This paper delves into the acceleration of transformer AI models using field-programmable gate arrays (FPGAs). Initially, we lay the theoretical groundwork for both transformer models and FPGAs, setting the stage for a comprehensive understanding of their synergistic potential. The study then proceeds to critically analyze existing methodologies for enhancing the speed of transformer models via FPGAs. This includes a thorough evaluation of current frameworks in this arena, featuring a comparative analysis to elucidate their efficacy and varied applications in different scenarios. Central to our discussion is an exploration of the challenges and hurdles encountered in implementing transformer models on FPGA technology. This encompasses technological limitations, spanning from hardware intricacies to software compatibility issues, which significantly influence the efficiency and scalability of these solutions. The paper also ventures into prospective research trajectories, contemplating future advancements in FPGA technology that could optimize transformer model efficiency. Moreover, we provide a succinct summary in the concluding section, offering deeper insights into the impact of FPGA-based acceleration on AI. This segment emphasizes the substantial contribution of FPGAs to the growth and widespread application of AI. Here, we unveil both the capabilities and potential constraints of employing FPGAs to enhance the performance of transformer models, suggesting innovative approaches that could signify a major leap in the field of AI acceleration.