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Introduction
As Artificial Intelligence models grow in complexity and size, their computational and memory demands skyrocket. This presents significant challenges for deployment, especially on resource-constrained devices like mobile phones, IoT sensors, or when serving models at high throughput in data centers. Model quantization emerges as a critical technique to address these challenges. It involves reducing the precision of the numerical representations used in a neural network, typically from 32-bit floating-point numbers (FP32) to lower-bit integers like 8-bit integers (This article was generated by an AI automation pipeline as part of a daily technical knowledge-base series. While effort is made to keep it accurate, AI-generated content can contain errors or become outdated. Please verify important details against the official documentation or sources linked above before relying on it, and use your own discretion.
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