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.

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 …

Nov 24, 2025 · Quantization has emerged as a crucial technique to address this challenge, enabling resource-intensive models to run on constrained hardware. The NVIDIA TensorRT and Model …

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 …

Jul 20, 2025 · Among many optimization techniques to improve AI inference performance, quantization has become an essential method when deploying modern AI models into real-world services.

Oct 16, 2025 · Quantization converts high-precision data into lower-precision data by compressing it to reduce data loss. By optimizing quantization, you can reduce your model's computational burden …

One of the basic choices in quantization is the number of discrete quantiza-tion levels to use. The fundamental tradeoff in this choice is the resulting signal quality versus the amount of data needed to …

Quantization is representing the sampled values of the amplitude by a finite set of levels, which means converting a continuous-amplitude sample into a discrete-time signal.

Quantization is a fundamental concept in the fields of statistics, data analysis, and data science, referring to the process of constraining an input from a large set to output in a smaller, discrete set.