Edge deployment of LLMs does not always mean running billions of parameters on a Raspberry Pi. For most production systems, the practical path is to run lightweight logic locally while offloading heavy inference to a cloud backend. This guide walks through building a hybrid edge-cloud pipeline, using Oxlo.ai as the cloud inference layer to keep costs predictable and integration simple.
Why Edge plus Cloud Inference Works
Running large models entirely on edge hardware is expensive to maintain and hard to scale. A more sustainable pattern is to use the edge for data collection, preprocessing, and fast local decisions, then forward complex generation tasks to the cloud. This minimizes hardware costs, reduces power consumption, and lets you upgrade models without flashing new firmware.
Architecture Overview
A typical stack looks like this:
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