxiv.org/abs/2510.11496Publisher landing page
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https://arxiv.org/pdf/2510.11496Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2510.11496Direct OA link when available
- Cited by
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0Total citation count in OpenAlex
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