Dynamic range quantization is a technique used in deep learning to reduce the precision of model parameters by representing them with fewer bits while preserving the model's performance. This method aims to optimize the efficiency of inference by balancing the trade-off between computational speed and accuracy, allowing models to run on devices with limited resources.
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