<ul><li>Model quantization involves converting model weights and activations from float32 to lower precision formats like float16 or int8.</li><li>Quantization to float16 is straightforward, while quantization to int8 involves mapping the wide range of float32 values to 256 integer values.</li><li>Two main quantization schemes are used: Affine Quantization Scheme for non-zero offset data and Symmetric Quantization Scheme for zero-centered data.</li><li>Different quantization methods like Dynamic Quantization, Static Quantization, and Quantization Aware Training are used to reduce model size, improve efficiency, and enable real-time AI on limited-resources.</li></ul>

Model Quantization for Scalable ML Deployment

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