Chip startup Tenstorrent has achieved a key milestone with its latest funding: Unicorn status.
But the company, which is designing specialized chips for running artificial intelligence applications, still has a long way to go to outpace its rivals and capture a significant share of what’s expected to be a huge market.
Earlier this week, Tenstorrent announced that it raised $200 million from a group of investors led by Fidelity Management and including Eclipse Ventures, Epic CG, and Moore Capital. The deal valued five-year-old company at $1 billion, the threshold at which a startup can claim so-called unicorn status.
Founder and CEO Ljubisa Bajic and chief technology officer Jim Keller, the legendary chip designer who joined in January, tell Fortune that the company’s first product will become commercially available in the third quarter. That processor is capable of 368 trillion operations per second on A.I. tasks, more than the up to 260 trillion claimed by market leader Nvidia’s equivalent chip, although many variables go into a chip’s performance on real-world applications.
A follow-on chip from Tenstorrent is being tested and is expected to ship in the first half of 2022.
Keller says a key early goal is to attract outside programmers who write cutting-edge A.I. applications to try out their software on Tenstorrent’s chips. “The programmers will use the computers, and if they like it, they tell their friends, and if they don’t like it, they give us feedback,” Keller says. “That’s where the rubber hits the road.”
Analysts are impressed with the early results, but warn there’s a long way to go and many competitors, from major players like Nvidia, AMD, and Intel to Google and other startups such as Graphcore, SambaNova, and Groq. The market for specialized A.I. hardware reached almost $15 billion last year, dominated by Nvidia, and it’s forecast to grow 20% annually to $31 billion by 2024, according to research firm IDC.
Many A.I. chip startups
“Tenstorrent’s technology appears to perform well—that’s an important first milestone,” says Peter Rutten, an analyst who follows the developing market for IDC. But, the entire field is “attracting a remarkable amount of investment dollars,” he says, and many companies with technology that is “pretty good performance-wise on all the important benchmarks.”
Groq, led by former Google TPU designer Jonathan Ross, raised $300 million from VC firms last month and SambaNova raised $676 million.
Tenstorrent chips stand out by reworking A.I. software on the fly as the programs run on the processors. The idea is to reduce the amount of unnecessary calculating so as to speed up key calculations. The company also has a team of 30 people working on software to make programming for the chips easier.
That’s a particularly promising strategy that sets the company apart, says analyst Karl Freund at Cambrian AI Research. “They know that software is critical and are spending more on software now than hardware,” he says. “If anyone can successfully challenge Nvidia, it may be these guys.”
Tenstorrent also focused on developing a different way to link many of its chips so they can share data more quickly than standard products. The chips don’t need to connect to each other via an outside networking technology like ethernet, but instead they can communicate with each other more directly. “We don’t need to go through any network infrastructure or any switches,” Bajic explains.
The approach is different than many rivals. Nvidia and AMD are pushing A.I. chips adapted from graphics processing units, or GPUs, that excel at running calculations quickly and simultaneously. Intel, which bought A.I. chip specialist Habana Labs in 2019, and Samsung have focused on adding dedicated specialty A.I. processors within their more general purpose chips.
Meanwhile, Google just announced an update of its A.I.-focused chip that it plans to link in vast pods of 4,096 chips to speed data sharing between processors. And California-based startup Cerebras is making chips that are the size of a record album, with 1.2 trillion transistors each, versus the rest of the industry’s postage-stamp sized silicon. Putting more transistors on a chip may increase computing power, but it also eats up more electricity and complicates the design.
General A.I.
The entire field is racing to make A.I. programs faster and more efficient for an increasing array of applications, from the speech recognition systems powering digital assistants like Siri and Alexa to self-driving cars.
One of the industry’s long-term goals is to enable the creation of artificial intelligence that can do more than work on a single specialized problem like speech recognition. So-called general A.I. has been an elusive goal for decades. But with Tenstorrent’s systems acting more like the human brain, Bajic sees the possibility of aiding the search. “There is a chance that some kind of threshold could be crossed,” he says.
A global chip supply shortage, caused by a lack of manufacturing capacity in the semiconductor industry and supply chain woes during the pandemic, has created challenges. The original plan was to put the chip on a card that customers could buy and plug into servers. Those will go on sale in July starting for $1,000.
But the server manufacturing is backlogged, delaying availability of new hardware for customers that may not want to install a startup’s card in their front line computers. So Tenstorrent will also sell rack-mounted computers with its chips that can be put directly in data centers or server rooms for $4,000 to $100,000 each. And Tenstorrent will install some of its chips in a cloud data center for customers to use remotely without having to buy any gear.
“We’re going to do the simple and obvious thing, which is sell people computers, and also set up computers that they can use (via the cloud) for a reasonable price,” Keller says. In the short run, Tenstorrent is probably too small to convince Amazon to install its processors in cloud data centers for customers, Keller says, but it could happen in the more distant future.
Keller’s career is filled with short but significant stints at major technology companies. In the 1990s, he helped create some of Digital Equipment’s industry-leading chips. Later, he worked on Apple’s earliest in-house chips for iPhones and iPads. Following that, he designed Tesla’s self-driving chip. Before joining Tenstorrent, Keller spent two years at Intel, which was struggling with manufacturing delays and leadership turnover. He left Intel in June 2020.
Hiring the chip design master has helped Tenstorrent build credibility in a crowded field and attract new funding.
Out of the basement
Tenstorrent CEO Bajic grew up in Serbia when the country was part of Yugoslavia and went to high school in Moscow, attracted by the strong math offerings. He moved to Canada in 1985 to study electrical engineering and then went to Silicon Valley to work on chips. After stints at VLSI and Nvidia, Bajic went in 2014 to AMD, where he first met and worked for Keller. Both left for other things—Keller to join Tesla for a few years and Bajic to develop his own ideas for a startup that became Tenstorrent.
At the time, Keller wanted to encourage Bajic, telling him he’d supply seed funding for a startup if Bajic came up with “something cool.” So Bajic developed an idea for a chip intended to excel at machine learning and built an early prototype. He says he brought it to show Keller at Tesla in a “big, brown ragged box.”
Bajic was late for the meeting, but Keller was still impressed with the contents of the ragged box and decided to offer initial funding for a startup. It wasn’t just the chip’s potential, Keller adds: “I funded him partly because I thought it would be hilarious if he and a couple guys lived in the basement for a year building this technology, which they literally did.”
Bajic eventually moved out of his basement and into nicer offices in Toronto. “One of the side effects is that I got used to low light conditions,” Bajic jokes now, as he sat in the same basement of his house for a Zoom interview with Fortune. After a few years of progress, he lured Keller to work for him.
The current boom in A.I. hardware startups reminds Keller of earlier eras of chips. At one time, there were more than 30 companies developing graphics processors, he recalls, and later there were about 40 startups working on networking chips (Keller worked at one called SiByte that was bought by Broadcom in 2000).
“A.I. is kind of going through the same boom, and, as best I can tell, it’s gonna be bigger than the previous ones,” he says.
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