Quantized Model Deployment

What if the only thing standing between your neural network and real-time mobile performance is the precision you refuse to give up?
Your model ran flawlessly in PyTorch-400MB of FP32 weights, a 350-watt GPU, and all the thermal headroom in the world. Then you deployed it to a phone. It stuttered. It heated up. The OS killed it before it produced a single inference. The market no longer asks whether AI can run on mobile. It asks why your AI is slower and less accurate than the cloud version. The answer is not your architecture. It is your precision.
This book is the field manual for engineers who refuse to accept the old compromise of smaller models and weaker accuracy. Inside, you will learn:
• Why INT8 and FP16 are not arbitrary format choices, but hardware-mandated keys to dedicated acceleration paths on Snapdragon, Apple Neural Engine, and MediaTek APU • How naïve post-training quantization can crater accuracy by double-digit percentages-and the calibration, range estimation, and outlier handling techniques that prevent it • The exact deployment architecture for TensorFlow Lite, Core ML, ONNX Runtime Mobile, and NNAPI, including operator fusion and numerical equivalence testing • Why quantization is the only optimization that simultaneously improves latency, accuracy, and power consumption-and how to combine it with pruning and knowledge distillation for wearables and IoT
Stop accepting the compromise between speed and accuracy. Build models that run cooler, faster, and sharper on the devices already in your users' pockets. The precision you can no longer afford is the precision you can finally reclaim.