There is increasing competition coming at Nvidia in the AI training and inference market, and at the same time, researchers at Google, Cerebras, and SambaNova are showing off the benefits of porting sections of traditional HPC simulation and modeling code to their matrix math engines, and Intel is probably not far behind with its Habana Gaudi chips. …
Imagine, if you will, that AMD could make as many Epyc CPUs and Instinct GPU accelerators as it wanted at a reasonable yield and cost. …
While chip designer and maker Intel has a new strategy and a new executive team to implement it, it is going to take a long time for changes made last year and this year to be felt and for product and process roadmap changes to put the company into a better competitive situation. …
To a certain extent, the only thing that really matters in a computing system is what changes in its memory, and to that extent, this is what makes computers like us. …
Within a year or so, with the launch of the “Grace” Arm server CPUs, it will not be heresy for anyone at Nvidia to believe, or to say out loud, that not every workload in the datacenter needs to have GPU acceleration. …
It has taken untold thousands of people to make machine learning, and specifically the deep learning variety, the most viable form of artificial intelligence. …
There are a lot of things that compute engine makers have to do if they want to compete in the datacenter, but perhaps the most important thing is to be consistent. …
When the “Aldebaran” datacenter GPUs were launched by AMD last November for the HPC and AI crowd pushing up into the exascale stratosphere, only the two top-end models of the Instinct GPU accelerators –ones that use the Open Accelerator Module (OAM) form factor put forth by Facebook and Microsoft under the Open Compute Project – were actually available. …
Paid Post There’s no doubt that the repurposing of GPU silicon has accelerated the development of artificial intelligence technology over the last decade. …
When any new abstraction layer comes to compute, it can only think in integers at first, and then it learns to do fractions and finally, if we are lucky – and we are not always lucky – that abstraction layer learns to do multiplication and scale out across multiple nodes as well as scaling in – slicing itself into pieces – within a single node. …
All Content Copyright The Next Platform