Computer vision on the edge: latency, cost and accuracy

Edge deployment turns vision from a cloud service into a real-time capability. But squeezing a model onto constrained hardware without wrecking accuracy is an art.

Quantization is your friend

Moving from FP32 to INT8 can cut latency and memory dramatically with minimal accuracy loss — if you calibrate carefully on representative data.

Pick the right runtime

TensorRT, ONNX Runtime and CoreML each shine on different hardware. Benchmark on your actual target device, not a spec sheet.