IEEE

Data Quality-Aware Mixed-Precision Quantization via Hybrid Reinforcement Learning

Abstract: Mixed-precision quantization mostly predetermines the model bit-width settings before actual training due to the non-differential bit-width sampling process, obtaining suboptimal performance ...
GitHub

Python implementation of the TurboQuant and QJL vector quantization algorithms.

turboquant-py implements the TurboQuant and QJL vector quantization algorithms from Google Research (ICLR 2026 / AISTATS 2026). It compresses high-dimensional floating-point vectors to 1-4 bits per ...
Microsoft

Advances to low-bit quantization enable LLMs on edge devices

Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...
IEEE

DL-AQUA: Deep-Learning-Based Automatic Quantization for MMSE MIMO Detection

Abstract: Directly affecting both error performance and complexity, quantization is critical for MMSE MIMO detection. However, naively pruning quantization levels is ...
InfoWorld

What is model quantization? Smaller, faster LLMs

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...
Techopedia

What is Quantization?

Quantization is a process aimed at simplifying data representation by reducing precision – the number of bits used. This process involves approximating a continuous range of values with a smaller set ...