AI infrastructure can face several common issues, including:
-- Scalability
-- Hardware limitations
-- Data management
-- Model deployment and monitoring
-- Security and privacy
-- Training time and resource constraints
-- Cost management
-- Integration with existing systems
Addressing these issues requires careful planning, design, and ongoing optimization of AI infrastructure to support the evolving needs of AI applications.

Creating a standalone AI Language Model System (LMS) that functions without an internet connection on a Windows PC presents several significant challenges:
-- Model Size and Resource Constraints
-- Model Optimization
-- Offline Data Access
-- Offline Training and Updates
-- User Interface and Integration
-- Security and Privacy
-- Performance Optimization
Collaboration between experts in AI research, software engineering, user experience design, and cybersecurity is essential to overcome these challenges and develop a robust and functional system.