YOLOv8 INT8 Quantization: 4x Faster Jetson Inference

The Numbers That Made Me Rethink Edge Deployment

YOLOv8n runs at 45 FPS on a Jetson Orin Nano in FP16 mode. Push it to INT8, and you're looking at 180+ FPS on the same hardware. That's not a typo—we're talking about a 4x speedup with less than 1% mAP drop on COCO validation. The catch? Getting there involves navigating a maze of calibration strategies, and the wrong choice can tank your accuracy to unusable levels.

I've been running object detection on Jetson devices for industrial inspection tasks, and the difference between 45 and 180 FPS isn't academic. It's the difference between processing one camera feed and processing four. Or running detection alongside tracking, segmentation, and your actual business logic without dropping frames.

Two Roads to INT8: Post-Training vs Quantization-Aware

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