Edge AI is the deployment of artificial intelligence inference models directly on edge devices — IoT sensors, cameras, smartphones, or industrial controllers — rather than sending data to a centralised cloud server. By running AI locally, organisations achieve real-time responses, offline operation, reduced bandwidth costs, and stronger data privacy.
Edge AI separates the two phases of machine learning — training and inference — and moves inference to the device. Training still happens in the cloud or on-premises with GPUs, where compute is abundant. The trained model is then compressed and optimised for the target hardware and deployed to edge devices. When the device encounters new data — a camera frame, a sensor reading, an audio clip — inference runs locally and returns a result in milliseconds, without any network round trip.
1
Train in Cloud
Full model trained on GPU infrastructure
2
Compress & Quantise
Reduce model size for device constraints
3
Deploy to Device
Push model to edge hardware via OTA update
4
Infer Locally
Real-time predictions without cloud dependency
Model compression is critical for edge deployment. Full-precision neural networks trained in the cloud are typically too large and too compute-hungry for edge hardware. The main compression techniques are:
Most production deployments use a hybrid architecture: lightweight models at the edge handle real-time classification and anomaly detection, while complex analysis — root cause determination, pattern learning across all devices, report generation — runs in the cloud on aggregated data. AINinza designs these hybrid architectures to put inference where it delivers the most value for each use case.
Computer vision models deployed on production line cameras inspect every unit in real time — detecting surface defects, dimensional deviations, and assembly errors at line speed. Cloud-based inspection cannot keep up with production cadence; edge inference runs at 30–60 frames per second, enabling 100% inspection rates that replace statistical sampling.
Shelf monitoring cameras with on-device models detect out-of-stock conditions, planogram compliance violations, and queue lengths in real time. Processing happens locally on the camera hardware — no PII is transmitted to a cloud, and store operations staff receive alerts within seconds of a stock-out occurring.
Wearable cardiac monitors run arrhythmia detection models on-device, alerting patients and clinicians to dangerous rhythm events in real time without requiring a smartphone connection. Edge AI enables continuous monitoring at home rather than periodic hospital-based tests — dramatically expanding the window of clinical observation.
<10ms
Typical Edge Inference Latency
4–8x
Model Size Reduction via Quantisation
Common questions about what is edge ai?.