This paper researches on the power consumption of AIGC applications based on LLM with different parameter scales across different hardware platforms. Artificial Intelligence Generated Content (AIGC) represents a leading-edge application of AI technology, primarily driven by large language models (LLMs) and their associated technologies. The deployment of LLM typically relies on critical facilities with three layers, i.e., the hardware, model, and application layers. This empirical study aims to identify key factors in power consumption when a large model is serving in the inference stage, which will hint the insights for improving the energy efficiency of computational infrastructures. In the context of the "dual carbon" goals, i.e., carbon peaking and carbon neutrality, this study aims to find an effective way to reduce the energy cost of AIGC applications, thereby supporting sustainable AI development in industry.

P2P applications suffer from free-riding. In economics terminology, free-riding is the rational behavior of the participants. So it's feasible to use game theory to analyze this problem and design countermeasures. Tit-For-Tat is a simple and efficient equilibrium strategy in repeated game environments. In this paper, we construct a game model in P2P environments and deduce the constraint under which the strict tit-for-tat is an equilibrium strategy. We then improve and adapt the tit-for-tat strategy to the dynamic property in P2P networks, and deduce the constraint under which it is still an equilibrium strategy. Finally we study through simulations the performance improvement of the enhanced tit-for-tat and give suggestion of how to choose proper system parameters under different network conditions.