Quantization in Machine Learning: 5 Reasons Why It Matters More Than You Think

Quantization might sound like a topic reserved for hardware engineers or AI researchers in lab coats.

K-Means Clustering in OpenCV and Application for Color Quantization

The k-means clustering algorithm is an unsupervised machine learning technique that seeks to group similar data into distinct clusters, with the aim of uncovering patterns in the data that may not be apparent to the naked eye. It is possibly the most widely known algorithm for data clustering, and it…

October 27, 2023

In "AI/ML Research"

Effective KV Compression with TurboQuant

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.

May 1, 2026

In "AI/ML Research"

Using Quantized Models with Ollama for Application Development

Quantization is a frequently used strategy applied to production machine learning models, particularly large and complex ones, to make them lightweight by reducing the numerical precision of the model’s parameters (weights) — usually from 32-bit floating-point to lower representations like 8-bit integers.

May 30, 2025

In "AI/ML Research"

AI Generated Robotic Content