LLMEdge: A Novel Framework for Localized LLM Inferencing at Resource Constrained Edge

Large Language Models (LLMs) have revolutionized natural language processing, providing robust capabilities for understanding and generating human readable text. However, deploying these models in resource-constrained edge environments, such as Internet of Things (IoT) platforms, remains a significant challenge due to the high computational and memory requirements of LLMs. This paper introduces LLMEdge, a novel framework proposed to address these challenges by leveraging quantized LLMs, efficient localized inference frameworks, and lightweight web application servers. Our contributions include quantization-aware LLM deployment for near real-time inference on resource-constrained edge devices, reducing cloud dependency, minimizing power consumption, enhancing user experience, and improving data privacy. LLMEdge holds promise for scalable, low-latency, and privacy-focused solutions in diverse IoT applications, paving the way for more intelligent and autonomous edge systems.