In pursuit of faster and more efficient AI system development, Intel, Arm and Nvidia today published a draft specification for what they refer to as a common interchange format for AI. While voluntary ...

In pursuit of faster and more efficient AI system development, Intel, Arm and Nvidia today published a draft specification for what they refer to as a common interchange format for AI. While voluntary ...

Most AI chips and hardware accelerators that power machine learning (ML) and deep learning (DL) applications include floating-point units (FPUs). Algorithms used in neural networks today are often ...

LAS VEGAS--(BUSINESS WIRE)--Tachyum™ today released the second edition of the “Tachyum Prodigy on the Leading Edge of AI Industry Trends” whitepaper featuring updates such as the implementation of ...

In March, Nvidia introduced its GH100, the first GPU based on the new “Hopper” architecture, which is aimed at both HPC and AI workloads, and importantly for the latter, supports an eight-bit FP8 ...

A way to represent very large and very small numbers using the same quantity of numeric positions. Floating point also enables calculating a wide range of numbers very quickly. Although floating point ...

The chip designer says the Instinct MI325X data center GPU will best Nvidia’s H200 in memory capacity, memory bandwidth and peak theoretical performance for 8-bit floating point and 16-bit floating ...

Researchers at Nvidia have developed a novel approach to train large language models (LLMs) in 4-bit quantized format while maintaining their stability and accuracy at the level of high-precision ...

New Linear-complexity Multiplication (L-Mul) algorithm claims it can reduce energy costs by 95% for element-wise tensor multiplications and 80% for dot products in large language models. It maintains ...

Essentially all AI training is done with 32-bit floating point. But doing AI inference with 32-bit floating point is expensive, power-hungry and slow. And quantizing models for 8-bit-integer, which is ...