I spent the last three weeks running identical YOLOv8 + Llama-3-8B-Inference workloads on an NVIDIA Jetson Orin Nano 8GB and an Intel Core Ultra 7 155H (Meteor Lake NPU) inside a fanless chassis. I measured wall-plug power, tokens/sec for the LLM path, and the real per-frame latency of a vision pipeline under sustained thermal load. This guide distills those measurements, plus 2026 unit pricing, so you can pick the right silicon for your edge deployment without lighting money on fire.
Before we dig into the silicon, a quick word on inference economics. Most teams I work with run their LLM and vision backbones on a mix of edge devices and cloud APIs. If you route any traffic to a Western model provider, you can cut that bill by 85%+ by relaying through HolySheep AI. The base URL is https://api.holysheep.ai/v1, the FX rate is locked at ¥1 = $1 (vs the bank rate of ¥7.3/$1), and they accept WeChat and Alipay. Public 2026 output prices per million tokens: GPT-4.1 $8, Claude Sonnet 4.5 $15, Gemini 2.5 Flash $2.50, DeepSeek V3.2 $0.42. More on this in the ROI section.