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arXiv · 2511.06719

MobileLLM-Pro Technical Report

Patrick Huber·Ernie Chang·Wei Wen·Igor Fedorov·Tarek Elgamal·Hanxian Huang·Naveen Suda·Chinnadhurai Sankar·Vish Vogeti·Yanghan Wang·Alex Gladkov·Kai Sheng Tai·Abdelrahman Elogeel·Tarek Hefny·Vikas Chandra·Ahmed Aly·Anuj Kumar·Raghuraman Krishnamoorthi·Adithya Sagar
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Abstract

Efficient on-device language models around 1 billion parameters are essential for powering low-latency AI applications on mobile and wearable devices. However, achieving strong performance in this model class, while supporting long context windows and practical deployment remains a significant challenge. We introduce MobileLLM-Pro, a 1-billion-parameter language model optimized for on-device deployment. MobileLLM-Pro achieves state-of-the-art results across 11 standard benchmarks, significantly outperforming both Gemma 3-1B and Llama 3.2-1B, while supporting context windows of up to 128,000 tokens and showing only minor performance regressions at 4-bit quantization. These improvements are enabled by four core innovations: (1) implicit positional distillation, a novel technique that effectively instills long-context capabilities through knowledge distillation; (2) a specialist model merging framework that fuses multiple domain experts into a compact model without parameter growth; (3) simulation-driven data mixing using utility estimation; and (4) 4-bit quantization-aware training with self-distillation. We release our model weights and code to support future research in efficient on-device language models.

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Patrick Huber, Ernie Chang, Wei Wen, Igor Fedorov, Tarek Elgamal, Hanxian Huang, Naveen Suda, Chinnadhurai Sankar, Vish Vogeti, Yanghan Wang, Alex Gladkov, Kai Sheng Tai, Abdelrahman Elogeel, Tarek Hefny, Vikas Chandra, Ahmed Aly, Anuj Kumar, Raghuraman Krishnamoorthi, Adithya Sagar. 2025-11-10. MobileLLM-Pro Technical Report. https://arxiv.org/abs/2511.06719

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