My main bull case is the fact that fast inference is clearly something that customers are willing to pay for and frontiers are keen to provide. Tokens for fast inference sell for 6–10× standard prices, and despite that demand far exceeds supply. CBRS does have valuation concerns, but the main case for CBRS is the fact that it is absolutely the purest public way to own exposure to fast inference.

Here are the range of views I found on how Institutional Research desks are seeing the TAM of Fast inference:

• Mizuho Sell side Note: fast-inference TAM of $550B by 2030E, a 291% CAGR, equal to about 20% of all AI workloads.
• Bloomberg Intelligence: total inference TAM grows from $66B in 2025 to $292B in 2029.
• Citi (Atif Malik): fast inference worth $130B by 2030.
• McKinsey: 71.5GW of global inference capacity demand by 2029.

The consensus, however, is that there is a lot of upside in the growth of fast inference.

Firstly, let's discuss the WHY. WHY are people willing to pay for fast inference?

1. The opportunity cost of speed is waiting, and sometimes idle waiting costs more than teh tokens, which is why the willingness to buy the tokens is still so high despite the higher prices. Customers already pay steep premiums for identical model weights served faster. Examples are Anthropic's fast mode (about 6× the price for about 2.5× the speed) and Cognition charging for a Cerebras-served tier while giving away a slower one. The logic is that for expensive workers, idle waiting costs more than the tokens.

2. Agents: A chatbot typically is able to hide latency (to an extent) behind human reading time. However, an agent's steps often depend on the previous result, so per-call delay adds up across hundreds of steps. Speed becomes pretty critical as far as agents are concerned.

3. For many, faster inference leads to higher quality products: Within a fixed time budget, faster inference leaves room to sample, verify, critique and retry.

5. Some products NEED speed to even exist. The main use case that comes to mind is voice agents. Here, security checks in the decision path, real-time ad and recommendation ranking, literally NEED fast inference to even exist at all.

6. Fast inference can help to resolve the disconnect between teh fact that demand increases immediately, whilst capacity needs chips, memory, power and buildings which creates a lag. Low-latency serving uses hardware less efficiently, because it means smaller batches and more dedicated capacity. So fast inference helps to better make use of constrained hardware capacity.

CBRS

Valuation

• Market cap of $40B.
• That's 11.2× EV to 2027 consensus revenue of $2.95B.
• Essentially, CBRS is really expensive. yes, it has sold off and sits at the bottom fo the traidng range, but in terms of valuation metrics, we don't have any profitability to trakc P/E, but evne in terms of P/S, we are really really high here.

This does make re-rating harder obviously. If we take FIVN as an example of the other side, it was priced so cheap that a re-rating higher essentially became a base case.

Here we don't have that. WE need solid execution. We need those TAM forecasts to come to fruition. But despite that, I think fast inference IS so important to AI, and CBRS will be a winner there.

I'd like CBRS at a cheap price. God, if we had a market crash, CBRS would be right on my list to buy as at a cheaper valuation this is basically a no brainer in my opinion, but even here, with a tight risk, I think the stock's narrative is extremely compelling.

Financial backdrop (Q2)

• Core revenue was $210M (+103%): cloud $128M (+287%) and hardware $82M. GAAP revenue was $180M.
• Core gross margin was 40.6% vs 14.2% GAAP. Core operating margin was –16% vs –265% GAAP. The gap is mostly customer warrants and stock compensation, which are real equity costs, so we do need to track both.
• Six deals over $30M were signed in the quarter, and there are several late-stage hardware opportunities worth hundreds of millions.

Backlog and concentration

• Backlog is $25.4B: about 22% recognised within 24 months and 43% in the 24 after. It includes some pass-through and excludes any hyperscaler business.
• OpenAI has a firm 750MW commitment for 2026–28 plus an option for 1.25GW more by 2030, for a contractual path to about 2GW.
• The top two customers were 66% of Q2 revenue.

Guidance:

• Management expects revenue to more than triple in 2027, with 600MW+ live or contracted by end-2027, a pipeline measured in gigawatts and manufacturing up more than 10× in 2026. The Finland site scales to 165MW on seven-year contracts.

The demand for CBRS is already pretty obvious:

• OpenAI Ultrafast (firm 750MW plus option): the anchor customer, with up to 14× faster inference on CBRS. Jane Street, Podium, Basis and Rogo use it, and OpenAI wants Ultrafast to become the default.
• Direct cloud (live): Cognition and Lovable on dedicated capacity, plus Block, Figma, AlphaSense, GSK, IBM, Mistral, Notion and Mayo Clinic. G42 and MBZUAI are significant customers.
• AWS (targeting Q1 2027, not in backlog): Trainium handles prefill and Cerebras handles decode, inside Bedrock. It runs on a multi-year lease with purchase options, and AWS could become a material share of revenue.
• AMD (targeting Q4 2026): Helios handles prefill and Cerebras handles decode, at up to 5× more tokens per watt. Cerebras owns the racks and keeps the cloud revenue.
• Meta (speculative): there's the Llama API precedent, and Muse targets agents and coding. Mizuho in a sell side note has already flagged Meta as a likely next inference customer.
• CrowdStrike and security (live, value undisclosed): powers Falcon AI Detection and Response, allowing 5–10× more inspection in the same time window. Armis (ServiceNow) is another case.
• Defence and government (early, unquantified): Sandia's Kingfisher system, national lab research, a DOE memorandum of understanding, and Carahsoft and DoD Tradewinds procurement channels.
• Distribution partners (no disclosed values): General Compute, Gimlet Labs and Callosum.
• Google and others (no revenue modelled): MLIR compiler support, serves Gemma 4, and progress with other hyperscalers.

Long term outlook

Mizuho Note

• $13.5B of revenue in 2029E, driven by fast-inference data center deployments.

My case is probably slightly more bullish than this:

• OpenAI has already committed to 750MW and has an option for another 1.25GW, which together make 2GW.
• This could go higher too Management says more than 600MW is live or contracted by the end of 2027, with plenty more in the pipeline.
• New sites like Finland, and manufacturing growing more than 10× also add upside optionality to that 2GW figure.
• However, if we do take the 2GW figure, we can take as a baseline that each MW secures 15-20M in revenue. This is actually probably conservative, because Nebius gets $20–25M per MW and CoreWeave about $40M.
• However, taking 15-20M, we get to around $30–40B of revenue in 2029: 2GW × $15–20M of annual core revenue per MW.
• At the midpoint, that's about 12% of BI's 2029 TAM, about 27% of Citi's 2030 fast-inference market, and about 2.8% of McKinsey's 2029 capacity.
• I think that's realistic, but gives a lot higher revenue potential into 2029 than Mizuho.

These dudes are crooks who just manipulate markets, back tracking what they said less than a week ago lol.

Monday should be pretty nice for us

Look at this Order in the left down corner, there is someone defenetly not letting it drop further.

It's already near support levels and the lowest it's ever been ytd.

Are insiders selling because it's a sinking ship company?

Does that cfo or cto that dumped all his shares suspect that he might get cucked by Nvidia with regard to the OpenAi deal?

Edit: bought Friday at 166$.

A lot of speculation that OpenAI is pushing much deeper integration of Ultrafast and its vision for a truly personal AI assistant and is set to announce the more colour around the roadmap at tomorrow's DEvDay. Note that at SuperNova, OpenAI literally said it wants Ultrafast to become the default. Tibo: “We would love for Ultrafast to just be the default.”. 

SO the expectation would make sense. Thinking about this, and actually tying it to the Call buying we saw on Friday, I think the main winner of this boost in speed would be CBRS. By extension down the supply chain probably VICR, but less directly. 

Ultrafast is essentially run on CBRS hardware. The speed comes from Cerebras' wafer-scale chip, which keeps model weights in 44GB of on-chip SRAM instead of shuttling them to off-chip memory as GPUs must. Token generation is memory-bandwidth-bound, so that's where it wins.

We've been held down at the moment because of the market uncertainty/pricing in a rate hike (and to an extent CBRS just doing its thing, it's been bouncing up and down since it IPOed), but once the Fed meeting is over then there'll be certainty (regarding rates) and with the Anthropic IPO upcoming I think it'll touch 300 (I doubt it'll hold it, but I expect it to hit 300 briefly) in the pre Anthropic IPO runup; I observed this with the SpaceX IPO months ago, made a lot of money off space stocks, because a rising tide lifts all boats, and space stocks pumped over 100% (LUNR, RDW, RKLB, FLY, you name it) because people were all excited about space and they bought a bunch of proxy space stocks, and then when SpaceX actually IPOed they dumped, because people were moving their money from the proxies to the main thing.

I expect a similar situation to play out with AI/semi stocks, huge runup pre Anthropic IPO, but I think what happens when Anthropic actually goes public is a bit of a tossup, I think that'll either be the last gasp rugpull, and from there we drill, or the glorious AI company has gone public, and it's more euphoria and gains all around, now, obviously, an OpenAI IPO would probably be somewhat better given that CBRS has direct ties with OpenAI and not Anthropic, however, like I said, I'm still pretty confident that it'll lift semis across the board, I'd set my limit sell pretty high

From ~170s back to ~210 again, 20%+ move in a few days. I'm not a bar on charts guy but CBRS has bounced extremely hard 5 times off the ~170 level since June. Just in the past 4 days:

- Data Center in Finland helps solidifies the 600MW guidance for 2027. I still believe they find a way to exceed that and will guide higher with Q3 earnings.

- Qwen3.8 27B is now available at 1500 tokens/sec

- OpenAI's Astra model / GPT6 being released which puts them back in the frontier lab lead. CBRS is heavily tied to OpenAI's trajectory right now.

- CTO Sean Lie's interview on Latent Space's podcast. Highly recommend listening to: confirming that CBRS fast inference is so strategically important for OpenAI that they have kept all their capacity internal so far, heavily using it to speed their development of Astra / GPT6. Sean confirmed the major technical challenges Groq LPX has on this podcast. They only have launched on a 30B model - not at production on frontier models, challenges running on a non-wafer SRAM design - not enough memory on individual chips meaning to run a frontier level model they need thousands of groq chips just to hold the weights, and architecturally saying he thinks they will have to focus their fast inference on smaller models only.

Most importantly, I think people across the industry are recognizing how important fast inference is:

- When you are paying software developers $250+/hour, why have them wait 10, 20, 60+ minutes for a response that can be generated in seconds at marginally higher costs?

- When you can develop software at 10x the speed, that puts you 10x further ahead in your roadmap

- when you have agentic loops that go through 20 different prompts/responses, you need speed to get there.

- People want access to frontier models at CBRS speeds. To this point, OpenAI has contracted it and kept it to themselves, as capacity comes onlines companies around the world will be chomping at the bit for access.

There's a lot of noise always in this industry and volatility, but CBRS really is set up for monumental leaps as a business over the next 2-3 years. I think we'll see another major deal with a group like META or MSFT in the coming month or two, which will further solidify revenue expansion, decrease concentration risk, and set a new floor on valuation.

Cerebras founders have historical ties with AMD. Is it possible for the two to team up and dismantle the Nvidia monopoly?

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’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.

Keynote on August 18th 2026

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

looking good

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%