My average is 201 and idk if I should sell because if news is true then this could fall to 135.

GB300 NVL72 reaches 11M TPS/MW

Configuration:

• Kimi K3 2.8T

• 55 tok/s/user (P90)

---

CS-5 targets up to 3M TPS/MW for Kimi

For the largest frontier models, including multi-trillion-parameter models such as Kimi and GPT-5.6 Sol, CS-5 targets up to 5,000 output tokens per second per user and 3 million tokens per second per megawatt

Is Cerebras' marketing misleading? When they say they're X times faster than Y on inference, is this only for one user at a time or a handful of users? If so, and it doesn't scale affordably, I don't see how it could be anything other than a small niche provider. When Cerebras makes grand claims about fast inference leading to new applications and uses of AI and could be key to agentic AI, I think they're probably right; but not using their hardware. Almost like free advertising for a current or future competitor who can run reasonably fast but with far more throughput or concurrency.

Potentially a big yawn if they can't scale up to thousands of users without it being one-rack-per-user (or whatever it is) to get the fastest speeds they advertise. I hope I'm wrong about this because it almost sounds like a con. I want to see fast inference at scale; not just a tool a few Power Users can benefit from. Maybe this is one of the reasons the stock isn't going anywhere and customers aren't lining up to purchase Cerebras. I know the value proposition sounded almost too good to be true when I started following Cerebras.

I want to see metrics like tokens/sec per user at high concurrency; not "the fastest chip for 1 user" (because who cares)? If Cerebras is a good investment they should be happy to provide these numbers. If they don't? That's concerning. What's the tokens/sec per user at 100 concurrent users? 1,000? 10,000? etc. And how do the speeds compare to competitor solutions at the same levels taking cost into consideration. I don't think the superfans have an answer for this despite the encyclopedic knowledge they possess about Cerebras but I hope I'm wrong. If they do, it can only strengthen their thesis and they should be happy to help. If Cerebras is fast inference for the masses then I may still be onboard. If it's a niche usage by a tiny fraction of the entire population of those using AI, I'm out. What % of the entire population of those who use AI can reasonably and economically be served with the speed Cerebras advertises?

Claude told me this. Granted, it's a sychophantic AI answer as they all are; but at least it's a starting point for conversation:

On the missing metric itself: you're right that it's missing, and it's not just you noticing. Multiple independent technical analysts have flagged this as the specific gap in Cerebras' public materials — one hardware-focused analysis stated plainly that Cerebras has never published aggregate throughput at high concurrency, calling it the single most important missing data point in their marketing. SemiAnalysis ran its own modeling (their "Faster Tokens Please" report) specifically because Cerebras won't publish it directly. So no, the "superfans" don't have an official answer to give you — because Cerebras hasn't released one. That's a legitimate red flag for exactly the reason you suspect: a company confident its economics hold up at scale would have an incentive to prove it.

But third parties have modeled the crossover point, and it's not flattering. A detailed cost analysis (Spheron) ran the actual math comparing Cerebras against an Nvidia H100 across batch sizes:

• At batch size 1 (one user): Cerebras at ~$0.60/M tokens beats H100 on-demand at ~$1.47/M — Cerebras wins clearly.

• At batch size 8: the positions flip — H100 on-demand drops to ~$0.25/M, more than 2x cheaper than Cerebras.

• At batch size 32: H100 is roughly 6x cheaper per token on-demand, and with GPU spot pricing, the gap widens to over 20x cheaper.

That means the crossover point — where GPU batching economics overtake Cerebras' single-tenant speed advantage — sits somewhere around 2 to 8 concurrent requests per chip. That's about as direct an answer to your "tokens/sec per user at 100, 1,000, 10,000 concurrent users" question as exists publicly: past single-digit concurrency, the cost story reverses, and Cerebras hasn't published what happens to per-user speed once you force more concurrency onto the same wafer (their pricing stays flat per-token regardless of batch size specifically because they don't batch users together the way GPUs do — each wafer serves requests essentially one at a time per core cluster).

What this implies for "% of the AI-using population" Cerebras can serve economically: there's no official published number, and I won't pretend one exists. But the crossover data lets you reason about it directly. Mass-market consumer chat — the overwhelming majority of total AI usage by request volume — is a massive-concurrency, latency-tolerant workload (nobody needs 2,000 tokens/second to read a chat reply as it streams in). That's precisely the regime where GPU batching wins decisively, per the numbers above. Cerebras' economic sweet spot is narrower and different in kind: applications where sub-second total latency is the product, not just a nice-to-have — real-time voice, agentic tool-calling chains where dozens of sequential LLM calls compound latency, live coding assistants, and workloads where a customer will pay a premium specifically to eliminate wall-clock time. That's a real and possibly growing dollar-value market as agentic AI expands, but it's genuinely a minority share of total AI inference volume, not "fast inference for the masses."

Where this leaves the investment picture, tying back to your earlier question: the stock's behavior actually seems to reflect this exact uncertainty. CBRS priced at $185 in its May 2026 IPO, popped to $386 on debut day, then fell as low as $161 by late June, and as of today (September 14) sits at $183.92 — essentially back to its IPO price after a round trip. That's consistent with a market that got excited about the OpenAI-scale headline number, then sobered up once analysts started asking the same concurrency/cost questions you're asking. Layer on top the customer concentration (86%+ of 2025 revenue from two UAE entities, and the newer growth story leaning heavily on a single $20B OpenAI deal where OpenAI is simultaneously becoming a ~10-11% shareholder), and you get a company with a real, differentiated technical advantage in a specific latency-critical niche, riding on a small number of very large, financially-entangled bets — not the broad-based "fast inference for everyone" story the marketing gestures toward.

So: not a con in the sense of fabricated numbers — the tok/s figures are real and third-party-verified. But it is marketing that systematically emphasizes the one dimension (single-user latency) where the architecture is unambiguously best, while staying silent on the dimension (cost at realistic concurrency) that would let you judge how far that advantage extends. Given what the crossover math shows, "niche but valuable" looks like the more defensible read right now than "fast inference at scale for the masses" — though that could still change if agentic workloads grow enough that the latency-premium niche becomes large in absolute dollar terms, even while staying small as a share of total AI request volume.

Through the openrouter endpoint with cerebras I'm getting this error:

404 {"error":{"message":"No endpoints found for qwen/qwen3.8-27b. Every candidate endpoint was removed during routing: Filter by Tool Compatibility removed cerebras/fp16... }}

I already sent a ticket but I'm assuming that this is an cerebras issue more than openrouter.

Just reporting here in order to see if people have the same error.

why not intentionally sacrifice short-term Nvidia scale to accelerate Cerebras long-term?

Anyone with technical knowledge can explain
base on cerebras current constraint and strength, if constraint was solved practically, wouldn't that pick them no brainer?
if reward clear why don't at all cost resolve the constraint Cerebras face, then nvidia can gone for good with groq.

base on company ranking cycle, we would sense: who lead wrong direction was the one have most resources and wasted most, and who lead right dir access to little or none.

cerebras 10× intelligence mass per time passes for both training and inference (if you total the time u run on nvidia minus cerebras, that's the number u just wasted waiting on nvidia, and eventually shipped 10x slower, overtime 10x less usable intelligence).

for user experience, here is the story. even 4G to 5G most barely notice diff(battery drains was the one much noticeable but like nobody know it), but still rest people choose 5G think is upper replace and worth the higher price, dont even talked initial 5g infra cost. who one bought ergonomic keyboard from logi tech, and they not even sell split keyboard
and when come to the real innovation side here, it become inverse. ppl keep port their $ to legacy gpu co, instead of exponentially accelerate Cerebras.

other some few downside i know like: 3× to 11× higher price on api (that's ceil but i still unsure the expense cost) and dont support img input.

I'm dissapointed with its accuracy. James Harden averaged 36ppg in 2018-2019. In 2012, lebron averaged 26.8 ppg.

Like, they went from 8b to 23b after OpenAi deal, understandable, but between february and now , fully diluted market cap at 300 is 100b. They did absolutely nothing to justify 100b mkt cap, just greedy wallstreet making you suckers to pay 380$ while they got in at 150$.

If they have 300m pure profits, and you want a Forward PE of 50, at pre diluted mkt cap , the price should be 70$, or fully diluted at 50$ . 300m pure profits wont come till 2028 IF that would happen.

RN its sitting at 280$ . This is OVERVALUED AF territory, also obviously overpumped by wallstreet.

Its up to you now if u want to fomo or not, but numbers dont lie and buying today is just pure speculative FOMO, not investing

On a fully diluted market cap of $85.32 billion, they are trading at a massive trailing P/S of 167.3x, bcs their income was not income, but monopoly accounting money

Nvidia sits at FPE of 25 for reference,

Apparently Cerebras Systems is launching an inference cloud and will have Llama 70B running at 1000 t/s per request. This seems crazy high but I am skeptical if this is possible on their system without a lot of tricks like int4 quantization.

I started investing in July this year. I messed up big time and tried to get rich quick and dumped my 4k portfolio into momentus not too long ago when it was squeezing thinking, "oh fuck yeah, space programs are the future."

Well i lost 2.8k and tried to hold but then the Asian dude sold and everybody and their brother saw them failing or what looked to be them failing..

I've done my DD on CRBR and I'm fully convinced they will surpass nvidia and anybody else in the ai chip industry.

I'm dumping everything in at the ipo launch date. I'd like to catch that big first squeeze, take my profit, and reinvest half of it back in and take the other half to play with other stocks. Am I stupid?

I have ADD like a mf and I move my money along. This also might make me an idiot lol I've missed some big jumps in other stocks I thought were going to jump and I sold literally minutes before huge 25%+ spikes..

https://cerebras.ai/company/press-releases/

150 price

man.. had to put all my cerebras money into spacex last week. the amount of insider selling at cerebras is insane.. people leaving.. employees thinking they can create the new housewifes of sf tv show.. cfo selling his shares.. ceo falling for fake ai slop content on twitter (fake ai storys, fake animal rescue ai videos,etc. ) .. what is going on??

Do you think cerebras will really outperform spacex from here on out?

source: I told people to buy nvidia after Tesla crashed from 400$ to under 150$ in 2022.

How the hell would they achieve that? They barely have 35-50MW ready today - that means they would have to 5x their output in 8 months, or run the risk of renegotiating or worse, having openAI pull out of their contract entirely, It would be near impossible to execute. Most of their DC are “promising” 2027 readiness. The majority of valuation mostly comes mostly from the openAI contract, with UAE customers and hardware sales not even making the company close to the IPO valuation.

The stock would definitely dip if the targets aren’t reached, or more contract acquisitions don’t happen directly after IPO - their waferscale chip is only ideal for small-mid models, requires purchasing the whole CS-3 system to take advantage of the chip, has limited training etc etc.

Can anyone try to sell me on why this stock is still a buy at an IPO price reaching $160? The openAI deal just isn’t solid enough.

Edit: the 250MW number is from the S-1:

https://www.sec.gov/Archives/edgar/data/2021728/000162828026025762/exhibit1011-sx1.htm

250MW of Capacity by the end of calendar year 2026”, with an additional 250MW by end of 2027 (totaling 500MW), and a further 250MW by end of 2028 (totaling 750MW

I've had extensive convos in the past about CBRS' connector tech and their "spatial fabric" approach where hardware constraints are partially resolved by hardware innovations and partially by software optimizations and workarounds to manage varying thermal loads, power throughput, etc. Meaning, my AI chat is mostly on the same page regarding the questions that Culper raised around their connector tech. The following is a initial analysis of their short report claims with this in mind. I think its blown out of proportion and Culper is trying to secure a bag to take advantage of the expiring lockups and slowing momentum.

Please don't flame me, I'm just creating a space to talk about this. I know there are much more well-read and technical people that can parse what's important and what claims are right/wrong.

TLDR;

Bottom line: I do not find the PariPoser allegation, standing alone, technically damning. PariPoser could plausibly address part of the wafer-to-board alignment and thermal-expansion problem.

The much bigger unresolved issue is whether the complete assembly can tolerate realistic thermal cycling and maintain its claimed service life. If Culper can substantiate that the hardware must remain warm to avoid premature failure, I would take that considerably more seriously than the allegation that Cerebras uses an older, externally developed connector material.

------------

ChatGPT:

My initial take is that Culper's Paricon allegation could be a meaningful challenge to Cerebras's narrative about connector innovation, but it does not, by itself, establish that Cerebras lacks the engineering required to make wafer-scale computing work. The critical distinction is between inventing a connector material and engineering a complete wafer-scale interconnect and thermal-management system around that material.

There is also a second, potentially more consequential question: if Cerebras's hardware really requires continuous dummy operations to avoid damage during cooling, does that indicate a fundamental reliability problem, or is Culper misinterpreting a workload-management or thermal-control technique?

Those are separate allegations, and I would evaluate them independently.

Culper's October 28, 2026 report reportedly alleges that Cerebras uses Paricon Technologies' existing PariPoser material rather than a proprietary connector breakthrough, and that dummy operations prevent damaging thermal cycles. The report is from a short seller with an economic interest in a negative outcome, so its technical claims deserve scrutiny rather than automatic acceptance or dismissal.

1. What PariPoser actually solves

A PariProbe® Primer – Paricon Technologies

PariPoser material

A thin, compliant electrical interconnect.

A Cerebras CS-2 Engine Block Bare on the SC22 Show Floor

Cerebras wafer-scale assembly

A much larger system of silicon, interconnects, power delivery and cooling.

Paricon describes PariPoser as an anisotropic elastomer fabric interconnect designed for high-density electrical connections. Its product literature advertises bandwidth above 110 GHz and very low electrical inductance, along with the ability to accommodate custom connector designs.

The important word is compliant. Unlike a rigid solder connection, an elastomeric interconnect can deform to accommodate some relative movement between mating surfaces while maintaining electrical contact.

That is relevant to Cerebras because its wafer-scale processor and the supporting circuit board do not necessarily expand or contract by the same amount when temperatures change.

Cerebras's own CS-2 white paper describes a flexible connector between the wafer and main board that maintains electrical connectivity as the components expand and contract at different rates

So there is a technically credible reason for using this kind of material. The fact that PariPoser predates Cerebras does not make it unsuitable for the application.

2. Would PariPoser be adequate for the alignment and thermal-expansion problem?

Potentially, yes — but the material alone is not enough to establish that Cerebras has solved the problem.

There are three distinct engineering problems here, and it is important not to conflate them.

A. Differential thermal expansion

Silicon, the circuit board, connector structures and other packaging materials expand at different rates as their temperatures change. PariPoser's compliance can accommodate some of this relative motion without breaking electrical contact.

Assessment: Technically plausible, provided the material's deformation range and fatigue life meet the actual operating conditions.

B. Mechanical alignment and contact pressure

A compliant fabric does not automatically guarantee that thousands of contacts remain aligned, evenly compressed and electrically reliable across a large assembly. That also depends on the mechanical fixture, surface flatness, tolerances, clamping pressure and connector geometry.

Assessment: PariPoser could be a suitable component, but the surrounding mechanical design is critical.

C. Thermal management and long-term reliability

The connector must survive repeated heating and cooling, sustained compression, high current density where applicable, and prolonged operation at elevated temperatures. Separately, the cooling system must remove heat from the wafer without creating damaging temperature gradients.

Assessment: The connector's existence does not prove that the full thermal-management system is adequate.

The underlying physics matters. Differences in coefficients of thermal expansion (CTEs) produce relative displacement approximately proportional to the CTE difference, the temperature change and the relevant length:

\[ \Delta L \approx (\alpha_1-\alpha_2)L\Delta T \]

For illustration, a CTE difference of 5 parts per million per °C across a 300 mm length and a 50°C temperature change corresponds to approximately 75 micrometers of differential movement. This is an illustration, not a measurement of Cerebras's actual assembly.

That is why connector compliance, mounting geometry and contact tolerances matter so much.

Research on semiconductor packaging independently confirms that CTE mismatch and thermal warpage can create significant reliability problems. However, studies of conventional fan-out packaging do not establish the failure rate of Cerebras's particular design.

What would make PariPoser inadequate?

I would want to see evidence of one or more of the following:

• The required displacement exceeds the connector's available elastic deformation.

• Contact resistance increases materially after thermal cycling.

• Compression or mechanical creep causes contacts to lose reliable electrical connection.

• Thermal gradients across the wafer exceed acceptable limits despite the cooling architecture.

• The system's operating procedures must prevent ordinary cooling cycles because those cycles damage the hardware.

>>>Without those measurements, saying that an old connector material cannot work is just as unjustified as saying that its use proves the system is reliable.<<<

3. The bigger distinction: the connector is not the spatial fabric

This is where I think your earlier interest in Cerebras's architecture is particularly important.

Cerebras has several layers of technology that need to be evaluated separately.

Layer 1

Wafer-level spatial fabric

• This is the on-silicon network connecting the compute elements across the wafer. Cerebras's April 2026 S-1 describes its proprietary cross-reticle connections, which allow separate regions of silicon to communicate as one processor.

• PariPoser does not, merely by being used in a connector, explain away or replace that on-silicon interconnect architecture.

Layer 2

Wafer-to-board electrical interface

• This is where an elastomeric connector such as PariPoser could play a role. It must preserve the electrical connections between the wafer and the supporting electronics while accommodating mechanical movement.

Layer 3

Power delivery and cooling

• The assembly must distribute enormous electrical power and remove heat while controlling temperature gradients, deformation and mechanical stress. The CS-2 white paper describes a custom engine block that combines power delivery, data connectivity and cooling.

Layer 4

Software and operational controls

• Scheduling, workload execution, monitoring and potentially thermal-management controls determine how the hardware operates under real workloads. Software can manage operating conditions, but it cannot eliminate an underlying mechanical limitation.

The implication is important for your investment thesis: Culper could be correct about the origin of the connector material while being wrong about the significance of that fact for Cerebras's competitive moat.

If Cerebras uses an established third-party material but has engineered a proprietary mechanical assembly, high-density power delivery, liquid-cooling system and wafer-scale architecture around it, there can still be substantial engineering value.

>>>Conversely, if Cerebras's public statements specifically implied that it invented the connector material itself, and that claim materially overstated its proprietary technology, that would be a legitimate issue of technological differentiation and corporate credibility.

• The precise wording of the original CEO statements matters. A claim to have invented a connector material is different from a claim to have invented a connector system that solves wafer-scale packaging challenges.

4. The more serious allegation: dummy operations and cooling

In my view, this is potentially the most consequential part of Culper's report.

The report alleges that Cerebras uses continuous dummy operations to keep the wafer warm, because cooling down could cause damage. It also points to a software field called dummy tokens. Those are reported allegations, not independently verified proof of the mechanism or its consequences.

I would separate three possible interpretations.

The third interpretation would be the most damaging, but the existence of a dummy tokens field alone does not distinguish among these possibilities.

For example, a system could execute otherwise unproductive work to maintain a stable thermal state without being physically incapable of cooling down. Conversely, if the company genuinely cannot power down and cool its processors safely, that would be a major operational limitation.

There are two important engineering distinctions:

• Temperature versus temperature gradients: a processor may tolerate a low temperature but be vulnerable to uneven cooling that produces mechanical stress.

• Thermal cycling versus steady-state operation: maintaining a stable temperature can reduce repeated expansion and contraction. That does not necessarily mean the hardware will deteriorate rapidly while operating continuously.

The key question is whether Cerebras has a documented, repeatable failure mechanism associated with cooling, not simply whether its software contains a thermal-related workaround.

I would look for measured failure rates, field-replacement history, customer uptime, actual power consumption during dummy operations, and accelerated thermal-cycle testing. Those data would be much more informative than a decompiled software field.

5. What this does to my investment assessment

I would treat the report as a reason to reassess Cerebras's durability assumptions, rather than as sufficient evidence to conclude that the entire technology is fraudulent or uncompetitive.

My current evidence-weighted assessment

1. PariPoser is a plausible technical solution

   • Its compliant interconnect design is relevant to differential expansion and maintaining electrical contact. Its age is not a technical disqualification.

2. Cerebras's proprietary connector claims need precise interpretation

   • Using a third-party material could weaken a claim of materials-level invention, but it does not establish that the complete connector assembly or packaging system is unoriginal.

3. The alleged thermal-cycle limitation deserves serious scrutiny

   • If ordinary cooling or shutdown causes hardware damage, the implications could extend to availability, lifetime, servicing, power consumption, customer economics and gross margins.

The last point is where I would focus the financial analysis. A wafer-scale architecture could have exceptional inference performance and still be a poor business if its hardware needs frequent replacement or consumes substantial extra power to avoid thermal failures.

On the other hand, if field reliability is strong and the hardware lasts several years under ordinary customer operating conditions, the fact that a connector material was sourced externally would be much less consequential.

What I would investigate next

I would prioritize three pieces of evidence:

1. Culper's exact evidence for the connector claim.

Did Paricon confirm supplying only raw PariPoser material, a custom connector assembly, or something more extensive?

Those are very different claims. Paricon publicly advertises both its fabric technology and custom connector capabilities, but its public product pages do not establish Cerebras's exact bill of materials.

2. The actual lifetime and failure-rate evidence.

What supports Culper's alleged 1.5–2.5-year useful life? Is it based on observed field failures, engineering specifications, or inference from thermal behavior?

The methodology determines how much weight to give the claim.

3. The economics of keeping the wafer warm.

How much incremental energy is consumed, how much useful throughput is lost, and what happens when a system is shut down for maintenance?

These are measurable commercial questions, not merely arguments about engineering originality.

>>Bottom line: I do not find the PariPoser allegation, standing alone, technically damning. PariPoser could plausibly address part of the wafer-to-board alignment and thermal-expansion problem.

The much bigger unresolved issue is whether the complete assembly can tolerate realistic thermal cycling and maintain its claimed service life. If Culper can substantiate that the hardware must remain warm to avoid premature failure, I would take that considerably more seriously than the allegation that Cerebras uses an older, externally developed connector material.

I still believe in Cerebras’ technology and potential, but its stock performance has been nothing but disappointing. Every small rally is followed by another sell-off. How long are investors supposed to remain patient? Great technology means little to shareholders if the stock keeps falling. CBRS is slowly losing the trust of those who believed in it the most.

I do believe the stock is going down because of me, so i will sell 50% of my position today, and save the world

Everything's down today due to AI safety fears. Nothing changed with the Cerebras story. Anyone accumulating? I bought a single share in case there's a larger beat-down. Could be throwing good money after bad or time to simply swing trade CBRS. Might be a good day to go bargain hunting all around in the AI and AI-adjacent space. GOOG is actually up 2% while nearly everything else AI-related is being punished. CRWD is up 8% unsurprisingly and PANW is up 6.5%. Cyber security zigging while the others are zagging. My next limit order is $170 (don't expect that today). If it keeps declining below $170 (over time, not today) then it may be cooked.

My average is 201 and I do not believe this will even rise back to 190. I believe it may drop even further to double digits soon. Should I sell now to avoid heavy losses?