This research introduces a hybrid language model architecture that efficiently combines on-device small language models with powerful remote LLMs to optimize performance, cost, and privacy.

• Implements uncertainty-aware speculative inference where the small model makes predictions that the large model validates
• Achieves 27-41% reduction in token generation latency while maintaining quality comparable to pure LLM outputs
• Provides an adaptive communication framework that intelligently decides when to use local vs. remote processing
• Demonstrates practical implementation for resource-constrained mobile devices with privacy benefits

This engineering breakthrough offers a practical path to deploying powerful AI capabilities on edge devices while optimizing for bandwidth, latency, and cost constraints.

Uncertainty-Aware Hybrid Inference with On-Device Small and Remote Large Language Models