Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream tasks can be accomplished with only few-shot or even zero-shot learning and exhibit state-of-the-art performances. As widely envisioned, the big AI models are to rapidly penetrate into major intelligent services and applications, and are able to run at low unit cost and high flexibility. In 6G wireless networks, to fully enable intelligent communication, sensing and computing, apart from providing other intelligent wireless services and applications, it is of vital importance to design and deploy certain wireless BAIMs (wBAIMs). However, there still lacks investigation on architecture design and system evaluation for wBAIM. In this paper, we provide a comprehensive discussion as well as some in-depth prospects on the demand, design and deployment aspects of the wBAIM. We opine that wBAIM will be a recipe of the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions. Then, we provide the core characteristics, principles, and pilot studies to guide the design of wBAIMs, and discuss the key aspects of developing wBAIMs through identifying the differences between the existing BAIMs and the emerging wBAIMs. Finally, related research directions and potential solutions are outlined.

翻译：近期，以ChatGPT为代表的大规模人工智能模型（BAIM）引发了革命性变革。通过特定领域的预训练大模型，仅需少量样本甚至零样本学习即可完成众多下游任务，且展现出最先进的性能表现。普遍认为，大模型将快速渗透至主要智能服务与应用领域，并能够以低单位成本和高灵活性运行。在6G无线网络中，为实现智能通信、感知与计算的全面赋能，除提供其他智能无线服务与应用外，设计与部署面向无线场景的大模型（wBAIM）至关重要。然而，当前关于wBAIM的架构设计与系统评估仍缺乏系统性研究。本文围绕wBAIM的需求、设计及部署维度展开全面探讨与深度展望。我们认为，wBAIM将成为6G无线网络构建高效、可持续、多功能、可扩展的无线智能体系的关键方案，服务于众多前瞻性愿景。随后，我们提出指导wBAIM设计的核心特征、原则及试点研究案例，并通过识别现有BAIM与新兴wBAIM的差异，系统阐述wBAIM开发的关键要素。最后，概述了相关研究方向与潜在解决方案。