Author: Ashutos Jain , Arjun Mehta , Piyush Chaudhar
Abstract: The integration of Large Language Models (LLMs) into mobile applications represents a significant shift in the capabilities of intelligent applications. iOS applications, in particular, benefit from the combination of robust hardware, efficient software frameworks, and privacy-focused APIs. This paper reviews the state-of-the-art methodologies for embedding LLMs in iOS apps, including both on-device inference and cloud-based model access. We analyze architectural strategies, performance optimization, privacy considerations, and real-world use cases. Challenges such as computational resource limitations, energy consumption, and latency are discussed, and practical solutions for developers are proposed. Finally, this paper presents future trends and recommendations for maximizing LLM efficiency in iOS applications.
Keywords: iOS applications, Large Language Models, on-device inference, cloud AI integration, performance optimization, privacy, Swift, Core ML
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