Edge AI~6 min read
Running lightweight models on the edge gateway: latency vs privacy
Move inference from the cloud to the gateway so sensor data can stay on-site for anomaly detection and simple decisions.
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Many IoT workloads are latency-sensitive: line inspection, facility monitoring, security automation. Round-tripping everything to the cloud raises bandwidth and compliance cost.
A common approach is to use quantized small models (on the order of hundreds of millions of parameters or less) on ARM/x86 gateways with TensorRT, ONNX Runtime, or llama.cpp-class stacks.
Operationally, plan for model versioning, OTA updates, fallback to rule engines on failure, and clean integration with existing MQTT/HTTP data paths.
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