An Empirical Study of On-Device Translation for Real-Time Live-Stream Chat on Mobile Devices
This study addresses the challenges of deploying real-time live chat translation models on mobile devices, particularly concerning CPU utilization, thermal management, and model selection. To this end, the authors introduce LiveChatBench—the first dedicated benchmark for this task—comprising 1,000 Korean–English parallel sentence pairs—and conduct a systematic evaluation of multiple on-device neural machine translation models across five real-world mobile devices. The analysis examines resource consumption, thermal behavior, and domain adaptation capabilities, revealing critical trade-offs among model performance, hardware constraints, and domain suitability. Notably, selected lightweight models achieve translation quality comparable to commercial systems such as GPT-5.1 under stringent on-device conditions, demonstrating a viable and efficient pathway for real-time on-device live chat translation.