The two chip companies have signed a term sheet, according to sources with direct knowledge of the agreement. Intel has signed a term sheet to acquire the AI chip startup SambaNova Systems, two sources with direct knowledge of the agreement tell WIRED. The details of the term sheet are unknown. The agreement is non-binding, meaning the deal is not yet finalized and could be dissolved without penalty. It could take weeks or even months before regulatory approval, liability scrutiny, and financial due diligence are complete.

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.

The venture capital arm of Google-parent company Alphabet is leading a $56 million funding round in SambaNova Systems, a start-up building computer processors and software for artificial intelligence and data analytics. It's the first time Alphabet's venture arm, GV, has invested in an AI hardware company. The move comes as Google CEO Sundar Pichai and other execs frequently insist that Google is an "AI-first" company. Google sells AI services for developers and counts on it to enable special features in its consumer products, while Waymo relies on AI for autonomous driving. Alphabet called out AI in the business overview section of its 2016 and 2017 annual reports.

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. A consensus is emerging in the AI community that a large foundation model with hundreds of billions to trillions of parameters is going to beat a highly tuned model on a small subset of relevant data every time. If this turns out to be true, it will have significant implications for AI system architecture as well as who will likely be able to afford having such ginormous foundation models in production. Our paraphrasing of "more data beats a better algorithm" is a riff on a quote from Peter Norvig, an education fellow at Stanford University and a researcher and engineering director at Google for more than two decades, who co-authored the seminal paper The Unreasonable Effectiveness of Data back in 2009, long before machine learning went mainstream but when big data was amassing and changing the nature of data analytics and giving great power to the hyperscalers who gathered it as part of the services they offered customers. "But invariably, simple models and a lot of data trump more elaborate models based on less data," Norvig wrote, and since that time, he has been quoted saying something else: "More data beats clever algorithms, but better data meets more data."

SambaNova says its latest chips can best Nvidia's A100 silicon by a wide margin, at least when it comes to machine learning workloads. The Palo Alto-based AI startup this week revealed its DataScale systems and Cardinal SN30 accelerator, which the company claims is capable of delivering 688 TFLOPS of BF16 performance, twice that of Nvidia's A100. However, in machine learning training workloads, SambaNova says the gap is even larger. The company claims its SN30-based DataScale systems are six times faster when training a 13-billion parameter GPT model than Nvidia's DGX A100 servers, at least according to its internal benchmarks, so take them with a healthy dose of salt. The SN30 is manufactured on a 7nm TSMC process node, which packs 86 billion transistors into a single die.

When it comes to building any platform, the hardware is the easiest part and, for many of us, the fun part. But more than anything else, particularly at the beginning of any data processing revolution, it is experience that matters most. Whether to gain it or buy it. With AI being such a hot commodity, many companies that want to figure out how to weave machine learning into their applications are going to have to buy their experience first and cultivate expertise later. This realization is what caused Christopher Ré, an associate professor of computer science at Stanford University and a member of its Stanford AI Lab, Kunle Olukotun, a professor of electrical engineer at Stanford, and Rodrigo Liang, a chip designer who worked at Hewlett-Packard, Sun Microsystems, and Oracle, to co-found SambaNova Systems, one of a handful of AI startups trying to sell complete platforms to customers looking to add AI to their application mix. The company has raised an enormous $1.1 billion in four rounds of venture funding since its founding in 2017, and counts Google Ventures, Intel Capital, BlackRock, Walden International, SoftBank, and others as backers as it attempts to commercialize its DataScale platform and, more importantly, its Dataflow subscription service, which rolls it all up and puts a monthly fee on the stack and the expertise to help use it. SambaNova's customers have been pretty quiet, but Lawrence Livermore National Laboratory and Argonne National Laboratory have installed DataScale platforms and are figuring out how to integrate its AI capabilities into the simulation and modeling applications. Timothy Prickett Morgan: I know we have talked many times before during the rise of the "Niagara" T series of many-threaded Sparc processors, and I had to remind myself of that because I am a dataflow engine, not a storage device, after writing so many stories over more than three decades. I thought it was time to have a chat about what SambaNova is seeing out there in the market, but I didn't immediately make the connection that it was you.

Did you miss a session from the Future of Work Summit? Fifty-three percent of enterprise technology leaders are worried they will run out of computing power in the next decade -- one of several challenges hindering organizations as they look to scale up artificial intelligence initiatives, according to a new report by SambaNova Systems. With AI and ML becoming ubiquitous across industries, it has the same potential to refactor the Fortune 500 as the internet has had over the past several decades. But as the AI revolution accelerates, there's a burgeoning gulf between the haves and the have-nots. That is, a growing number of top companies have figured out how to deploy AI initiatives at scale, gaining a competitive edge against businesses that have yet to do so.

In what may be a first, an Eastern European bank is teaming up with the government of Hungary to field an AI supercomputer that will be used to create a large language model of the Hungarian language. OTP Bank, which was founded in Hungary and operates banks across the region, worked out an agreement under which the government provides about half the funding for the supercomputer developed under contract with SambaNova Systems. The government will have access to the system for public and academic research, said Péter Csányi, deputy CEO and head of the digital division at OTP. The contract with SambaNova Systems capitalizes on the Generative Pretrained Transformer (GPT) for generating large language models that SambaNova announced in October, branding it as dataflow-as-a-service. "Building a system like this to run a GPT model is not something any bank has done before," said Marshall Choy, VP of product at SambaNova.

All the sessions from Transform 2021 are available on-demand now. SambaNova Systems, winner of VentureBeat's 2021 Innovation in Edge award, is a significant contender in the global edge computing market. The startup raised $676 million in April 2021 and is moving from its origins as an AI-specific chip company to one that provides comprehensive dataflow-as-a-service to its clients. Research firm MarketsandMarkets forecasts an impressive 34% compounded annual growth rate for the market and anticipates its value reaching $15.7 billion by 2025. Traditional central processing units (CPUs) and graphics processing units (GPUs) are based on transactional processing, which needs accuracies to the nth degree for computations.

A staggering amount of money is pouring into data center AI chip companies at the moment. Data center AI chip companies are raising eye-watering amounts of money. In the last week, we've seen Groq announce a $300 million Series C round of funding, and SambaNova raise a staggering $676 million Series D. SambaNova is now valued at somewhere above $5 billion. They are not the only ones in this sector raising these huge amounts of money. Fellow data center AI chip companies Graphcore (raised $710 million, valued at $2.77 billion) and Cerebras (raised more than $475 million, valued at $2.4 billion) are hot on their heels as the sector continues to gain momentum.