I mean we kinda know the frontier models are multi trillion parameter models. The only open weights that are close to the frontier are that size too

The WSE is very expensive to build, and they have a waiting list of customers who are already willing to pay a lot of money for the available supply.

Without having any inside information, one possible theory:

All or a vast majority of of the cerebras manufacturing capacity was going to a few companies that aren't publicly available inference providers on openrouter, for their own internal use.

or

The asking price of the S-3, no matter how speedy it might be, for small/medium size customers made it economically prohibitive to purchase and use to sell public inference vs. buying more common nvidia b200 or whatever.

If you're willing to pay a significant premium for latency, why use openrouter? And anyway Cerebras only supported a few specific models.

Sol is supposed to be 5T according to rumour. The imminent Astra is allegedly 10

(Where did you see that?)

This was also interesting: "CS-4 delivers more than 1,000 tokens per second on models exceeding 10 trillion parameters." Was it known that there were 10 trillion parameter models in use?

I think the frontier providers keep the size of their models carefully hidden.

Maybe, but GPU is just one aspect of NVIDIA's dominance. If you are buying Vera Rubin GPUs, you're getting an NVL72 rack, which is only one of several racks that you're probably buying. You'll also need your NVIDIA racks with NVIDIA networking & storage gear, too. At the end of the day, they're "vertically integrated" for your accelerated computing data center (e.g. the "AI Factory"). This doesn't even count the software layer, where CUDA + CUDA-X (not to mention the software for all the sysadmin pieces) has a huge first mover advantage over anyone else.

it only takes ~445 GB300 NVL72 (about $22b) to run ALL of openrouter demand for a year. Microsoft rolled out $32b of DC 2026Q1.

imo the issue is that most openrouter demand is inauthentic activity (things that anthropic and openai models will refuse to do like pretend to not be bots when interacting with humans)

I’ll get that 250kW home power service dropped in next week!

They do offer API services to individual users... though with a set of models that makes it unlikely that you want to use it. They are promising Qwen 3.8 27B any day now though*.

if you have the money as an "individual user" to purchase one of their racks... save your money and retire.

* Actually they sent out an email claiming they already have it, but I don't seem to have access, they're promising to release it to the "shared tier" any day now.

An error no frontier LLM would make, eh

Nvidia is at this time a pretty well run company tech wise. They are going to keep iterating on the inferencing hardware stack over the next five years too.

Maybe it is just part of their "compact design".

Cerebras is only claiming ~2x the performance of Groqvidia which usually isn't enough for people to switch.

Sometimes I wonder if mistakes are now used to indicate the possibility that a human actually wrote it.

Information about RAM type/size and connection topology of the RAM to be used for context cache seems to be conspicuously absent from the slick looking marketing materials.

I think they run whatever models they get paid to run. But mostly from enterprise. They are clearly not interested in consumer dollars.

If they had ask Claude it would probably look like this: Introducing the all new Cerebras CS-4, a revolutionary rack-scale solution that delivers up to 30x faster inference compared to GPUs, enhanced economics, and a simple path to load-bearing hyper scale capacity.

"10x more throughput per watt than CS-3"

And, the software side isn't finished being optimized, either. We've seen with Qwen 3.8 27B and DeepSeek V4 Flash 0731 and GLM 5.3 that quite small models can pack a punch. Intelligence density will improve, efficiency of kernels will improve, efficiency of KV caching and MTP will improve, algorithms for splitting workloads across compute units will improve.

It'll all be as cheap as DeepSeek was before the price hike. And, it'll become more and more realistic to run near-frontier intelligence on personal devices.

162 kW

Hence why taalas was one of the best strategic acquisitions of the year.

I'm honestly baffled they were not acquired by somebody else (sorry AMD).

Congratulations! You have just realized that the AI data center build out is a total scam, built on both the insurmountable trillions of debt, and the assumption that only GPUs are all we need to continue scaling.

There exist other AI accelerators (TPUs, ASICs) that perfectly exceed the throughput that LLMs need to scale as well. But the true solution is more software optimizations. There's a tiny handful of them but more needs to be discovered so that we can reduce building hundreds of more data centers as the alternatives mature.

As better software becomes more useful for the alternative AI hardware for developers with LLMs running efficiently you then would have more choices of hardware to run your LLMs on rather than just only GPUs.

This is part of why I think the data center build-out is a bubble. We've barely scratched the surface when it comes to hardware optimization. We'll see exponential improvements in energy efficiency and speed over the next decade. Exponential, not linear.

GPUs really aren't that great for AI. They just happen to be the best chips we have in mass production right now for this work load, and it takes time to field new designs. Basically every chip engineer on the planet is working on this right now.

Is CUDA still a moat? Are we not at the point where frontier models can reimplement software stacks, given you throw enough tokens at the problem.

It is worth mentioning, the OpenRouter demand isn't static though. It has increased week on week since early 2026.

For now you can rig an adapter to your nearest DC EV charging station, but make sure it's near a body of water for the cooling.

I was curious so I looked it up: looks like a GB300 NVL72 is about $4M. So $22B would buy you 5500 such racks, no?

Rumors and allegations aren't worth much. Why don't they just tell us mere mortals?

It's rumored fable is around that 10T number

There’s been a spate of Reddit AI bots using all lower case in hopes of evading detection.

It’s still incredibly obvious.

I mean the product is a server rack and while there's no advertised price I would assume it's six figures. So yes, an enterprise product.

But even an enterprise is going to care about the difference between "we can run the model we want with support from the manufacturer" and "we have to purchase the product, and then spend another 6 figure sum having developers port a recent model to the product to use it".

> save your money and retire.

Now that this hypothetical person has retired, what are they gonna do all day? Just sit on the beach and drink Mai Tais? If that's what they wanna do, sure, but nerds gonna nerd, and if I had that kind of money to retire on, I'd totally buy some ridiculously expensive AI box for fun.

There's a few more details at the bottom of this page: https://www.cerebras.ai/blog/introducing-cerebras-cs-4

44GB on-chip-sram * 3 chips. Per chip: 43.2 PB/s memory access + 53.5 PB/s on-chip fabric bandwidth + 2.4 Tbits/s "IO" bandwidth (I think that means their RoCE v2 RDMA over Ethernet interface).

I suspect there might be a certain amount of customization for how much RAM they attach when you order it.

True, I have to agree with you. The AI giants might be investing a huge amount of money in generation 1 technology. There might be a much better way to do it just around the corner. They might know this and thus the hurry to IPO.

A rough analogy would be if the first generation of ISP's spent billions on dial-up exchanges, when fibre could be invented next year.

Huh, why I'm not surprised that HN is full of opinions confidently stated without any numbers or resources to back up?

> built on both the insurmountable trillions of debt, and the assumption that only GPUs are all we need to continue scaling.

Insurmountable according to whom? And who assume that only GPUs are all we need to continue scaling? Google, Amazon, Microsoft, Meta and OpenAI, all have or plan custom non-GPU AI chips. Do they plan to use them not for scaling?

I wonder: in world where inference is cheap, how many engineering agents that use simulation as their feedback we will use?

In the scenario, engineering everything becomes so easy - so why not optimize everything? every component, every product, every system?

And maybe llm's could invent. So even more to simulate. And simulation is inherently compute-heavy.

So unless there are some other bottlenecks, we'll use a lot of simulation servers.

Taalas will be one of the great disaster investments of the early AI era. It'll be a near total write-down.

The absolute worst market time to etch a model to a chip is right now (very rapid iteration). There is no scenario where they can keep up. The Taalas approach will be viewed as comically foolish within just a few years.

Cerebras will win in terms of approach.

It's 1998: hey, I can drastically speed up your web service, let's etch it right to silicon.

God, I was going to ask if this could be deployed in a standard existing datacenter, but I guess that answers that question.

By the time these gigawatt datacenters are done being built the hardware will be so far behind state of the art they may be mostly useless.

Whether it's a bubble or not depends on how much the demand for compute and the type of workload keeps growing, though.

If AI tends to be something used mainly in ideation and development, which is how a lot of people use it today, then once consumer hardware gets good enough you could see a bunch of the current data centre workloads move onto consumer devices.

But if AI starts being used more in repeatable, operational workloads I think it makes sense to have significant cloud infrastructure for it. TBH I haven't seen much of this, and I've been skeptical about people using agents for much of anything when it can be done with just software. But we are starting to see more of this kind of workload, like the taggable Claude in your slack etc that people seem to really love.

That's unusually honest and the sharpest thing in this thread.

I presume per rack?

Can you imagine something radiating that much energy into a space in your home?

I wonder what this looks like in 5 years... Will there be a massive push to repurpose these giant boxes into housing? Will they get turned back into the farm land from where they came? When a data center goes bust, what happens to the parts left behind?

TPUs and ASICs run in data centers too. Your argument only holds true if there's some satisfied limit to demand for inference. If not, data centers will continue to spring up to host more and more agents. Even if agents were running on hardware and software as efficient as the human brain, its conceivable we want trillions of them running at any given time which would require data center scale.

I guess we know why there's a fair bit of investment money going into small modular nuclear reactor startups now.

On the plus side, lots of cheap servers to swoop up :)

I don’t really visit Reddit much these days but would love to see an example.

Why would they? What the upside, for them?

If this is true, it's even more impressive that some of the open weight models that are <3.5T in size, approx 33% of its size, are within a few points of it in the artificial analysis leaderboard.

Ah but if you have the kind of money where this is a reasonable retirement hobby purchase, you aren't bothered by representing yourself as a "enterprise" :P

They have managed to make the external link 300GBps/2us. Cs3 was 150/5.

This is 1/3rd blackwells nvlink c2c bandwidth already. Not too bad. We can make KV cache offload work with that I suppose.

If magically KV cache was not an issue, pipeline parallelism on cerebras can be quite pleasant. As for the KV cache offload, I have hopes their CPO solution they're trying with that canadian company ends up bearing fruit.

You’re at least an order or magnitude under… likely two.

A single AI server with a mere 8 GPUs from Nvidia is already mid 6 digits. A rack system from Nvidia is mid 7 digits.

There’s some info out there that suggests the CS1 had an 8 digits price tag, so it wouldn’t be surprising to see that here.

I feel like 6-figures would be the clearance price on it...

You're underselling it, and here's why.

I guess because I have actually set foot in a data center I don't imagine literally every product in my home.

I'd think the infrastructure would tend towards factories, smelters, and so on. Industrial things that have reasonably high power demands, can use the building, and don't care about the lack of windows.

They're typically not built where you want housing, and the buildings are distinctly the wrong shape.

If you can't use the power infrastructure profitably my next thought would be warehousing.

But also... we've seen a pretty continually increasing demand for compute. Even if AI busts a bit (or becomes a bit more efficient) I bet most data centres stay data centres, just less profitable ones.

And advanced geothermal. Fervo Energy let's us get energy that's not based on burning fossil fuels but is, instead, able to produce energy from the ground.

It's mandatory liquid cooling, so it's meant to be attached to a specialized liquid cooling loop that gets the heat outside the building.

This is far beyond the practical maximums of like 10 to 15kW per 44U cabinet front to rear air cooling for 'regular' rackmount server stuff.

Everything has some satisfied limit to demand, often depending on the price. If you assume there will never be any satisfied limit to demand for inference at any price you can justify any investment.

Yeah, its not like this js some sort of Open AI company. That'd be ridiculous.

You want to take a look at the "Scaling Laws" paper, so you can extrapolate from these numbers.

I don’t think they will until they change the architecture.

They don’t have a prefix cache like other providers, or at least don’t have a discount in their billing structure. Each message charges for the whole context window. It’s wildly more expensive for long multi turn scenarios with lots of tool calls (coding). It’s better for short few turn tasks.

Edit: I don’t know if they actually have a proper cache. This could just be a billing artifact.

But power hungry.

In that 5+ year timeline, the compute per watt could change by three orders of magnitude.

GPUs are to LLMs what CPUs are to gaming — not a good fit.

Not necessarily, there could be diminishing returns on mere parameters count .

There is nothing to say for example a 1 Quadrillion parameter model will be vastly more intelligent than current SOTA especially since new training data is largely synthetic today

Surely it would depend on your model, since they need to etch this into silicon.

> GPT-OSS 120B which is nigh useless nowadays:

I still think that was a really great model that got overlooked. It was really great in terms of latency/throughput while still being fairly intelligent.

I was planning on using it for a design tool, but moved over to luna since it's comparable speeds and cost for a lot more intelligence.

True, it's also "per user", somehow, but I think it's a misleading metric. Cerebras chips take the whole wafer?

A single TSMC wafer contains 60 to 65 B200s, assuming 70% yields that's 40ish wafers per die.

Cerebras cannot redefine wafer economics.

GLM 5.3 is "only" 753B parameters. Much much smaller.

Yeah, did some googling after WarmWash's comment and I concur.

With what? More debt? What will nvidia say?

6 figures is a single mid range Xeon or Epyc server these days.

Do you know about Jalapeno?

Looking back nearly 80 years, what has been the limit to transistor demand so far?

Unlimited.

What has been the limit to electricity demand globally?

Unlimited.

We can't get enough and never will. Costs have to become pretty severe to turn back the demand as well.

Indeed. You need 45 to 60 liters per second of cooling water flowing over a Cerebras wafer every minute to keep it under 90C. And that’s assuming the water leaves at 90C…

More realistically, you need much more cooling water.

Yeah, but there's certainly a part of the curve where price drops by X OOMs and demand increases by much more than X OOMs. (Presumably some of that is substitution and some of that is new use cases.)

A cursory estimate courtesy of ChatGPT suggests that there is a grand total of one order of magnitude or less of power efficiency improvement available compared to current Blackwell if the entire system’s power consumption outside the ALUs went all the way to zero.

If you want three orders of magnitude improvement, you probably need to find two of those orders of magnitude somewhere else: process improvements, different ALU design, model architecture changes, etc.

That's precisely what he is saying, there is diminishing returns (or optimization left on the table).

I read it as it is impressive because smaller models 2.5T are squeezing similar returns as 10T models despite being 1/4th size not that there beyond 2T today the number or parameters do not have much meaning

You're thinking of Taalas.

gpt-oss-120b is absolutely unusable over Cerebras. It fails to call tools half the time and just continues to think about what tool it'll call repeatedly. Like it says it'll call a tool and then it doesn't, and then it says it'll call the tool again and then it doesn't, and it just does that in a loop forever. It's awful. Also forgets to end the thinking block too. Even if the model itself was just-okay for its time, even at 1000t/s+ it's not worth it. And it's EXPENSIVE, like $5 per minute expensive

Depends on what you mean, they have more redundancy which means that the yield can be much higher.

> I was planning on using it for a design tool, but moved over to luna since it's comparable speeds and cost for a lot more intelligence.

Everyone should occasionally go back to the old models to see how much worse they were, like even a year ago you could generate results but they were typically full of bugs and you have to fix a non-insignificant amount of it all manually: https://blog.kronis.dev/blog/i-blew-through-24-million-token...

Admittedly that post was before agentic development truly took off and that 3k EUR figure when paying per API tokens would nowadays be closer to like 6k EUR for the volume of work I do, but still.

It's the same how Qwen 2.5 was pretty problematic for anything remotely serious, same with Qwen 3 Coder Next (80B), and at least the most recent versions are getting better but still not quite good enough in real world use cases outside of benchmarks. They've come a long way, regardless!

As MoE with 5B active parameters it's pretty fast. But you still need a lot of vRAM, or have to run small quantitations. Qwen models just gave you more bang for your buck, and the gap became worse with every qwen release

^

OpenAI is partnering with Cerebras while simultaneously investing in their own silicon play. Hedged bets.

After sitting thru their keynote today, it makes sense. The main throughput speedups they tout are an obvious evolution of the GPU that all companies will be building in the next year. Wafer-scale interconnected memory and compute is just going to beat out mountains of network cabling any day on both cost and performance metrics.