A Measurement Study of LLM Inference Trade-offs Across Edge Continuum Hardware

2026-09-08

AI Summary

研究通过在边缘和近边缘设备上部署大语言模型，评估其在质量、延迟、模型大小和能耗之间的权衡，使用了NVIDIA Jetson AGX Orin和近边缘服务器进行对比测试。

Abstract

Large language models (LLMs) are increasingly used as backends for intelligent web services, but serving them across the edge continuum requires balancing quality, latency, model footprint, and energy. This paper presents a controlled measurement study of self-hosted LLM inference across edge and near-edge deployment nodes: an NVIDIA Jetson AGX Orin and a near-edge server with CPU-only and GPU-enabled inference modes. We evaluate multiple open-weight LLMs and quantization variants using a fixed question-answering workload, and compare them against GPT-4o as a cloud-hosted accuracy and latency reference. Our benchmarking pipeline reports accuracy, model footprint, per-token decoding latency, prefill latency, and overall execution energy. The results show that GPU-enabled server execution provides the lowest compute-side latency, while Jetson Orin shows lower measured energy, consistent with its lower platform power under our setup. CPU-only execution is consistently dominated in latency for our workload and shows higher measured energy. We also show that parameter count and downloaded weight-file size alone do not reliably predict observed accuracy or latency. Finally, using Pareto-frontier analysis, we study how deployment decisions may change under possible streamed-token delivery overheads, highlighting that compute-side inference metrics alone can lead to suboptimal placement for latency-sensitive interactive web services.

Problem

• large language models
• edge continuum
• inference trade-offs
• latency
• energy

Innovation

• edge continuum
• LLM inference
• Pareto-frontier analysis
• energy efficiency
• latency