Training AI models is expensive, and the world can tolerate that to a certain extent so long as the cost inference for these increasingly complex transformer models can be driven down. …
There is a seemingly endless list of problems to be solved and issues to be addressed in vendors’ steady march toward quantum computing. …
Everybody knows that companies, particularly hyperscalers and cloud builders but now increasingly enterprises hoping to leverage generative AI, are spending giant round bales of money on AI accelerators and related chips to create AI training and inference clusters. …
The National Institute of Advanced Industrial Science and Technology (AIST) in Japan is going to be installing the third generation of its AI Bridging Cloud Infrastructure 3.0 supercomputer. …
A rising tide may lift all boats, and that is a good thing these days with any company that has an AI oar in the water. …
This week is another major conference for a server OEM, and that must mean it is another time for an OEM to announce its tight partnership with AI compute engine juggernaut Nvidia. …
Updated: Here is something we don’t see much anymore when it comes to AI systems: list prices for the accelerators and the base motherboards that glue a bunch of them together into a shared compute complex. …
Computex, the annual conference in Taiwan to showcase the island nation’s vast technology business, has been transformed into what amounts to a half-time show for the datacenter IT year. …
There are many things that are unique about Nvidia at this point in the history of computing, networking, and graphics. …
For most of the generative AI revolution thus far, the big original equipment manufacturers, or OEMs, have been sidelined as Nvidia and now AMD have done direct allocations of their GPU compute engines to hyperscalers, cloud builders, and other lighthouse customers. …
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