Inside The Massive GPU Buildout At Meta Platforms
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. …
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. …
As a founding member of the Ultra Ethernet Consortium, which has the express purpose of making Ethernet as good for AI and HPC clusters as InfiniBand but with the scalability and familiarity of Ethernet, Arista Networks wants to benefit mightily from the AI wave that is coming to enterprise datacenters the world over. …
Server makers Dell, Hewlett Packard Enterprise, and Lenovo, who are the three largest original manufacturers of systems in the world, ranked in that order, are adding to the spectrum of interconnects they offer to their enterprise customers. …
The exorbitant cost of GPU-accelerated systems for training and inference and latest to rush to find gold in mountains of corporate data are combining to exert tectonic forces on the datacenter landscape and push up a new Himalaya range – with Nvidia as its steepest and highest peak. …
Transitions in the datacenter take time.
It took Unix servers a decade, from 1985 through 1995, to supplant proprietary minicomputers and a lot of mainframe capacity that would have otherwise been bought. …
We said it from the beginning: There is no way that Meta Platforms, the originator of the Open Compute Project, wanted to buy a complete supercomputer system from Nvidia in order to advance its AI research and move newer large language models and recommendation engines into production. …
As we are fond of pointing out, when it comes to high performance, low latency InfiniBand-style networks, Nvidia is not the only choice in town and has not been since the advent of InfiniBand interconnects back in the late 1990s. …
Here we go again. Some big hyperscalers and cloud builders and their ASIC and switch suppliers are unhappy about Ethernet, and rather than wait for the IEEE to address issues, they are taking matters in their own hands to create what will ultimately become an IEEE standard that moves Ethernet forward in a direction and speed of their choosing. …
It was a fortuitous coincidence that Nvidia was already working on massively parallel GPU compute engines for doing calculations in HPC simulations and models when the machine learning tipping point happened, and similarly, it was fortunate for InfiniBand that it had the advantage of high bandwidth, low latency, and remote direct memory access across GPUs at that same moment. …
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