I am working on a computer vision project using AI Agents at the edge for industrial safety. We need to run deep learning inference on NVIDIA Jetson modules with minimal latency. Should I focus on Quantization-Aware Training (QAT) or is post-training quantization (PTQ) sufficient for 2025 hardware? I'm specifically looking for advice on maintaining mAP while dropping to INT8 precision.
3 answers
For industrial safety, where accuracy is non-negotiable, you should definitely opt for Quantization-Aware Training (QAT). In my experience with YOLOframeworks on Jetson Orin, PTQ often leads to a significant accuracy drop in low-light environments, which is common in factory settings. QAT allows the model to "learn" the quantization errors during the fine-tuning phase, resulting in a much more robust INT8 model. Additionally, look into pruning your model before quantization; removing redundant weights can give you that extra 5-10ms latency reduction you need for true real-time safety monitoring at the edge.
Patricia, that's solid advice. Have you compared the performance of TensorRT’s built-in INT8 calibration versus custom QAT scripts for the latest Blackwell-based edge modules?
Don't overlook the importance of the data pipeline. Even with INT8, your bottleneck might be image pre-processing on the CPU rather than the GPU inference itself.
Spot on, Barbara. Moving pre-processing to the GPU using something like DALI can drastically improve the overall frames-per-second (FPS) of the entire AI Agent system.
James, for the Blackwell architecture, TensorRT has improved significantly. However, custom QAT still wins if you have a highly specialized dataset. For "standard" industrial objects, the built-in calibrator is usually enough, but if you're detecting micro-cracks or specific PPE gear, the extra effort of QAT pays off in reducing false negatives.