trade-offs in model quantization
Trade-offs in model quantization refer to the compromises made when reducing the size and computational requirements of a machine learning model through quantization techniques. This process involves representing model parameters with fewer bits, resulting in lower precision and increased quantization errors. The trade-offs arise from balancing the reduction in model size and inference latency with the potential loss of accuracy caused by quantization-induced errors. Achieving optimal trade-offs involves finding the right balance between model size, computational efficiency, and the desired accuracy for a specific application.
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