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Introduction to Model Quantization
As deep learning models, particularly large language models (LLMs), continue to grow in complexity and size, their computational demands and memory footprints become substantial. Deploying these models efficiently, especially on resource-constrained devices like mobile phones, IoT sensors, or even for cost-effective inference in data centers, presents a significant challenge. This is where model quantization emerges as a critical optimization technique.
Model quantization is the process of reducing the precision of the numerical representations of a neural network's weights and activations. Instead of using high-precision floating-point numbers (e.g., 32-bit floats, FP32), quantization converts them to lower-precision integers (e.g., 8-bit integers, INT
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