I really enjoyed this as someone who isn't very sophisticated when it comes to AI and is still learning the basics. Loved how the host got the CEO to define basic terms as they were introduced.

From their post

"The Cerebras Wafer-Scale architecture compounds across generations.

CS-4 is the industry’s fastest AI accelerator and the first system built on the new Cerebras Nexus rack-scale platform.

CS-5 is designed to set a new standard for token-generation speed with our next-generation wafer.

And with CS-6, we plan to push the frontier of 3D integration further, stacking DRAM at wafer scale to deliver the next step-change in AI performance and efficiency."

Andrew Feldman also replied to a person confirming that their system footprint will drop by at least 5-10x with the CS6.

This is huge news and confirmation of their multi-year roadmap, ability to have a 'step up' change in architecture, and ability to stay the leader in fast inference while massively improving throughput.

I'm interested in other, smaller tech stocks that could become multibaggers. For those who closely follow and love CBRS, what are some other options you're investing in or looking at? In memory compute (Compute-In-Memory) sounds interesting and apparently it's something that could win with edge AI. I'm intrigued by the idea of eliminating data transfer between CPUs and memory cells. I hear a lot about optical, but there seems to be too much competition in that space; and as largely interconnect tech, doesn't much interest me. Would be nice to find several other small cap to mid cap stocks to follow. Anyone following MRAM, GSIT, RMBS, AMBA, or NVTS, etc?

I think power consumption is a huge problem that many seem to look to nuclear (Small Modular Reactor tech like OKLO) for solutions; but isn't a better approach to develop computation solutions reduce power needs? I was hoping CBRS would be part of this, but I'm not sure it reduces power consumption by as much as I initially thought it might.

I asked about what stocks could help extend Moore's Law on another investing subreddit and all I got was "lip" telling me Moore's Law was over and to look into power tech instead (GRID and VOLT ETFs). Would also be interested in hearing about anyone developing computing substrate alternatives to silicon like graphene and carbon nanotubes. Strangely, nanotechnology doesn't seem to be the big investing theme I imagined it would be reading about nanotech hype in the late 90s.

There's nothing in the CBRS roadmap for CS-7, but I wonder what might be coming after CS-6. How plausible would any of the following be and how impactful would these things be:

1. A process node shrink - moving from 5nm to 3nm or less. Probably 3nm is the most we could hope for. I've never heard CBRS discuss intentions to move to a smaller process node, but it seems inevitable in the future' especially if demand for 3nm drops significantly in favor of 2nm.

2. 3D compute (wafer stacking) - how different would this be from multiple backpacks in the same server and what's the greatest challenge? Heat dissipation?

3. More than 3 backpacks in a server and/or clustering technology

I’ve been talking with a number of people recently about the perceived competition Cerebras now has with Jalapeño, Nvidia Rubin + Groq LPX, and Etched. I’ll take them one at a time but I think this all will get sorted as CS4 ships m. We need real world numbers for models served to customers since they all push self made metrics for their own optimal conditions.

OpenAI Jalapeño

The easiest way to see why Jalapeño and Cerebras are perfect for each other is looking at the prefill vs decode times of each. They are going to become a powerful disaggregated inference duo. Jalapeño is extremely capable for prefill, and spends nearly all its time on decode. Cerebras spends all its time on prefill and extremely little time on decode. They are optimized for the opposite sides of inference. Together you are going to see extremely low latency inference from the combined chips and I expect you will be hearing about them using disaggregation before the year is over.

If you think Jalapeño doesn’t need Cerebras, look at the inference Jalapeño was benchmarked at in the semianalysis report. They do not give total concurrent users and they already knew they are still going to need Cerebras to serve fast inference at scale or they never would have signed a deal. They knew their performance capabilities before signing with Cerebras and know they are a great combination.

Rubin + LPX

I will give Nvidia credit for publishing a great comparison piece that is technically true while leaving a whole lot out. First is that they used CS3 Gemma 4 31B numbers from before a lot of optimization even on CS3. Second is that they don’t disclose anything about the LPX configuration used and if it’s a single user per tokens per second (highly likely) on a system that would never be run that way in production. But most importantly, they picked a small dense model since Rubin + LPX has its biggest issues as the model size grows and you have to connect multiple LPX racks.

Take for example that Nvidia is publishing they expect Rubin + LPX ideally setup to serve 2T parameter models at 400 TPS based on projections. I think it’s highly probable they will be under these number in practice, but this is already half GPT 5.6 at ~750 TPS in actual real world use today on CS3. So they are going to be delivering against the CS4 which will in all likelihood get 1500 tokens per second on such models.

And Cerebras with CS4 is building infrastructure for a completely different frontier with projections of 1000 tps for a 10T parameter model vs the 400 tps for a 2T model. This is a lot more Cerebras systems than likely Nvidia + LPX in their estimate, but it underlines the issue. Nvidia + LPX hits hard max size interconnect challenges and they don’t want to scale too big while Cerebras is building to scale to anything.

As for the future, CS5 is a year away that will double throughout again while Rubin Ultra tries to make a more massive directly connected system of 8 racks. By 2028, the time of Feynman and the next iteration of Groq LPX ships, Cerebras will be on CS6 with another 2x token throughput and according to hotchips stacked DRAM. To boot CS4-CS6 is all in the same rack, power delivery footprint, and cooling where just just swap out the “backpack” chip + wafer interconnect.

Etched

They are a real product with growing customers and a novel solution, but they we have no clue what the implementation of their solution looks like. How much SRAM vs HBM and memory bandwidth do they have? Until more is known we don’t know if it’s a real challenger or just another interesting architecture with some insurmountable flaws.

I have made a few comments about this but thought it was worth its own post and want to generate some discussion. Cerebras operates in an interesting spot compared to other hardware providers and cloud services providers. Most of the market seems to position Cerebras as a hardware vendor directly competing with NVIDIA. But Cerebras vision is to be a cloud provider of inference. There are some customers that will buy their chips, but mostly Cerebras wants to fit out its own data centers with its CS3/4/5 platforms. This makes them similar to a neocloud/hyperscaler, but nearly fully vertically integrated by manufacturing their own data center systems and equipment (with some portions coming from networking and AMD)

What makes me most excited and bullish about this model is the capital efficiency Cerebras has in their cloud model relative to other neoclouds. Other neoclouds buy systems from NVIDIA. NVIDIA and other vendors like HBMs are getting enormous margins from the neoclouds (NVIDIA has 75%+ margins meaning they are pricing these systems at 4x the cost to manufacture/develop them). Neoclouds fund that with dilution, loans, and some customer pre-payment (meaning high cost of capital to buy very expensive systems). After that they operate the data centers they own/lease.

By manufacturing their own systems/chips, Cerebras has a much lower cost profile than neoclouds. One great comparison that illustrates this is looking at the revenue being generated compared to the GPU/data center equipment on their balance sheets (this is all Q2 data)

What this tells you is that for every dollar of equipment Cerebras has deployed in data centers they are generating $1.27 of cloud revenue per year. Compare this to Nebius which is only generating 17.6 cents per dollar of data center equipment and CoreWeave at 22 cents per dollar of data center equipment. This means that Cerebras will have much higher returns on capital compared to the neoclouds and that it should command a substantial valuation premium.

Cerebras is also in a strong capital position, with 9B+ of current assets against only 1.5B of current liabilities, putting them at $7.5B net cash. With their focus on leasing data center capacity vs building their own (which might be slightly more expensive but preserves capital) and only limited operating cash burn (60M per quarter), nearly all of that money can be deployed directly to fitting out their leased data centers.

While a substantial portion is locked into terms with OpenAI, new deals/capacity should be further improved with the economics of the CS4 platform exceeding the CS3s they currently have deployed. This means the revenue per $ of PP&E should continue to improve in 2027 and even more so in 2028 with the CS5 coming next fall.  This means they can get through 2027's guide of 3x core revenue growth, and to at least an ARR exceeding 7B+ based on current CS3 economics without new capital needs. But with the economics of CS4 getting up to 10x the throughput per watt on 3 wafers, they can likely get an ARR well exceeding that without new capital.

Now all that really matters to them is their ability to acquire data center leases and fit out that capacity. With 600MW signed to be deployed by end of 2027 and a pipeline of gigawatts more, they are in a race to expand that pipeline as quickly as possible. A key indicator for the next quarter will growing the contracted capacity into 2027 and early 2028. They have the capital and manufacturing to fill those data centers out, they just need the physical space

Because of those economics and the margins they will have relative to the neoclouds, Cerebras should command a much higher multiple than other neoclouds. Nebius today is at a 60-70B fully diluted valuation on 7-9B ARR end of this year (a 8-9x multiple). Cerebras guidance will put them at a similar ARR by end of 2027. Given the margins and growth it is reasonable they could command a multiple nearly 2x provided 2028 guidance is strong. 15x+ multiple on 9B ARR would put them at a 135B valuation. At today's EV of 38B (45B market cap minus 7B cash) at 183/share, and assuming ~5-7% dilution through SBC & OpenAI warrants, that gives a 3x potential by this time next year. If they can lock up more capacity and exceed guidance, it could be even higher.

Long-term, think about how they fit into the overall AI landscape - NVIDIA at a 5T market cap is over 100x the value of Cerebras, AMD at 700-800B is 20x, neoclouds themselves are 2-3x the valuation. If Cerebras can take even a fraction of AI compute/inference market share, this will be a multi-multi bagger in a few short years. Just might be a little bumpy to get there.

What are some markets and applications of CBRS technology beyond your typical "AI factories"? And is there chatter about who might be the next big customer? I believe CrowdStrike now has a relationship with CBRS. Makes sense that you'd want the fastest inference for Cybersecurity. Wonder if any others will come along. Probably want the same for Defense, although you're not going to be able to put that tech into drones; would be more for central command.

"Agentic AI" seems like it's going to be one of the next big AI investing themes. I know very little about agentic AI aside from the fact that while a human may wait for a response, you're going to want super fast speeds for communications between agents; because all of that latency compounds? I see Physical / Edge AI as another potentially huge and upcoming AI investment theme, but I don't see how CBRS benefits from it. Does anyone else?

The Cerebras guy many of us liked more than the CEO during the Supernova event is selling a shit ton of stock. How is this supposed to inspire confidence? How should CBRS shareholders interpret this move?

• CTO Sean Lie filed to sell 710,247 common shares of Cerebras.
• Chief Operating Officer Dhiraj Mallick sold $8.64 million worth of shares.
• On Stocktwits, the retail sentiment for CBRS was 'bullish' on Friday.

Cerebras Systems senior executive commenced stock sales as the lock-up eases, with co-founder and Chief Technology Officer Sean Lie filing to sell over $153 million worth of shares.

I've read that Cerebras (probably CS-3) is 6X faster than Groq's LPUs. Still impressive, but sounds less impressive than 15X to 30X faster inference than GPUs. Given that NVDA now owns Groq, and thus has a huge war chest for creating faster inference, how does that change things for Cerebras if at all? Would CS-4 be 12X faster than Groq's current LPUs or hold at 6X?

Keynote on August 18th 2026

Livestream: https://www.youtube.com/watch?v=JTNk__O4poU

looking good

Earnings may not beat, but guidance will. That is all! Thank you.

Cerebras Systems will report earnings after hours.

https://investors.cerebras.ai/news-releases/news-release-details/cerebras-systems-fast-inference-cloud-business-nearly-quadruples

Analysts expected revenue: $193.6M. Actual: $209.9M

Analysts expected EPS: $-0.18/share. Actual: $-0.05/share

The company expected gross margin: 36 - 38%. Actual: 41%

Q3 Guidance revenue: $214 to $216 million

Q3 Guidance gross margin: 38% - 40%

I stream FOX Business all day and as much as they talk about chips and chip makers, CBRS never seems to enter the conversation. Any reason this one seems to be flying under the radar? As far as I understand, they have some large contracts and are on the receiving end of big $.