Explain model quantization techniques for deploying deep learning models efficiently. Compare post-training quantization with quantization-aware training, and discuss tradeoffs between model size, latency, and accuracy.

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Model quantization reduces numerical precision of neural network weights and activations, enabling smaller model sizes, faster inference, and deployment on resource-constrained hardware.

Explain model quantization techniques for deploying deep learning models efficiently. Compare post-training quantization with quantization-aware training, and discuss tradeoffs between model size, latency, and accuracy.

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