The major cloud builders and their hyperscaler brethren – in many cases, one company acts like both a cloud and a hyperscaler – have made their technology choices when it comes to deploying AI training platforms. …
There is more than one way to get to a large language model with over 1 trillion parameters that can do lots of different things and enterprises can use to create AI training and inference infrastructure to extend and enrich their thousands of applications. …
The only way to accurately predict the future is to live it, but just the same, prognostication is one of the things that we humans love to do. …
Having commercialized its waferscale AI computing platform to a certain extent over the past several year, Cerebras Systems reportedly wants to get an initial public offering done before the AI hype peaks. …
If you have the entire corpus of the Internet scrubbed of nonsense plus whatever else you can scrounge up in whatever language all put into the right format so you can chew on that data one token at a time with trillions of parameters of interconnections between those tokens to build a large language model for generative AI applications, you have an enormous problem. …
The HPC gurus of the world may have started moving into the hyperscalers and cloud builders in recent years, but they don’t tend to work for vendors and they tend to stay in one place and lean in. …
One of the first tenets of machine learning, which is a very precise kind of data analytics and statistical analysis, is that more data beats a better algorithm every time. …
When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. …
Most of the large companies and institutions we cover here at The Next Platform have already been in the AI/ML trenches, particularly the hyperscale web and media companies (where much of this work began) and in high performance computing. …
While Hungarian banks with collective names like OTP Group might not have household recognition here in the U.S., …
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