Not AMD but related. Cerebras was talked about a little here before. Supposedly it goes public next month. Anyone considering investing a little?
[ EDIT: why those HTML-ish window frame attributes are visible I don’t know. ]
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I am thinking about it. Sounds very interesting. It is in the right space and could be the beginning of the bull run in Inference for AI.
https://www.sec.gov/Archives/edgar/data/2021728/000162828024041596/cerebras-sx1.htm
**Our Market Opportunity
We participate in a large and growing AI market. Our full suite of AI computing solutions addresses use cases for training, inference, software, and expert services. We estimate the TAM for our AI computing platform to be approximately $131 billion in 2024, growing to $453 billion in 2027, a 51% CAGR. This TAM is comprised of the following core markets:
•AI Compute for Training. As businesses continue to evaluate and deploy solutions, we believe the market for training new models will continue to grow. Based on market estimates in Bloomberg Intelligence research, our estimate of the TAM for AI Training Infrastructure is $72 billion in 2024, growing to $192 billion in 2027, a 39% CAGR.
•AI Compute for Inference. While GenAI training is essential to developing models that are powerful and accurate, we believe inference is the next phase of the ongoing wave of AI disruption. As more enterprises develop models and start to deploy their models in applications at scale, the need for high performance and efficient inference is becoming critical to fully realize the commercial potential of ML. We believe that the inference opportunity is enormous, as the market is in the early phases in its adoption cycle. Our estimate of the TAM for AI Inference is $43 billion in 2024, growing at an estimated 63% CAGR to $186 billion in 2027.
•Software and Expert Services. Based on market estimates in Bloomberg Intelligence research, our estimate of the TAM for GenAI Software and Services is $16 billion in 2024, growing to $75 billion in 2027, a 67% CAGR.
We believe we are at the very early stages of a large and fast-growing market opportunity. As adoption of AI continues to accelerate, we expect numerous new applications will be identified, and we believe our solutions are well-positioned to capitalize on the wave of disruption that will come in the coming years.**
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I’m hazy on the opportunity for Cerebras vs. other players. Have you looked closely at their tech? It’s astounding – processors that cram 900,000 cores onto a single chip the size of a 12 inch wafer, with 44GB memory incorporated on the wafer as well, and ludicrous memory bandwidth because the interconnect among the cores on the wafer basically eliminates cross-network latency… but who has use cases that really fit this thing well? A weird beast.
They have only one real solid customer:
In July 2023, Cerebras found the deep pockets it needed to push its technology into production at this scale with a partnership with Group 42, or G42 for short, which is a multinational conglomerate formed in 2018 in Abu Dhabi and backed by the government of the United Arab Emirates with over 22,000 employees that build AI models in Arabic and operate datacenters to run those models.
This past April, according to the S-1 document filed by Cerebras with the SEC, G42 agreed to buy $300 million in hardware, software, and services, which is related to its four phase rollout of a cluster of “Condor Galaxy” supercomputers that we reported on here. The initial Condor Galaxy-1 machine installed earlier this year had 576 WSE compute engines, with a total of 489 million cores and 18 exaflops of aggregate FP16 tensor math and 738 TB of SRAM memory, all for around $900 million at list price but we only saw $37 million drawn down from that G42 traunch and the rest is still in $263 million in restricted cash.
In May, G42 upped its acquisition agreement with Cerebras by another $1.43 billion, which it agrees to spend by February 2025 to build out the rest of the Condor Galaxy infrastructure. The S-1 document also shows G42 has agreed to purchase 22.85 million shares of Cerebras stock at a purchase price of $335 million by April 15, 2025. So presumably Cerebras will go public after that time, not before.
By filing for its initial public offering, Cerebras is now looking to Wall Street to give it even more funding to expand its operations and a chance for its employees that have stock options to cash out. Cerebras is not saying how much of its stock it will float to the public or at what price. Presumably G42 is getting a sweet deal on its stock, and the asking price will be higher than the $14.66 per share it has agreed to pay. The word on the street is that Cerebras is looking to raise between $750 million and $1 billion, and that could translate into a valuation of somewhere between $7 billion and $8 billion.
The interesting thing about any S-1 filing is that it gives us a look at the financials of the company going public for the first time. And in this case, as we said above, it shows how much the G42 deal – and therefore AI work in Arab countries – has mattered to Cerebras. And, frankly, how little money government agencies, hyperscalers, and cloud builders have spent thus far on waferscale systems from Cerebras despite the many obvious advantages.
In terms of the ludicrous capacity of this thing, again:
The initial Condor Galaxy-1 machine installed earlier this year had 576 WSE compute engines, with a total of 489 million cores and 18 exaflops of aggregate FP16 tensor math and 738 TB of SRAM memory
489 million cores?!? How much inference are you doing? (esp if they lean towards deferring to NVDA in training.) If they’re doing training and they can run rings around the performance of other training HW then maybe.
I dunno. I agonize over this one. The gadget is cool, but… I dunno.
I had looked briefly at getting in pre-IPO but for now, gonna hold off.
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That’s ok caromero, thanks for your post. The customer bit is really concerning and the customer is in Dubai. But the thing I do like is that they are on the cutting edge of AI, providing tools for inferencing. It makes sense to have something like this that will cut the number of links between GPU clusters because all those links slow everything down. Still looking at it.
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The Fool RuleBreakers on Cerebras.