I Made My AI Model 84% Smaller and It Got Better, Not Worse

A hybrid edge-cloud system is proposed to address the high costs and latency associated with deploying large AI models. The approach involves first using domain adaptation to specialize a model for a specific task, like customer support, and then applying quantization to reduce its size by 84%. This smaller, optimized model handles the majority of requests locally on edge devices, providing fast response times and keeping data local. A smart router escalates only the more complex queries to a larger, more powerful model in the cloud, drastically reducing inference costs while maintaining high overall accuracy.