<p>Artificial intelligence (AI) server systems, including AI servers and AI server clusters, are widely utilized in AI applications. The performance of an AI server system determines the performance of the performance an AI application, which has garnered significant attentions and investments from industries and users. However, performance is influenced not only by the AI computing accelerating chips but also by the other configurations of an AI server system, such as architecture, memory, bus, central processing unit (CPU), interconnect equipment, software, etc. As these components are often provided by different vendors, the myriad combinations thereof present challenges in assessing performance in an architecture-neural, reproducible, fairness-enhanced, and performance bottleneck identification-oriented manner. In response to this need, this paper introduces AISBench, a performance benchmark for AI server systems. AISBench comprises standardized rules and a test toolkit that has been agreed upon by over 20 AI server system and server component manufacturers. Compared to other AI performance benchmarks, AISBench provides a more comprehensive metrics system that enables performance benchmarking as well as identification of performance bottlenecks for optimization. Experimental data indicate that our benchmark testing approach offers sufficient comprehensiveness, effectiveness, and stability.</p>