LLMEdge: A Novel Framework for Localized LLM Inferencing at Resource Constrained Edge
Partha Pratim Ray, Mohan P. Pradhan · 2024
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.