Generative AI on the Edge: Architecture and Performance Evaluation

• URL: http://arxiv.org/abs/2411.17712v1
• Date: Mon, 18 Nov 2024 16:09:01 GMT
• Title: Generative AI on the Edge: Architecture and Performance Evaluation
• Authors: Zeinab Nezami, Maryam Hafeez, Karim Djemame, Syed Ali Raza Zaidi,
• Abstract summary: 6G's AI native vision of embedding advance intelligence in the network requires a systematic evaluation of Generative AI (GenAI) models on edge devices. This research investigates computationally demanding Large Language Models (LLMs) inference on a single commodity Raspberry Pi serving as an edge testbed for ORAN.
• Score: 0.3999851878220877
• License:
• Abstract: 6G's AI native vision of embedding advance intelligence in the network while bringing it closer to the user requires a systematic evaluation of Generative AI (GenAI) models on edge devices. Rapidly emerging solutions based on Open RAN (ORAN) and Network-in-a-Box strongly advocate the use of low-cost, off-the-shelf components for simpler and efficient deployment, e.g., in provisioning rural connectivity. In this context, conceptual architecture, hardware testbeds and precise performance quantification of Large Language Models (LLMs) on off-the-shelf edge devices remains largely unexplored. This research investigates computationally demanding LLM inference on a single commodity Raspberry Pi serving as an edge testbed for ORAN. We investigate various LLMs, including small, medium and large models, on a Raspberry Pi 5 Cluster using a lightweight Kubernetes distribution (K3s) with modular prompting implementation. We study its feasibility and limitations by analyzing throughput, latency, accuracy and efficiency. Our findings indicate that CPU-only deployment of lightweight models, such as Yi, Phi, and Llama3, can effectively support edge applications, achieving a generation throughput of 5 to 12 tokens per second with less than 50\% CPU and RAM usage. We conclude that GenAI on the edge offers localized inference in remote or bandwidth-constrained environments in 6G networks without reliance on cloud infrastructure.

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