VentureBeat

Cohere cracks lossless quantization and native citations with first full Apache 2.0 licensed open model Command A+

At the architectural level, Command A+ represents a major evolution from Cohere’s previous dense models. It is a decoder-only Sparse Mixture-of-Experts (MoE) Transformer. While the model houses a ...
IEEE

Randomized Quantization for Privacy in Resource Constrained Machine Learning at-the-Edge and Federated Learning

Abstract: The increasing adoption of machine learning at the edge (ML-at-the-edge) and federated learning (FL) presents a dual challenge: ensuring data privacy as well as addressing resource ...
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 ...
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 ...