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. …
The one thing that AMD’s return to the CPU market and its more aggressive moves in the GPU compute arena have done, as well as Intel’s plan to create a line of discrete Xe GPUs that can be used as companions to its Xeon processors, has done is push Nvidia and Arm closer together. …
Throughout the many different types of system architecture in the past six decades, one thing has always remained true: Hardware always gets ahead of software, and rather than be too annoyed about it, there is another thing that is also true. …
At this point in the history of the IT business, it is a foregone conclusion that accelerated computing, perhaps in a more diverse manner than many of us anticipated, is the future both in the datacenter and at the edge. …
To a certain way of looking at it, Nvidia has always been engaged in the high performance computing business and it has always been subject to the same kinds of cyclical waves that affect makers of supercomputers and enterprise systems. …
As the list of the most powerful supercomputers on the planet reveal, GPU accelerated high performance computing is alive in well across many technical and scientific domains. …
Sales of various kinds of high performance computing – not just technical simulation and modeling applications, but also cryptocurrency mining, massively multiplayer gaming, video rendering, visualization, machine learning, and data analytics – run on little boom-bust cycles that make it difficult for all suppliers to this market to make projections when they look ahead. …
More than five years ago, Nvidia, driven by its co-founder and CEO, Jensen Huang, turned its considerable focus to developing technologies for the revitalized and burgeoning artificial intelligence space. …
A research team from Nvidia has provided interesting insight about using mixed precision on deep learning training across very large training sets and how performance and scalability are affected by working with a batch size of 32,000 using recurrent neural networks. …
If there is anything the hyperscalers have taught us, it is the value of homogeneity and scale in an enterprise. …
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