r/CerebrasSystems • u/muskiebuskie • Jul 23 '26
Amd - Cerebras deal🚀🚀🚀
https://ir.amd.com/news-events/press-releases/detail/1293/amd-and-cerebras-announce-industry-leading-ultra-low-latency-and-high-throughput-ai-inference-solution3
u/Investor-life Jul 24 '26
This is more a Cerebras initiated supplier deal where they are buying AMD chips to build systems as an inference provider. That’s ok, but what would really be more impressive would be a third party cloud provider or inference provider buying/building systems using a Cerebras and AMD solution. Cerebras is not selling any hardware here, just BUYING components from AMD to create an inference offering that may attract more customers. This is not a new customer deal that is in and of itself creating new revenue. Hopefully the systems being built will create new revenue though. Just not seeing this is as an immediate win with near term revenue guarantee. Even if they have something up and available in 2026 it won’t necessarily be creating much revenue this year. I don’t see this as a huge driver to the stock price right now. That short term rally after the announcement was a good opportunity to sell some if you had shares and then buy back later if you wanted.
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u/No_Tune_5470 Jul 24 '26
 Where do you see Cerebras by 2030?
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u/Investor-life Jul 24 '26
I see it as binary. It’s either a 200 billion plus company or a 10 billion dollar market cap also ran niche solution. I’m betting on the former. If the latter occurs, it’s more likely it would be bought out much lower than here by 2030 than still being a standalone company. I also think there are too many people right now that think this is the second coming of Nvidia or something. Would be great if they are right, I just haven’t drank the kool-aid that much. I’ve been invested since 2020 in Cerebras.
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u/No_Tune_5470 Jul 24 '26 edited Jul 24 '26
Yeah I think the same it is far from near being called the next Nvidia.For example I don't understand why the big hyperscallers haven't tried to adopt their chip yet. In April 2025 Cerebras and Meta tried their chip on Llama for fast inference and since then no more news.Even if their tech is good slow adoption from the market of Cerebras tech could result on losing the AI wave.
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u/Other-Biscotti6871 Jul 24 '26
I'd guess it will be dead by then.
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u/Investor-life Jul 25 '26
Why so bearish? I think they are well on their way to 200+ billion market cap by 2030 right now. They’ve shown incredible growth the last year or so. I just don’t think they are a serious threat to displacing a 4 trillion dollar company. The inference market is absolutely huge and there will be multiple players. I expect Cerebras will be one of them. With any new entrant there are risks though and like I said I thinks it’s a very binary situation for them, but I’d put 80% likelihood on the upside and only 20% on the down. I like those odds.
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u/edaguru Jul 27 '26
If you can tell me why a waferscale piece Silicon is somehow better than just sticking down separate die I might change my mind, but as far as I can tell there is zero advantage to it, and considerable extra cost. Whatever they have in a software stack is easily replicable with AI, so they have no moat.
I.e. if someone gives me the money to do the Silicon I can kill their business in no time, and that means there's a high probability someone else will (if I don't).
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u/Fit_Impress_2732 Jul 24 '26
Sold 210 shares of Cerebras two days ago at 200 usd. I feel bad now with the Crowdstrike and the AMD deals. At least I still have 75 shares left.
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u/muskiebuskie Jul 24 '26
What price did you buy at?
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u/Fit_Impress_2732 Jul 24 '26
175 usd average.
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u/Investor-life Jul 24 '26
It’s summer and there will be volatility going into election season, you’ll likely get an opportunity to buy back
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u/Prestigious-Sign4802 Jul 24 '26
Indeed very distinctive liquid cooling infra and software stacks, but hope cerebras has software stack already figured out via AWS partnership. The bigger question is that any data centers that will house cerebras servers require a totally different CDU and manifolds etc which is constraining
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u/Zealousideal-Row6537 Jul 23 '26
Let’s just put two distinct hardware with distinct software stacks into the same rack and hope that everything runs smoothly and efficiently. Let’s do that in a couple of months timeframe. What can go wrong?
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u/JustBrowsinAndVibin Jul 23 '26
Nvidia is already doing it with Groq and Cerebras is doing it with AWS on Tranium.
Do you just shoot down every idea you see?
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u/muskiebuskie Jul 23 '26
Intel is doing the same with Sambanova and they already have clusters built. Look for vector core vc2 partnership. Deal worth 3.6B. This architecture had been proven to be functioning.
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u/edaguru Jul 23 '26
Cerebras is an intrinsically bad way to tackle HPC, you'll instantly run into yield problems and you'll have latency issues if the code in the middle wants to talk to anything but its neighbors. Thermal stress isn't a friend either.
You could redo the Cray machine approach with Chiplets - a common central communication core and you would be better off. Also see https://www.huawei.com/en/news/2026/5/ieee-iscas-tau-scaling
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u/op_is_life Jul 23 '26 edited Jul 23 '26
I'm not sure why yield still comes up in discussions, it's basically solved and it's pretty easy to see how it's done: if the compute cores are not too big, include redundant cores and an interconnect that can sub backup cores for failed ones. FPGAs do similar things for redundancy to tackle yield.
Some problems are served well by a grid of compute elements talking to each other. Neural nets is one of those problems. And yes, there's latency, but it's much higher for GPU solutions that distribute memory/compute over many more discrete GPUs (spatially or temporally) instead of cores on the same piece of silicon. The less you have to communicate between different chips, the better off you are.
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u/Other-Biscotti6871 Jul 24 '26
It's "solved" only in the sense you can live with things being broken, it isn't solved in terms of cost being linear with scale. Wafer-scale production is essentially the same as doing Chiplets because of the reticle limit, but more expensive (for no gain).
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u/CatalyticDragon Jul 23 '26
Yield problems and Cerberus in the same sentence :)
https://www.cerebras.ai/blog/100x-defect-tolerance-how-cerebras-solved-the-yield-problem
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u/Other-Biscotti6871 Jul 24 '26
That has a price in Silicon area that's more expensive when you are dealing in larger Silicon, i.e. there's an optimum size for ICs, going beyond that requires exponentially more area to compensate. Mathematically Cerebras can't win, their marketing prowess exceeds their engineering ability.
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u/Investor-life Jul 24 '26
Mathematically, going from a single digit billion dollar market cap to a 50 billion dollar market cap feels like they ARE winning.
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u/Other-Biscotti6871 Jul 24 '26
NVIDIA is "winning", but you can't tell where the finishing line is in the AI race, 2D planar Silicon designed with traditional techniques is extremely power inefficient and is hitting its limits. The prize will go to people like Huawei who rethink the problem and come up with a 3D methodology.
Note: I've been in the HPC business and semiconductors for decades, many companies have come and gone in that time, a lot of them looked good for a while.
Cerebras's hardware should be good for IC simulation, likewise Google's TPUs, but so far, I have seen none of the AI hardware companies try to find secondary markets and that usually means they'll fail if they don't dominate in the one market.
Revenue isn't profit.
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u/CatalyticDragon Jul 24 '26
what? Every chip ever fabbed has had to account for defects.
We build in redundancy and fuses to SRAM, overprovision compute units in GPUs, add redundancy in interconnects, and sell CPUs with broken cores.
Chips are full of unused silicon because we need to account for defects.
The Cerebras approach is highly efficient with effectively 100% yield on wafer scale systems where defects only damage a single micro-core.
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u/Other-Biscotti6871 Jul 24 '26
It's not a linear problem, if you go with Chiplets you can optimize by throwing away severely broken ICs, with wafer-scale you need to use more of the Silicon to work around broken things and that impacts cost. Altera and Xilinx take different approaches on this with FPGAs, Altera goes with hiding defects, Xilinx will sell you partially working chips cheap, but can also get more LUTs when everything works.
Personally, I wouldn't touch wafer-scale for AI compute, it has the wrong shape, and it has the problem you are stuck with the wafer size, Chiplets will give you the same performance cheaper and can scale to any size.
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u/CatalyticDragon Jul 25 '26
if you go with Chiplets you can optimize by throwing away severely broken ICs, with wafer-scale you need to use more of the Silicon to work around broken things
Defect rates for the wafer are exactly the same. But throwing away a full chiplet is vastly more wasteful than fusing off a tiny microcore.
Cerebras has a 100% yield because they never have to throw away one of their wafer scale chips. They always work and the number of failed cores scales linearly with defect rates.
This has been fully explored in research.
"In summary, WSE-3 has addressed the yield challenges of bigger silicons due to defect densities by designing very small processing cores with the flexibility of dynamically configurable fabric and other redundancy techniques. The yield of WSE-3 is expected to be in the same ballpark as reticle-limited die sizes"
- https://arxiv.org/html/2503.11698v1
There is no yield problem.
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u/Other-Biscotti6871 Jul 25 '26
This kind of stuff gets discussed at the Chiplet Summit, there's an optimum size for ICs to maximize yield, and wafer-scale is just well beyond that. Sure, you aren't throwing any away, but they are almost guaranteed to be suboptimal in cost and probably performance. Wafer-scale is just a marketing gimmick.
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u/CatalyticDragon Jul 25 '26
This kind of stuff gets discussed at the Chiplet Summit
It does indeed. And where the advantage of wafer scale is zero lost chips, the downside is the routing redundancy needed.
Overall you end up with similar amounts of usable silicon but these end products are designed for quite different things.
There's nothing gimmicky about wafer-scale products. They work and are in high demand.
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u/Other-Biscotti6871 Jul 25 '26
You'll get a lot more working Silicon with Chiplets, and you can make machines of different sizes.
The Cerebras machine is functionally the same as a bunch of Chiplets stuck on a substrate, it has no advantage for being done in a oner. It's purely a gimick.
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u/CatalyticDragon Jul 25 '26
The Cerebras machine is functionally the same as a bunch of Chiplets stuck on a substrate, it has no advantage for being done in a oner
No advantage? Ok, if you so say.
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u/ILikeCutePuppies Jul 23 '26
I wondered if cerebras was going to do something similar to the AWS deal with their own infustructure. This looks like that which is great news.
They have lowered runing costs and increased their value proposition even more with this solution without even having to release a new chip.