Updated with just-announced Intel roadmap changes.
It is often said that companies – particularly large companies with enormous IT budgets – do not buy products, they buy roadmaps. …
Back in the day, when the AI revolution was young, Nvidia co-founder and chief executive officer used to make the rounds with each new generation of datacenter GPUs and provide the first DGX systems based on any particular GPU to marquee customers. …
There are many things that are unique about Nvidia at this point in the history of computing, networking, and graphics. …
The generative AI revolution is making strange bedfellows, as revolutions and emerging monopolies that capitalize on them often do. …
If you stare at something for a little bit of time and let your mind wander, you can think of a new way to analyze something that you have looked at a bunch of times. …
If high bandwidth memory was widely available and we had cheap and reliable fusion power, there never would have been a move to use GPU and other compute engines as vector and matrix math offload engines. …
We have said it before, and we will say it again right here: If you can make a matrix math engine that runs the PyTorch framework and the Llama large language model, both of which are open source and both of which come out of Meta Platforms and both of which will be widely adopted by enterprises, then you can sell that matrix math engine. …
We like datacenter compute engines here at The Next Platform, but as the name implies, what we really like are platforms – how compute, storage, networking, and systems software are brought together to create a platform on which to build applications. …
If you handle hundreds of trillions of AI model executions per day, and are going to change that by one or two orders of magnitude as GenAI goes mainstream, you are going to need GPUs. …
For a lot of state universities in the United States, and their equivalent political organizations of regions or provinces in other nations across the globe, it is a lot easier to find extremely interested undergraduate and graduate students who want to contribute to the font of knowledge in high performance computing than it is to find the budget to build a top-notch supercomputer of reasonable scale. …
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