Analysis and prediction are core to today’s IT as organisations embark on digital transformation, with use cases that range from speech recognition and pattern analysis in science, to use in fraud ...

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 ...

Training deep neural networks is one of the more computationally intensive applications running in datacenters today. Arguably, training these models is even more compute-demanding than your average ...

Arm Holdings has announced that the next revision of its ArmV8-A architecture will include support for bfloat16, a floating point format that is increasingly being used to accelerate machine learning ...

Why floating point is important for developing machine-learning models. What floating-point formats are used with machine learning? Over the last two decades, compute-intensive artificial-intelligence ...

No matter what kind of pricing proposition AMD brought to the server sector or how many cores and threads it offered up, Intel could always play the AVX-512 card, touting superior performance in very ...

I will review the AI chips made by AMD, Google and Tesla. It is possible that Tesla with a strong Dojo 3 chip could pass AMD as second best AI chip performance and volume of chips. The top line of ...

The new BF16 Stable Diffusion 3.0 Medium model is now available in Amuse 3.1. This release brings bfloat16 precision to the widely used Stable Diffusion 3 framework, letting you run complex image ...