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Tsinghua AIFudanHKUSTIEEEOPPOerSchool of Information Science and TechnologyTencent AIUSTCOct 13, 2025arXiv:2510.11496The paper introduces AndesVL, a suite of mobile-side Multimodal Large Language Models (MLLMs) ranging from 0.6B to 4B parameters, built upon Qwen3's LLM and various visual encoders, designed to address the limitations of deploying large cloud-based MLLMs on edge devices. AndesVL achieves competitive performance on diverse benchmarks, including text-rich image understanding and VQA, compared to similar-scale models. The authors also present a 1+N LoRA architecture and Quantization-Aware LoRA Fine-Tuning (QALFT) framework, along with optimizations like a cache eviction algorithm (OKV), speculative decoding, and compression, to enhance deployment efficiency on mobile devices, demonstrating significant speedups and memory reduction.
Run state-of-the-art multimodal LLMs on your phone: AndesVL achieves a 6.7x speedup and 30% memory reduction on mobile chips.
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.
