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 ...

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 ...

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 ...

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 ...

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 ...

AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn’t a viable option for inference on the edge, where ...

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 ...

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 ...