In mathematics and digital signal processing, quantization is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite …

Nov 6, 2025 · Quantization is a model optimization technique that reduces the precision of numerical values such as weights and activations in models to make them faster and more efficient.

4 days ago · Quantization reduces the precision of model parameters and activations (for example, from FP32/FP16 to FP8) to shrink memory footprint, improve inference speed, and lower energy …

Quantization is the process of mapping continuous infinite values to a smaller set of discrete finite values. In the context of simulation and embedded computing, it is about approximating real-world …

Jul 22, 2024 · Explore the quantization of Large Language Models (LLMs) with 60+ illustrations.

What is quantization in machine learning? Quantization is a technique for lightening the load of executing machine learning and artificial intelligence (AI) models. It aims to reduce the memory …

Uniform scalar quantization is the simplest and often most practical approach to quantization. Before reaching this conclusion, two approaches to optimal scalar quantizers were taken.

Quantization is a process of converting a continuous set of values (like all the possible real - number values) into a discrete set. In simple terms, it's like taking a wide range of possibilities and breaking …

What is quantization? Quantization is a technique used in machine learning to reduce the computational and memory requirements of models, making them more efficient for deployment on servers and …

Apr 17, 2025 · Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats. But in reality, it sits at the intersection of performance and practicality in modern …