Nvidia Bends The Clouds To Its Own Financial Will
Nvidia is not rich enough – or dumb enough – to build a cloud to rival the likes of Amazon Web Services, Microsoft Azure, or Google Cloud. …
Nvidia is not rich enough – or dumb enough – to build a cloud to rival the likes of Amazon Web Services, Microsoft Azure, or Google Cloud. …
The top hyperscalers and clouds are rich enough to build out infrastructure on a global scale and create just about any kind of platform they feel like. …
Predicting the future is hard, even with supercomputers. And maybe specifically when you are talking about predicting the future of supercomputers. …
Like everyone else on planet Earth, we were expecting for the next generation of graphics cards based on the “Ada Lovelace” architecture to be announced at the GTC fall 2022 conference this week, but we did not expect for the company to deliver a passively cooled, datacenter server friendly variant of the GeForce RTX 6000 series quite so fast. …
You can’t be certain about a lot of things in the world these days, but one thing you can count on is the voracious appetite for parallel compute, high bandwidth memory, and high bandwidth networking for AI training workloads. …
Imagine, if you will, that Nvidia had launched its forthcoming “Grace” Arm server CPU three years ago instead of early next year. …
When Nvidia announced a deal to buy Mellanox Technologies for $6.9 billion in March 2019, everyone spent a lot of time thinking about the synergies between the two companies and how networking was going to become an increasingly important part of the distributed systems that run HPC and AI workloads. …
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. …
Normally, when we look at a system, we think from the compute engines at a very fine detail and then work our way out across the intricacies of the nodes and then the interconnect and software stack that scales it across the nodes into a distributed computing platform. …
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