| Abstract |
The growing demand for data-driven applications requires AI solutions that are both efficient and scalable to support big data analytics across diverse industries. Large Language Models (LLMs), such as GPT-4, offer advanced performance but incur high computational costs, latency, and energy demands, making them less suitable for real-time analytics or edge computing. Small Language Models (SLMs), like LLaMA 2 and Mistral 7B, have been developed to address these challenges by reducing cost and power consumption while retaining high task-specific performance. This paper explores how SLMs integrate within hybrid AI architectures, handling sub-tasks like pre-processing and localized inference, while LLMs perform complex analytics in cloud environments. Such hybrid architectures present scalable, efficient solutions for sectors including finance, healthcare, and smart cities. The challenges of SLM integration, such as reduced contextual understanding and accuracy tradeoffs, are also addressed. Further, we propose future directions to mitigate these issues, including AutoML frameworks, federated learning, and quantization techniques to enhance model efficiency. This paper discusses deployment strategies for SLMs within hybrid architectures to achieve balanced performance, scalability, and efficiency in big data applications. © 2024 IEEE. |
