What are the best strategies for Quantization-Aware Training to deploy models on Edge devices?

All answers to this question.

If you are dealing with safety-critical applications, QAT is absolutely worth the extra compute time. PTQ often suffers from "Weight Clipping" issues that can cause catastrophic failures in edge cases. With QAT, the model learns to compensate for the rounding errors during the forward pass by using "Fake Quantization" nodes. This usually recovers about 2-3% of the accuracy lost during a standard 8-bit conversion. Tools like NVIDIA TensorRT or Google’s AI Edge provide excellent wrappers for this, but you need to ensure your loss function remains stable during the final fine-tuning epochs.

   Answered 2025-05-15 by Margaret O'Brien

Have you considered "Mixed Precision" instead of a flat INT8 conversion? Sometimes keeping just the final layers in FP16 can save your accuracy while still giving you the 2x speedup you need on modern mobile NPUs.

   Answered 2025-05-17 by James Patterson

• James, we tried Mixed Precision, but our target hardware only has dedicated accelerators for INT8. Anything else falls back to the CPU, which kills our battery life targets. That’s why we’re leaning toward QAT. We need the model to be fully "Integer-Ready" to take advantage of the hardware's neural processing unit without any software-level emulation or expensive floating-point math overhead.

     Commented 2025-05-19 by Sarah Jenkins

Make sure you use a representative calibration dataset if you do try PTQ again. Most people fail because their calibration data doesn't match the real-world distribution.

   Answered 2025-05-21 by Robert Evans

• Spot on, Robert. Distribution shift is the silent killer of quantized models. A diverse calibration set is non-negotiable if you want the weights to represent reality.

     Commented 2025-05-24 by Margaret O'Brien