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Artificial Intelligence

Model Quantization for Edge Inference

A

Abdellah Elghazi

April 08, 2026

Introduction

Running AI models on resource-constrained devices requires advanced compression techniques like quantization.

1. From FP32 to INT8

Quantization converts model weights to integers, reducing size and accelerating inference on compatible hardware.

2. Post-Training Quantization

Post-Training Quantization (PTQ) is easy to apply, while Quantization-Aware Training (QAT) integrates errors during training for higher accuracy.

Conclusion

Quantization is a cornerstone of deploying efficient AI everywhere in the modern ecosystem.

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