This survey explores the integration of edge intelligence (EI) in animation design, particularly with large models, highlighting current state-of-the-art and future prospects. Edge intelligence, characterized by decentralized processing and real-time data analysis, offers a transformative approach to handling the computational and data-intensive demands of modern animation. The paper examines the evolution, current trends, and challenges of large models in animation, as well as the integration of EI with these models. It discusses the technical aspects of edge-assisted animation design, including data management, real-time rendering, and interactive design. The survey also addresses the challenges and solutions in integrating EI with large models, proposing future research directions. Key technologies underpinning EI include distributed data processing, machine learning, and real-time analytics. The paper emphasizes the importance of EI in reducing latency, improving data processing efficiency, and enabling real-time decision-making in animation. However, the application of large models in animation also presents challenges such as resource intensity, complexity, data dependency, and ethical concerns. The survey concludes that while EI and large models offer significant potential for revolutionizing animation design, further research is needed to address the challenges and optimize their integration. The paper also highlights the importance of scalability, efficiency, and compatibility in deploying large models in animation, and suggests future work focused on enhancing the synergy between EI and animation design.This survey explores the integration of edge intelligence (EI) in animation design, particularly with large models, highlighting current state-of-the-art and future prospects. Edge intelligence, characterized by decentralized processing and real-time data analysis, offers a transformative approach to handling the computational and data-intensive demands of modern animation. The paper examines the evolution, current trends, and challenges of large models in animation, as well as the integration of EI with these models. It discusses the technical aspects of edge-assisted animation design, including data management, real-time rendering, and interactive design. The survey also addresses the challenges and solutions in integrating EI with large models, proposing future research directions. Key technologies underpinning EI include distributed data processing, machine learning, and real-time analytics. The paper emphasizes the importance of EI in reducing latency, improving data processing efficiency, and enabling real-time decision-making in animation. However, the application of large models in animation also presents challenges such as resource intensity, complexity, data dependency, and ethical concerns. The survey concludes that while EI and large models offer significant potential for revolutionizing animation design, further research is needed to address the challenges and optimize their integration. The paper also highlights the importance of scalability, efficiency, and compatibility in deploying large models in animation, and suggests future work focused on enhancing the synergy between EI and animation design.