Cerebras to raise IPO price range to $150-$160 as demand surges, sources say

[KevinK]

Three updates on this one:

Since Cerebras supports multi-user workloads on common hardware, doesn't this capability answer your question?

1) This article is much better than the S1 for us hardware types.

Cerebras — Faster Tokens Please

// OpenAI and AWS Partnerships, Tokenomics Explainer, Architecture Deep Dive, Datacenter Ramp, Technical Roadmap

newsletter.semianalysis.com

2) The article delves into the economics and potential Pareto curve and limitations of Cerebras - I'm not going to summarize them all here. Most important is that Cerebras is good at fast tokens and they are 6x more valuable (at least at current rates from OpenAI) vs normal tokens. No actual Pareto frontiers for Cerebras yet, though.

The company is considering a new IPO price range of $150-$160 a share, up from $115-$125 a share, and raising the number of shares marketed to 30 million from 28 million, said the sources, who asked not to be identified because the information isn't public yet.
At the ⁠top of the new range, Cerebras would raise roughly $4.8 billion, up from $3.5 billion under its original terms, though the figures remain subject to change before pricing, the people said.

3) Pricing is now $185 / share

 

[blueone]

Three updates on this one:

1) This article is much better than the S1 for us hardware types.

Cerebras — Faster Tokens Please

// OpenAI and AWS Partnerships, Tokenomics Explainer, Architecture Deep Dive, Datacenter Ramp, Technical Roadmap

newsletter.semianalysis.com

2) The article delves into the economics and potential Pareto curve and limitations of Cerebras - I'm not going to summarize them all here. Most important is that Cerebras is good at fast tokens and they are 6x more valuable (at least at current rates from OpenAI) vs normal tokens. No actual Pareto frontiers for Cerebras yet, though.


3) Pricing is now $185 / share

I read the section on the WSE-3 I/O networking earlier. I'm still going over it to make sure I understand what the authors are saying, but I'm not convinced the article accurately represents how the Cerebras system uses I/O. Yet.

 

[blueone]

I've often wondered... how much would Cerebras be worth if they achieved the same results without wafer-scale? How big is the wafer-scale premium? Can wafer-scale really be applied more broadly than AI? (I'm currently a skeptic.)

 

[KevinK]

how much would Cerebras be worth if they achieved the same results without wafer-scale?

Not sure they could have. What would they have done - another Groq ?

How big is the wafer-scale premium?

That's a good question - maybe we'll get a better view on their Pareto cost / interactivity tradeoffs as they grow. And their collaboration with Amazon on disaggregation should be revealing in how well they can build heterogeneous systems that offer a broader set of model operating cost / interactivity points.

Can wafer-scale really be applied more broadly than AI?

Who knows ? They made a good bet on AI as the killer app back in 2016 - an app that benefits from huge scale / density / interconnect, plus plenty of high bandwidth SRAM with sufficient value that the added cost (yield, HW and SW R&D, cooling) is secondary. Maybe there will be another app like AI some day. But until then, there's still a lot of running room with AI in the data center.

 

[blueone]

As I type this Cerebras (CBRS) is trading at $301/share.

 

[blueone]

An interesting tidbit that I noticed when I was looking at the Cerebras website... one of original individual investors in Cerebras was Lip Bu Tan. It's a very distinguished list, including names like Andy Bechtolsheim, Pradeep Sindhu, Dadi Perlmutter, Fred Webber, and Nick McKeown, among others.

 

[Barnsley]

As I type this Cerebras (CBRS) is trading at $301/share.

Even higher after hours.

2025 revenue $510M

Is interesting if nothing else.

The cost of AI isnt coming down anytime soon methinks if these guys are a big driver. I assume they want to pump that revenue otherwise their valuation is nonsense.

 

[lefty]

the $180 price was only for insiders. For normal people, the price started selling at $380 and ended at $321

 

[soAsian]

 

[KevinK]

I read the section on the WSE-3 I/O networking earlier. I'm still going over it to make sure I understand what the authors are saying, but I'm not convinced the article accurately represents how the Cerebras system uses I/O. Yet.

Based on the evidence SemiAnalysis offers, I'm not sure that MemoryX and SwarmX I/O work as well for streaming weights (inference & training) and gradients (training) as positioned. AFAIK, their wins today leverage sharding a smallish model within just a few WSEs

"The key takeaway is that Cerebras, while fast, pays a large latency cost to move data on and off the wafer, and therefore their cost-to-performance ratio (or perf per Joule) will depend on how much of that latency they can hide or minimize. A clue about the difficulty of this in practice may be reflected in Model offerings on Cerebras Inference Cloud. The largest production model is GPT-OSS, which is only 120B total parameters. There are larger preview models, but even those top out at 355B (GLM 4.7). For reference, Sonnet and Opus are 1T and 5T parameters respectively, per Elon. Notably, the formerly popular Llama 70B and 405B models were also deprecated, potentially due to the economics of serving them."

"Cerebras’s chips are only economically capable of serving relatively small models today, or at least based on what’s available to the public. GPT-5.3-Codex-Spark, for example, is NOT at all the same thing as the full GPT-5.3-Codex; it’s gpt-oss-120b fine-tuned on GPT-5.3-codex traces. In other words, it’s a distilled model that’s over 10x smaller."

Found the video version of this article to be helpful as well:

 

[blueone]

Based on the evidence SemiAnalysis offers, I'm not sure that MemoryX and SwarmX I/O work as well for streaming weights (inference & training) and gradients (training) as positioned. AFAIK, their wins today leverage sharding a smallish model within just a few WSEs

"The key takeaway is that Cerebras, while fast, pays a large latency cost to move data on and off the wafer, and therefore their cost-to-performance ratio (or perf per Joule) will depend on how much of that latency they can hide or minimize. A clue about the difficulty of this in practice may be reflected in Model offerings on Cerebras Inference Cloud. The largest production model is GPT-OSS, which is only 120B total parameters. There are larger preview models, but even those top out at 355B (GLM 4.7). For reference, Sonnet and Opus are 1T and 5T parameters respectively, per Elon. Notably, the formerly popular Llama 70B and 405B models were also deprecated, potentially due to the economics of serving them."

"Cerebras’s chips are only economically capable of serving relatively small models today, or at least based on what’s available to the public. GPT-5.3-Codex-Spark, for example, is NOT at all the same thing as the full GPT-5.3-Codex; it’s gpt-oss-120b fine-tuned on GPT-5.3-codex traces. In other words, it’s a distilled model that’s over 10x smaller."

Found the video version of this article to be helpful as well:

I'm not buying this explanation yet. MemoryX units are only used to store weights. The parameters are declustered into each WSE-3 node, then the processing is all local, so there isn't inter-node traffic for processing like there is for Nvidia systems. It looks like the MemoryX Ethernet links are only for filling and draining the 44GB SRAM, which at ~10GB/s per link wouldn't take very long. But I'm not confident that I fully understand model processing yet, because I've never read the code being executed. I've gotten lazy and distracted in my old age. I'm also not sure about how mature Cerebras multi-node systems are.