The Limits of AI Model Quantization: A Deep Dive
Artificial Intelligence (AI) has revolutionized industries, from healthcare to finance. As AI models grow in complexity, so does their computational demand. To address this, researchers and engineers have turned to various optimization techniques, including quantization. Quantization involves reducing the precision of numerical representations used in AI models, thereby reducing their memory footprint and computational cost. While quantization offers significant benefits, it's crucial to understand its limitations, particularly for large, complex models. Understanding Quantization At its core, quantization involves converting high-precision floating-point numbers into lower-precision integer or fixed-point numbers. By reducing the number of bits required to represent each parameter, quantization can lead to several advantages: Reduced Memory Footprint: Smaller models can be deployed on devices with limited memory, such as mobile devices and edge devices. Faster Inference Time: Lower-p…
