Using LLM for Edge AI: A Step-by-Step Guide

Running a 70 billion parameter model on a Raspberry Pi or an NXP i.MX8 is still impractical for most teams. The realistic path to LLM-powered edge AI is a hybrid architecture: run quantized small language models locally for fast filtering, and offload complex reasoning to a cloud inference backend. This guide shows how to wire that pipeline together, and why Oxlo.ai is a strong fit for the cloud layer.

Define the Edge-to-Cloud Split

Start by mapping your workload. Tasks that demand low latency and handle sensitive data should stay on the device. Think