aiqbench analysis
This empirical study addresses the critical gap between theoretical efficiency and practical deployment constraints for on-device AI, specifically focusing on CPU utilization and thermal limits during real-time translation. By constructing a new benchmark and testing across heterogeneous mobile hardware, the research demonstrates that carefully selected lightweight models can match commercial performance even under strict resource budgets. These findings offer actionable insights for engineers developing edge-based inference systems where power and heat dissipation are primary bottlenecks.
An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices
arXiv:2601.02641v2 Announce Type: replace Abstract: Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU utilization and thermal conditions. In this paper, through extensive experiments, we investigate two key issues that must be addressed to deploy on-device models in real-world services: (i) the selection of on-device models and the resource consumption of each model, and (ii) the capability and potential of on-device models for domain adaptation. To this end, we focus on a task of translating live-stream chat messages and manually construct LiveChatBench, a benchmark consisting of 1,000 Korean-English parallel sentence pairs. Experiments on five mobile devices demonstrate that, although serving a large and heterogeneous user base requires careful consideration of highly constrained deployment settings and model selection, the proposed approach nevertheless achieves performance comparable to commercial models such as GPT-5.1 on the well-targeted task. We expect that our findings will provide meaningful insights to the on-device AI community.
Abstract:Despite its efficiency, there has been little research on the practical aspects required for real-world deployment of on-device AI models, such as the device's CPU utilization and thermal conditions. In this paper, through extensive experiments, we investigate two key issues that must be addressed to deploy on-device models in real-world services: (i) the selection of on-device models and the resource consumption of each model, and (ii) the capability and potential of on-device models for domain adaptation. To this end, we focus on a task of translating live-stream chat messages and manually construct LiveChatBench, a benchmark consisting of 1,000 Korean-English parallel sentence pairs. Experiments on five mobile devices demonstrate that, although serving a large and heterogeneous user base requires careful consideration of highly constrained deployment settings and model selection, the proposed approach nevertheless achieves performance comparable to commercial models such as GPT-5.1 on the well-targeted task. We expect that our findings will provide meaningful insights to the on-device AI community.
Artificial Intelligence (cs.AI)
arXiv:2601.02641 [cs.AI]
(or arXiv:2601.02641v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2601.02641 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 251 words
