Broadcom turned in its financial results for its third quarter last night, and all of the tongues in the IT sector are wagging about how the chip maker and enterprise software giant has landed a fourth customer for its burgeoning custom XPU design and shepherding business. …
Doubling the transistor count every two years and therefore cutting the price of a transistor in half because you can cram twice as many on a given area transformed computing and drove it during the CMOS chip era. …
Years before Amazon Web Services launched in March 2006, there were a slew of grid computing startups and incumbent system makers – and a few of them with deep supercomputing experience – that were hawking remotely accessible, utility-style computing on demand. …
Sometimes, when datacenter computing does a paradigm shift, things get cheaper, like the move to RISC/Unix systems in the 1990s, and then to X86 systems in the 2000s. …
Building a successful cloud and growing it quarter after quarter, year after year, with first mover status or without it, is not an easy task. …
Every business has its patterns, and so it is with Google and its hodgepodge of advertising and cloud computing. …
As part of the pre-briefings ahead of the Google Cloud Next 2025 conference last week and then during the keynote address, the top brass at Google kept comparing a pod of “Ironwood” TPU v7p systems to the “El Capitan” supercomputer at Lawrence Livermore National Laboratory. …
The HPC centers of the world like fast networks and compute, but they are also always working under budget constraints unlike their AI peers out there in the enterprise, where money seems to be unlimited to what sometimes looks like an irrationally exuberant extent. …
If you want to be a leading in supplying AI models and AI applications, as well as AI infrastructure to run it, to the world, it is also helpful to have a business that needs a lot of AI that can underwrite the development of homegrown infrastructure that can be sold side-by-side with the standard in the industry. …
Rather than measure the funding rounds and valuations of AI startups building foundation models in US dollars, perhaps we should just convert that right into GPU-hours rented to train models, since this accounts for the vast majority of spending that OpenAI, Anthropic, and a handful of others do in their day to day operations. …
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