LoXR: Performance Evaluation of Locally Executing LLMs on XR Devices

The deployment of large language models (LLMs) on extended reality (XR) devices has great potential to advance the field of human-AI interaction. In the case of direct, on-device model inference, selecting the appropriate model and device for specific tasks remains challenging. In this paper, we deploy 17 LLMs across four XR devices--Magic Leap 2, Meta Quest 3, Vivo X100s Pro, and Apple Vision Pro, and conduct a comprehensive evaluation. We devise an experimental setup and evaluate performance on four key metrics: performance consistency, processing speed, memory usage, and battery consumption. For each of the 68 model-device pairs, we assess performance under varying string lengths, batch sizes, and thread counts, analyzing the trade-offs for real-time XR applications. We finally propose a unified evaluation method based on the Pareto Optimality theory to select the optimal device-model pairs from the quality and speed objectives. We believe our findings offer valuable insights to guide future optimization efforts for LLM deployment on XR devices. Our evaluation method can be followed as standard groundwork for further research and development in this emerging field. All supplemental materials are available atthis http URL.

@article{khan2025_2502.15761,
  title={ LoXR: Performance Evaluation of Locally Executing LLMs on XR Devices },
  author={ Dawar Khan and Xinyu Liu and Omar Mena and Donggang Jia and Alexandre Kouyoumdjian and Ivan Viola },
  journal={arXiv preprint arXiv:2502.15761},
  year={ 2025 }
}