BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large
  Language Models on Mobile Devices

Xudong Lu; Yinghao Chen; Cheng Chen; Hui Tan; Boheng Chen; Yina Xie; Rui Hu; Guanxin Tan; Renshou Wu; Yan Hu; Yi Zeng; Lei Wu; Liuyang Bian; Zhaoxiong Wang; Long Liu; Yanzhou Yang; Han Xiao; Aojun Zhou; Yafei Wen; Xiaoxin Chen; Shuai Ren; Hongsheng Li

Paper · arXiv 2411.10640

BlueLM-V-3B: Algorithm and System Co-Design for Multimodal Large Language Models on Mobile Devices

Xudong Lu, Yinghao Chen, Cheng Chen, Hui Tan, Boheng Chen, Yina Xie, Rui Hu, Guanxin Tan, Renshou Wu, Yan Hu, Yi Zeng, Lei Wu, Liuyang Bian, Zhaoxiong Wang, Long Liu, Yanzhou Yang, Han Xiao, Aojun Zhou, Yafei Wen, Xiaoxin Chen, Shuai Ren, Hongsheng Li

47 upvotesNovember 16, 2024arXiv 预印本

AI 摘要

BlueLM-V-3B optimizes the deployment of multimodal large language models on mobile platforms through redesigned dynamic resolution and hardware-aware system optimizations, achieving high performance with a small model size and fast generation speed.

multimodal large language modelsdynamic resolutionhardware-aware deploymentOpenCompass benchmarkparameter-efficient fine-tuningtoken/s4-bit LLM weight quantization

Abstract

The emergence and growing popularity of multimodal large language models (MLLMs) have significant potential to enhance various aspects of daily life, from improving communication to facilitating learning and problem-solving. Mobile phones, as essential daily companions, represent the most effective and accessible deployment platform for MLLMs, enabling seamless integration into everyday tasks. However, deploying MLLMs on mobile phones presents challenges due to limitations in memory size and computational capability, making it difficult to achieve smooth and real-time processing without extensive optimization. In this paper, we present BlueLM-V-3B, an algorithm and system co-design approach specifically tailored for the efficient deployment of MLLMs on mobile platforms. To be specific, we redesign the dynamic resolution scheme adopted by mainstream MLLMs and implement system optimization for hardware-aware deployment to optimize model inference on mobile phones. BlueLM-V-3B boasts the following key highlights: (1) Small Size: BlueLM-V-3B features a language model with 2.7B parameters and a vision encoder with 400M parameters. (2) Fast Speed: BlueLM-V-3B achieves a generation speed of 24.4 token/s on the MediaTek Dimensity 9300 processor with 4-bit LLM weight quantization. (3) Strong Performance: BlueLM-V-3B has attained the highest average score of 66.1 on the OpenCompass benchmark among models with leq 4B parameters and surpassed a series of models with much larger parameter sizes (e.g., MiniCPM-V-2.6, InternVL2-8B).