ONNX INT8 vs FP16: 3x Latency Drop on Jetson Orin Nano

#onnx #jetson #int8 #modelquantization

Switching from FP16 to INT8 cut our object detection pipeline from 47ms to 15ms per frame on the Jetson Orin Nano

That's the kind of speedup that transforms a barely-real-time demo into a production-ready edge AI system. But here's the catch: the accuracy drop wasn't uniform across model architectures. ResNet-based models handled quantization gracefully ($<$2% mAP loss), while MobileNet variants occasionally spiked false positives by 14% on small objects.

I ran this benchmark because most ONNX quantization guides stop at "it's faster" without showing you where it breaks. If you're shipping inference on Jetson devices, you need to know the exact tradeoff curve — not just average numbers.

Macro shot of a computer part showcasing intricate electronic connections. — Photo by Sergei Starostin on Pexels

The Hardware Baseline: Why Jetson Orin Nano INT8 Performance Matters

The Jetson Orin Nano packs 1024 CUDA cores and 32 Tensor Cores into a 15W power envelope. NVIDIA markets it as an "AI at the edge" platform, but the real question is: which precision mode actually delivers?

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