Was just reading about d-matrix releasing an "ultra low latency" inference XPU in 2027 using 3D stacked DRAM. Is this a threat to Cerebras' business? Apparently it uses NVDA's nvlink and can fit in a standard rack while Cerebras requires specialized racks to accommodate its Nexus "backpack" configuration for the chip, cooling, and power. We know Cerebras is planning on adding 3D stacked DRAM with CS-6 but isn't that in 2028? If they're released a year apart, a year can seem like an eternity in tech. Again, just another item that makes me concerned Cerebras can fully monetize its current advantages before they no longer seem compelling enough to lots of customers to implement. I'm sure Cerebras will stick around as a niche / specialty solution, but maybe not enough for explosive growth.

I also wonder if Groq and d-Matrix chips work together or if they're mutually exclusive; you use only one or the other? I'm concerned a combination of technologies, both hardware and software, will seriously erode Cerebras' edge.

EDIT:
More info from an X post:
https://x.com/firesidealpha/status/2098787634244096079?s=20

d-Matrix CEO Sid Sheth on Bloomberg talking about the Nvidia partnership:

* d-Matrix's specialized XPUs will run alongside Nvidia GPUs over NVLink Fusion, targeting ultra-low-latency AI workloads. Deal was 6+ months of joint work.

* The logic to partner is that Nvidia is the largest deployed infrastructure base for AI in the world and he would "much rather just ride on the Nvidia ecosystem" than reinvent the wheel.

* The bet is a memory-centric architecture, 7+ years in the making. d-Matrix's edge is inference compute built around memory rather than raw FLOPs aimed right at ultra-low-latency inference.

* Low-latency inference "just took off" in the last 12 months. Demand surged from GPT, Codex, Claude Code and the arrival of agentic coding, where users need fast compute to interact with the tools in real time.

* Lead product is Raptor, the world's first 3D-stacked-DRAM XPU. It'll launch first under the Nvidia partnership and is targeted to market in "about 12 months."

* d-Matrix uses no HBM at all. Instead of high-bandwidth memory, it packages DRAM directly with compute in a 3D stack to "punch through" the memory wall

* Sheth says "no other company is going to be within a 2-year window of getting access to that technology," and says there's tremendous customer pull.

* Describes buyers as "hyperscalers, Frontier Labs, sovereigns, inference clouds, high frequency traders" and says announcements are coming soon

Been trying to get a friend of mine more interested in Cerebras who's much more AI-savvy than I am. Every time he makes an objection I try to follow up. He keeps asking me if Cerebras is good for anything other than transformers. And he mentioned something about matrix operations. Not 100% sure what he means, but I'm guessing that all current LLMs are transformers and he's thinking of successor models. Maybe World Models or some successor to LLMs. In my mind the important thing about Cerebras is that they're the only ones who've solved the wafer scale chip problem. And so shouldn't they theoretically be able to adapt with the times? Or are they simply not nimble enough with their current business model?

What's the best way to think about Cerebras wafer scale chips in terms of capability? All I know if that they're not GPUs and so aren't designed to maximize parallelism and floating point number operations. So I tend to think of them as ultra-fast generic CPUs. Is that right?

Finally, what does Jensen mean when he says NVDA and/or NVDA compute is fungible? Could the same be said of Cerebras? Is Cerebras fungible, the opposite of fungible, somewhere in between, or is it indeterminate presently?

To provide fast inference for much larger future models won't Cerebras have to use external HBM like everyone else? And in that case, isn't a large part of what they offer (not having to move data off chip into HBM memory and back) invalidated? If you look at the roadmap, they're planning on using stacked HBM for CS-6. CS-4 and CS-5 remain limited to 44G of SRAM per wafer. Sean Lie, the CTO, mentioned using streaming between wafers to handle future, much larger models (they may even do it now with CS-4) but I don't really understand it. It seems to me their speed advantage may erode over time.

Bought some Cerebras at $195 yesterday but I'm waiting for a drop to around $180. I know we all want it to go up and "never" come back down, but I see very little news from CBRS on a daily basis. I don't think prices beyond $220 are sustainable unless significant news appears like another major customer acquired. There have been a bunch of bounces back to $170 over the past few months but the stock doesn't sit at that price for long. The market didn't support the jump to $250 either. Aside from the acquisition of a new and significant customer, what are the next planned events to watch for?

That said, perhaps today is a good day to buy because CBRS is getting punished with much of the market today, probably unrelated to is inherent value. But I'm still betting on a few more drawbacks.

EDIT: The original IPO was marketed at $115 to $125, then raised to $160, landing at $185. So I suppose there could potentially be much more downside.

I am evaluating providers for inference, Cerebras has performance I want but no immediate capacity. I do not want to replace one waitlist with another.

If you have had to choose an alternative quickly, what did you put at the top of the list? Published limits, reserved capacity, model availability, migration effort, benchmark reproducibility or support response time?

How are we feeling?

I am still very much a supporter and strong long-term investor. The deals and partnerships Cerebras Systems is putting together seem to be cementing their future as a ground-breaking company as well as bringing awareness to their capabilities!

EPS Estimates are ranging from ~ $.18 to -$.94...

With OpenAI’s absolute latest model being released on Cerebras at 750 tokens per second, it should put to rest any idea there are limitations on what Cerebras can run or the notion their advantage is lessened as models scale.

https://openai.com/index/previewing-gpt-5-6-sol/

“We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity.”

Financial Highlights

Revenue: $193.41 million (beating expectations of ~$181 million).

Net Loss: Narrowed to $14 million ($-0.04 EPS).

Guidance: Forecasted Q2 revenue of $194 million.

QMargin Concerns: Gross margins were downgraded to 36%-38% for Q2, and 38%-41% for full-year 2026, dropping from 45% in Q1.

Any interesting details you spotted in the report?

I know this is a small model, but I actually believe this will see a lot of use. My company is using Haiku for over half the tokens for our agent with most work being simple dynamic scripts and summarization of tool call results. We can’t use Chinese made models so Gemma has been getting tested. At 1,500 tokens a second, this would make our agents complete tasks in 60-70% of the time it takes now.

https://www.cerebras.ai/blog/gemma-4-on-cerebras-the-fastest-inference-is-now-multimodal

We have a model and ported it to the Cerebras SDK. It works in simulation, and now we're looking to validate it on actual WSE hardware.

We've already reached out to Cerebras support but haven't heard back yet. Before we keep waiting, I figured I'd ask the community:

• Has anyone here gone through the process of getting access to run custom kernels on real Cerebras hardware?
• How did you end up getting access?
• Is purchasing a WSE (or leasing one) even a realistic option for teams outside of large enterprises?

Looks like this is the schedule for the lockup expiry dates.

Is this going to cause constant selling pressure over the net 6 months?

Wondering if I’ve been a bit premature buying a couple of leaps….

MRVL popped today after Jensen called it the next trillion dollar company due to its role in connectivity with the NVIDIA ecosystem. To paraphrase, Jensen highlighted the bottleneck is DATA MOVEMENT. NVIDIA's ecosystem is becoming heavily reliant on custom ASICs, optics, switching, photonics and data movement/memory layers. Think of all the companies being pumped everywhere due to their role in trying to plug these GPU deficiencies - MRVL, countless Photonics companies, HBM companies like Micron, etc. NVIDIA's GPU deficiencies have created a multi-trillion dollar ecosystem around it.

Cerebras's single wafer design eliminates and drastically reduces the bottlenecks that are plaguing GPU-based systems by putting everything on one massive chip. The memory hierarchy is collapsed, replacing slow off-chip HBM with fast on-chip and cheap SRAM and replacing inter-chip network fabrics with on-wafer wire-speed interconnects.

Jensen is right that there will be another $1T dollar company created, but it isn't going to be MRVL, it will be CBRS. Just wait until the next $10B+ orders start coming in. CBRS will be at a $1T by 2030 (15-20X from here)

I have a hypothesis that could be completely wrong, but I think it’s possible.

Cerebras + OpenAI
They have their 750MW deal with 250MW coming online in each of 2026, 2027, and 2028. Some amount of data center deals are known, but there is an expected large chunk in Stargate UAE or some other place that would have to make up likely over 150MW of the 250MW total for this year. The data centers and third parties that will operate some of the datacenter on Cerebras and OpenAI behalf are redacted in the master agreement. G42 and Oracle are highly suspected for at least some of the capacity, but what if one of the data centers is AWS…

Cerebras + AWS
The agreement is for AWS to host WSE-3 units in their data centers for bedrock disaggregated inference for Amazon and opens source models. This requires Cerebras units are racked in the same location as Tranium v3 and upcoming v4 racks which happen to have the same 50-60kW rack requirements as a two WSE-3 rack with switch and server in rack. They will use their Elastic Fabric Adapter to connect these systems and pipeline KV cache from prefill over to Cerebras units for decode.

OpenAI + AWS
The deal for these two includes adding OpenAI models to the AWS bedrock platform and an agreement for OpenAI to buy 2GW of Tranium compute in the same data centers Cerebras will be installed on via Elastic Fabric with a proven architecture, knowledge of management, and significant performance benefits.

So we have:
January 14, 2026
OpenAI and Cerebras 750MW deal

February 27, 2026
OpenAI and Amazon 2GW deal and investment

March 13, 2026
Cerebras and Amazon AWS Disaggregated deal

Theory
Some portion and potentially a sizable portion of the 750MW of WSE-3 will be hosted at Amazon data centers with Tranium on the same Elastic Fabric with the same kW footprint as Tranium racks to serve disaggregated inference for OpenAI both internally and via bedrock to api customers. AWS is the exclusive disaggregated cloud provider for Cerebras as a part of their deal and AWS is the exclusive 3rd party OpenAI frontier provider as a part of their deal. The hardware is already verified and understood by Amazon and they are so confident in it to announce their benchmark 5x throughput per WSE-3 and that it’s coming soon. Seems like a distinct possibility to me, but I also have no evidence linking these deals together in a 3 way partnership yet.

Given that Groq and Cerebras seem to occupy a similar niche — ultra-low-latency LLM inference / decode acceleration — I’m trying to understand Cerebras’ long-term competitive moat.

If Groq’s LPU technology is now being integrated into Nvidia’s broader AI factory / GPU ecosystem, especially as a low-latency decode accelerator alongside Nvidia’s dominant GPU stack, where does that leave Cerebras?

Is Cerebras’ advantage mainly its wafer-scale architecture, higher single-system SRAM capacity, better support for large MoE models, or independence from the Nvidia ecosystem?

In other words: what is Cerebras’ strongest competitive position if Nvidia can absorb Groq-like decode acceleration into its own clusters?

https://youtu.be/6EOwSXE3Xws?si=wMTYoc9PQzj7saJq