Construction and application of a quantitative efficiency assessment model for green AI in intelligent customer service systems - MA Xiaoliang; LIU Ying; ZHAO Ruqiang; YANG Bangxing; GAO Jie; DENG Congjian (Dianxin kexue)
The rise of AI and the increasing prominence of LLM are positioned as core technologies for knowledge dissemination and multi-turn dialogues. Alongside their growth, the high energy consumption associated with data processing, model training, and deployment in AI large models is necessitating effective evaluation to facilitate quantitative comparisons before and after model optimization. An assessment method for the energy consumption of AI large models was introduced, aimed at quantitatively evaluating the service efficiency (E) of AI models. This model was incorporated with multiple dimensions such as training convergence time (T), model parameter size (P), and floating-point operations (F), and quantitative analysis was achieved through the construction of an energy consumption function C(T, P, F). Furthermore, by employing the nonlinear least squares method, model parameters were derived. This analysis method was not only applicable to the operational efficiency analysis of AI models used by telecommunications operators but can also be generalized for energy consumption assessment of AI models across various industries.