Running AI models on x86 CPUs is becoming easier and faster ...

Right off the bat, let’s give a shout out to the mathematician propeller-heads who create the transformations that make it possible to do all kinds of high performance computing to simulate, model, ...

A monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Credit: VentureBeat made with OpenAI ChatGPT-Images-2.0 While many AI open source model providers are pursuing larger and more powerful models, Google is still giving attention to the smaller, more ...

As large language model (LLM) inference demands ever-greater resources, there is a rapid growing trend of using low-bit weights to shrink memory usage and boost inference efficiency. However, these ...

Copyright: © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

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 ...

FLUX is an educational deep learning framework that reimplements the core functionality of PyTorch and TensorFlow from scratch, using only C++ and the Standard Template Library. No external ...

Researchers claim to have developed a new way to run AI language models more efficiently by eliminating matrix multiplication from the process. This fundamentally redesigns neural network operations ...

Abstract: Multiplying matrices is among the most fundamental and compute-intensive operations in machine learning. Approximated Matrix Multiplication (AMM) based on table look-ups can significantly ...

On Monday, Nvidia unveiled the Blackwell B200 tensor core chip—the company’s most powerful single-chip GPU, with 208 billion transistors—which Nvidia claims can reduce AI inference operating costs ...