Model quantization represents model weights, activations, or cache values with fewer bits to reduce memory traffic, storage, energy, and often inference latency. This guide explains the mechanism, ...
Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
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