Everyone knows that machine learning inference is going to be a big deal for commercial applications in the years ahead, but no one is precisely sure how much inference is going to be needed. …
It is hard to imagine how anyone could run Nvidia better than it is being run right now. …
It is a good thing for Nvidia that most of the hyperscalers in the world – or at least the ones that matter – also have substantial public cloud businesses. …
Having to install a new kind of systems software stack and create applications is hard enough. …
Researchers at South Korea’s Electronics and Telecommunications Institute (ETRI), in conjunction with Arm, are one step closer to designing and deploying a native CPU that can handle double-precision supercomputing applications and low-precision, low-power AI inference. …
The move to integrating AI into current operations and finding its role in entirely new applications at Royal Bank of Canada (RBC) is similar to what we’re seeing among other large-scale enterprises. …
As we’ve talked about in the past, the focus on data – how much is being generated, where it’s being created, the tools needed to take advantage of it, the shortage of skilled talent to manage it, and so on – is rapidly changing the way enterprises are operating both in the datacenter and in the cloud and dictating many of the product roadmaps being developed by tech vendors. …
Long gone are the days when high performance computing was limited solely to traditional simulation and modeling at academic and government research labs. …
There are many things the U.S. government can do, but innovating at a rapid pace in the ever-evolving world of artificial intelligence is not necessarily one of them. …
It is not inconceivable, but probably also not very likely, that the datacenter business at GPU juggernaut Nvidia could at some point in the next one, two, or three years equal that of the core and foundational gaming sector. …
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