At the time, all this was theoretical. But last week, the company announced they’d linked 16 CS-2s together into a world-class AI supercomputer.

Meet Andromeda

The new machine, called Andromeda, has 13.5 million cores capable of speeds over an exaflop (one quintillion operations per second) at 16-bit half precision. Due to the unique chip at its core, Andromeda isn’t easily compared to supercomputers running on more traditional CPUs and GPUs, but Feldman told HPC Wire Andromeda is roughly equivalent to Argonne National Laboratory’s Polaris supercomputer, which ranks 17th fastest in the world, according to the latest Top500 list.

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A supercomputer, providing massive amounts of computing power to tackle complex challenges, is typically out of reach for the average enterprise data scientist. However, what if you could use cloud resources instead? That’s the rationale that Microsoft Azure and Nvidia are taking with this week’s announcement designed to coincide with the SC22 supercomputing conference.

Nvidia and Microsoft announced that they are building a “massive cloud AI computer.” The supercomputer in question, however, is not an individually-named system, like the Frontier system at the Oak Ridge National Laboratory or the Perlmutter system, which is the world’s fastest Artificial Intelligence (AI) supercomputer. Rather, the new AI supercomputer is a set of capabilities and services within Azure, powered by Nvidia technologies, for high performance computing (HPC) uses.

Lawrence Livermore National Laboratory has long been one of the world’s largest consumers of supercomputing capacity. With computing power of more than 200 petaflops, or 200 billion floating-point operations per second, the U.S. Department of Energy-operated institution runs supercomputers from every major U.S. manufacturer.

For the past two years, that lineup has included two newcomers: Cerebras Systems Inc. and SambaNova Systems Inc. The two startups, which have collectively raised more than $1.8 billion in funding, are attempting to upend a market that has been dominated so far by off-the-shelf x86 central processing units and graphics processing units with hardware that’s purpose-built for use in artificial intelligence model development and inference processing to run those models.

Cerebras says its WSE-2 chip, built on a wafer-scale architecture, can bring 2.6 trillion transistors and 850,000 CPU cores to bear on the task of training neural networks. That’s about 500 times as many transistors and 100 times as many cores as are found on a high-end GPU. With 40 gigabytes of onboard memory and the ability to access up to 2.4 petabytes of external memory, the company claims, the architecture can process AI models that are too massive to be practical on GPU-based machines. The company has raised $720 million on a $4 billion valuation.

Chip startup Cerebras Systems Inc. today debuted Andromeda, a supercomputer optimized to run artificial intelligence applications that features more than 13.5 million processor cores.

Sunnyvale, California-based Cerebras Systems is backed by more than $720 million in venture funding. The startup sells a chip called the WSE-2 that is specifically designed to run AI software. The new Andromeda supercomputer that Cerebras Systems debuted today is based on the WSE-2 chip.

According to Cerebras Systems, Andromeda can provide performance in excess of one exaflop when running AI applications. One exaflop equals 1 million trillion calculations per second. The startup says that Andromeda’s performance makes it suitable for, among other use cases, training large language models, which are complex neural networks that can perform tasks such as translating text and generating software code.