Large Multimodal Model (LMM) GPT-4V(ision) endows GPT-4 with visual groundingcapabilities, making it possible to handle certain tasks through the VisualQuestion Answering (VQA) paradigm. This paper explores the potential ofVQA-oriented GPT-4V in the recently popular visual Anomaly Detection (AD) andis the first to conduct qualitative and quantitative evaluations on the popularMVTec AD and VisA datasets. Considering that this task requires bothimage-/pixel-level evaluations, the proposed GPT-4V-AD framework contains threecomponents: 1) Granular Region Division, 2) Prompt Designing, 3)Text2Segmentation for easy quantitative evaluation, and have made somedifferent attempts for comparative analysis. The results show that GPT-4V canachieve certain results in the zero-shot AD task through a VQA paradigm, suchas achieving image-level 77.1/88.0 and pixel-level 68.0/76.6 AU-ROCs on MVTecAD and VisA datasets, respectively. However, its performance still has acertain gap compared to the state-of-the-art zero-shot method, e.g., WinCLIPann CLIP-AD, and further research is needed. This study provides a baselinereference for the research of VQA-oriented LMM in the zero-shot AD task, and wealso post several possible future works. Code is available at\url{https://github.com/zhangzjn/GPT-4V-AD}.