Cerebras (CBRS) Q3 2026 Earnings Call Transcript

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DATE

Thursday, Nov. 12, 2026

CALL PARTICIPANTS

• Head of Investor Relations - Sean Dorsey
• Co-Founder, Chief Executive Officer and President - Andrew Feldman
• Chief Financial Officer - Bob Komin

TAKEAWAYS

• Core Total Revenue -- $230.9 million, reflecting a 70% increase driven by record demand for cloud and services.
• GAAP Total Revenue -- $210.8 million, representing 55% growth compared to the prior year.
• Core Cloud and Other Services Revenue -- $144.9 million, growing 272% due to increased adoption by AI native enterprises.
• Core Hardware Revenue -- $85.9 million, reflecting 5% sequential growth from expanding system shipments.
• Core Gross Margin -- 41.0%, representing a 2 percentage point improvement from the previous year.
• Core Cloud Gross Margin -- 46.9%, increasing 510 basis points sequentially due to new business delivered at higher pricing.
• Core Hardware Gross Margin -- 31.0%, reflecting the final burn-off of legacy hardware purchase agreements with special pricing.
• Core Net Income -- $30 million, marking the first time this metric turned positive since the company's initial public offering.
• Remaining Performance Obligations (RPO) -- $26.1 billion, representing the balance as of Sept. 30 and excluding the recently signed Oracle agreement.
• Total Liquidity -- $7.8 billion in cash and marketable securities, providing flexibility for rapid private cloud build-out.
• Data Center Capacity -- 700 gross megawatts under contract for delivery by late 2027 to meet high cloud demand.
• Manufacturing Capacity -- A tenfold increase expected by the end of 2026 to support anticipated growth in system sales.
• Q4 2026 Core Revenue Guidance -- $310 million to $330 million, reflecting sequential growth approaching 40% driven by new hardware shipments.
• Q4 2026 Core Gross Margin Guidance -- 47% to 49%, driven by hardware sales at higher current prices.
• Full Year 2026 Core Revenue Guidance -- $940 million to $960 million, raised from previous outlooks due to strong execution.
• 2027 Revenue Target -- A projected tripling of total revenue compared to 2026 levels, supported by expanding manufacturing and data center capacity.
• WSE-5 Processor Development -- Less than five months from tape out to system testing, reflecting execution on the fifth-generation platform.
• Disaggregated Inference Efficiency -- Technology designed to increase token generation by up to fivefold per megawatt of power.
• Q4 2026 Core Operating Margin Guidance -- (1%) to 1%, representing a significant improvement from the (14.3%) loss in Q3.
• OpenAI Cancellation Risk -- Less than 5% of total committed megawatts are subject to potential cancellation this year due to capacity delivery delays.

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RISKS

• Feldman stated, "we find ourselves running behind schedule on initial tranches" of data center capacity for the OpenAI agreement, which has resulted in potential delay credits recorded against remaining performance obligations.

SUMMARY

Cerebras Systems Inc. (NASDAQ:CBRS) reported record performance for the third quarter of 2026, led by a significant expansion in its cloud services business and the addition of three hyperscaler partners in the last six months. Management reported that the company achieved positive core net income for the first time and raised its guidance for the fourth quarter and full year. The company reported a dual-channel strategy involving direct hardware shipments and first-party cloud offerings, with an emphasis on securing 700 megawatts of data center capacity to support its high-speed inference technology. Management indicated it expects to triple revenue in 2027 as next-generation hardware enters production and international facilities come online.

• The company announced a new cloud agreement with Microsoft, expanding its footprint to three hyperscaler partnerships including existing relationships with AWS and Oracle.
• Management reported that over 75% of new data center capacity in 2027 will be located outside the U.S., with facilities planned for Canada, France, Norway, Finland, and Brazil.
• The company is scaling its manufacturing partnerships with Flex and Sanmina, targeting a tenfold increase in system production capacity by the end of 2026.
• Cerebras launched production-scale disaggregated inference, which uses AMD GPUs for the prefill stage to improve throughput efficiency across its cloud and for select customers.
• The company acted as a day-zero partner for Moonshot AI's Kimi K3.1 model, achieving an industry-best performance of 2,000 tokens per second.
• Feldman stated that the platform's architectural design avoids the use of high-bandwidth memory, which he believes will face continued shortages and price increases through 2028.

INDUSTRY GLOSSARY

• Core Revenue: A non-GAAP measure that excludes pass-through revenue and non-cash amortization to reflect underlying business performance.
• Disaggregated Inference: A method of processing AI models where the prefill and decode stages are handled by separate, optimized hardware components.
• Fab Yield: The percentage of chips on a wafer that are functional following the fabrication process.
• Parametric Yield: The percentage of chips that satisfy specific electrical and performance requirements such as speed and voltage.
• RPO (Remaining Performance Obligations): The sum of the transaction price for contracts with an expected duration of more than one year that has not yet been recognized as revenue.
• Take-or-pay contract: A contractual provision requiring the buyer to pay for a specified minimum amount of goods or services regardless of whether they are taken.
• WSE-5: The fifth-generation Wafer-Scale Engine processor, which occupies an entire silicon wafer to provide high-speed AI compute.

Full Conference Call Transcript

Operator: Good day, and thank you for standing by. Welcome to the Cerebras Systems Third Quarter 2026 Earnings Conference Call. [Operator Instructions] Please be advised that today's conference is being recorded. I would now like to turn the conference over to Sean Dorsey, Head of Investor Relations. Please go ahead, Sean.

Sean Dorsey: Thank you, operator. Good afternoon, everyone, and welcome to Cerebras Systems Q3 2026 Earnings Call. Earlier today, we issued our press release and posted our supplemental earnings presentation to the Investor Relations section of our website. A replay of this webcast will also be available on our Investor Relations website following the call. Joining me today are Andrew Feldman, our Co-Founder, Chief Executive Officer and President; and Bob Komin, our Chief Financial Officer. Before we begin, I would like to remind everyone that today's discussion will include forward-looking statements under the safe harbor of the Private Securities Litigation Reform Act of 1995.

These statements include, but are not limited to, statements regarding our future financial performance, business strategy, market opportunity, customer demand, product road map, technology leadership, supply chain, operating model and outlook for Q4 and full year 2026. Forward-looking statements are based on our current expectations and assumptions and are subject to risks and uncertainties that could cause actual results to differ materially from those expressed or implied. These risks are described in our SEC filings, including our final prospectus related to our IPO and our future periodic filings with the SEC. We undertake no obligation to update these forward-looking statements, except as required by law. During today's call, we will also discuss certain non-GAAP financial measures.

Reconciliations between GAAP and non-GAAP results are included in today's press release and supplemental materials available on our Investor Relations website. With that, I'll turn the call over to Andrew.

Andrew Feldman: Thank you, Sean, and thank you all today for joining us. As we look to the months ahead, we see 2 certainties: rapid change and compute shortages. In the last 90 days, the AI ecosystem has seen the title of leading frontier model change hands multiple times. It has seen dozens of interesting open models released, major security incidents occur, and powerful new consumer agents emerge, and many other developments that none of us could even remember. It's impossible for anyone to predict AI capabilities 90 days from now, let alone a year from now. Yet through all that change, intelligence and speed remain the 2 essential features for every use case from coding to security to personal agents.

At Cerebras, our job in this rapidly changing world is to make sure that no matter how AI grows, Cerebras grows. As foundation models grow, we grow; as open models grow, we grow; as personal agents grow, we grow; whether enterprises or startups grow, we grow; and whether customers want hardware or cloud capacity, we grow. Our goal is to position ourselves so that whatever drives the industry, Cerebras wins. In Q3, we made remarkable progress towards that goal. First, I'd like to report that we exceeded all guided business metrics.

And while I'm pleased with these results, I believe even greater importance is the fact that we closed a number of deals with key AI industry leaders since our last earnings call. With the latest being our announcement that today, we signed an important cloud deal with Microsoft, which along with the AWS and Oracle announcements previously made brings us to 3 hyperscalers who chose to leverage Cerebras platform as part of their inference strategy. These new deals alongside continued growth and execution with our existing customers enable us to raise Q4 core guidance across the board and continue to see revenue tripling in 2027 with expanding gross margins.

Bob will share all of the details regarding our Q3 performance and forward-looking guidance in his section. Let me take a minute to clarify the shape of our business. As an AI accelerator company, our pioneering innovations are in the chip systems and software that make AI compute fast. We are an AI hardware company with 2 channels to market. First, we sell systems, installing racks and Cerebras hardware in our customers' data center. This is a great business. Revenue is recognized quickly and cash is received quickly. It is also lumpy and depends on customers having data center capacity ready to receive the hardware. Our second route to market is through our cloud.

There's a very rapidly growing category of AI natives who want to consume tokens rather than deploy hardware. And given the fact that data centers and compute are in very short supply, the fact that we have both, the system and the data center is a strategic advantage. The AI cloud business is a particularly good business if you make your own hardware. Our cost per megawatt is much lower. And as such, we can bring more capital efficiency than neo-clouds. The mix of these 2 channels will vary from quarter-to-quarter, but we believe both will grow significantly over time. Let me also address speculation about our OpenAI partnership.

OpenAI is a very large customer and a close partner, and I believe they will continue to be so going forward. OpenAI grew in Q3 as compared to Q2, and we expect more growth in the future. The success of OpenAI's Ultrafast Sol 5.6 Preview based on Cerebras was overwhelming, demonstrating unequivocally that the demand for fast tokens is enormous. It is also true that Cerebras didn't have sufficient capacity to support Ultrafast in a broad rollout, including for Astra. As others have stated, we believe that capacity is a key driver for business decisions in our industry. In fact, even with the enormous supply of GPUs available to OpenAI, usage-based restrictions still needed to be implemented.

After signing the agreement with OpenAI, it took us longer than we would have liked to find contracts for and bring online the substantial increase in data center capacity required to meet the aggressive requirements in the agreement. And as such, we find ourselves running behind schedule on initial tranches. Given the importance of this partnership to our business, we understand the close scrutiny it gets from investors. The details of the agreement or news about it are unlikely to come from any source other than Cerebras or OpenAI. For example, there were rumors that Cerebras couldn't support Astra for technical reasons, these are false.

Our CS-3, CS-4 and CS-5 are more than capable of supporting Astra scale models and larger models as well. At the same time, I expect and you should expect OpenAI to build capacity using a range of hardware solutions. OpenAI will retire older models, deploy new ones across those compute options for internal and external use. Their compute needs are vast with plenty of opportunities for different solutions to play a role as they scale. Before turning to our business framework, let me make one more general point. Despite the experimentation, low batch GPU, start-up announcements, mega kernels and hyperscalers' internal custom silicon, we continue to be the fastest inference solution partner.

We believe our road map will enable us to maintain that advantage for years to come. Our framework for evaluating progress remains unchanged. We assess our business across 3 dimensions: customers, capacity and capabilities. Let's begin with customers. We are rapidly expanding our customer base to broaden our reach and diversify our revenue stream. Q3 was a successful quarter to that end. We had an important win with 2 new hyperscalers. We signed an agreement to bring Cerebras hardware into Oracle Cloud where Cerebras will be deployed in Oracle data centers serving AI labs, enterprises and developers building interactive and intelligent applications. We expect this Cerebras-powered Oracle Cloud inference service to launch in the summer of 2027.

We also signed an exciting deal with Microsoft, about which we will share more next year. That brings us to 3 hyperscaler wins in the last 6 months. This is an important achievement and it positions us well for 2027 and 2028. We also extended our reach through a variety of neo-clouds with off-takers of high caliber like Fireworks, diversifying our routes to market and giving customers more ways to access Cerebras high-speed inference. We also benefited from open weight models, which continue to enable new applications and expand what AI can do in everyday life and across the industry.

For example, just this week, we announced that Cerebras Fast Inference is powering the Bilt Neighborhood OS platform and personal assistant, bringing blisteringly fast inference to millions of people. With consumer apps like Bilt, we're putting the power of Cerebras directly into people's pockets. That is why fast tokens command a premium and our cloud customers tell us that fast tokens generate exceptional gross margins and that Cerebras fast tokens are the most profitable tokens they sell. Let's now turn to capacity. Capacity has 2 components: data center capacity and manufacturing capacity; both are critical to growth. Data center availability remains a constant constraint across the industry, and we are no exception.

Moreover, we believe the evolving U.S. policy landscape could create greater challenges for data center development in the U.S. In 2027, more than 75% of our data center capacity comes online outside of the U.S. In fact, in the next several months, new facilities in Canada, in France, and in Norway come online. And during the quarter, we signed contracts for additional capacity in Norway and Finland, further expanding our European footprint, and we're in the final stages of contracting for a data center in Sao Paulo, our first in South America. So far, we secured approximately 700 gross megawatts of data center capacity for delivery by the end of 2027.

Our first-party cloud is already among the larger non-hyperscaler AI clouds and we're far down the path in negotiations for gigawatts of capacity. Finally, the best way to increase data center capacity is by increasing the token generating capability of the hardware in the data center, put simply to generate more tokens per megawatt. To this end, disaggregated inference plays a role in expanding our capacity by increasing by up to 5x the number of tokens that the data center can deliver. More super fast tokens per megawatt means each data center produces markedly more revenue. Turning now to the second element of capacity, manufacturing and supply. We're scaling manufacturing with Flex and Sanmina.

We expect to exit 2026 with more than 10x the manufacturing capacity we had at the start of the year. In 2027, we plan to add factories and further increase capacity to support the substantial growth we anticipate. Our supply chain is well positioned to support this growth as well. Most importantly, we have sufficient wafer commitments from TSMC, which continues to distinguish itself as a world-class partner. It is also important what our supply chain does not need. That is also key to us. Unlike other competitors, we do not use HBM memory. We believe memory shortages and the price increases associated with them will continue through 2028, raising competitors' costs and limiting supply.

Industry-wide memory challenges create an opportunity for Cerebras. Factories and supply are necessary, but not sufficient to scale capacity. We must also have high yields. Cerebras having invented a fundamentally new computing architecture, applying metrics and mental models designed for other architectures can be like examining a Tesla and asking where is the exhaust pipe or trying to measure its tail pipe emissions. Electric cars require a different frame of reference and so this wafer-scale computing. We welcome questions about every aspect of our design, and we'll work to share architectural details more clearly, but there are also a set of persistent falsehoods proselytized to the financial community.

They believe that our systems cannot support large models, obviously false as they currently do, and that our manufacturing yields are low. Both are false. I already addressed model size earlier in my comments and in previous discussions. Since manufacturing yields are harder to observe, I'll take some time here to address the topic directly. There are 2 types of yields: fab yield and parametric yield. Our yields from TSMC, which are the fab yields, are high. So are our current parametric yields. While our technical blog covers this subject in detail, I'll provide a brief overview. All wafers contain defects. For decades, those defects were a central reason that the industry considered wafer-scale processors impossible.

We solved that problem through redundancy. Our design contains roughly 1 million identical tiles with a small portion held in reserve. When a defect disables a tile, the hardware routes around it and activates a spare. This mechanism works both at the fab and in the field, delivering high wafer yield and strong in-field reliability. The second challenge is parametric variation. Even when silicon functions, its electrical characteristics vary across the wafer. Some regions run faster or slower than other regions at the same voltage. This is a basic fact of chip design and one of the obstacles we had to solve to make wafer-scale computing possible. We addressed parametric variation in 3 ways.

First, we removed the wafer's outer edges. The edges are the weakest part of a wafer and account for a bunch of the parametric variation. We use the largest square that fits into a 300-millimeter wafer, excluding much of the weakest silicon. Second, we design and test for a broad range of conditions. Our cores tolerate a wider range of variation. During timing closure, the process of verifying that circuits meet timing requirements, we validate the design across operating conditions much broader than those used by others in the industry. This enables expected performance despite variations across the wafer. Finally, we tune voltage independently across the wafer. We invented technology to adjust core voltage independently across 84 wafer sections.

We can raise voltage in slower sections or lower it in faster sections, giving us precise control over performance. We know no other chip with this capability. The result is exceptionally high yield at both the fab and the factory. We achieved this by engineering solutions to the problems others said made wafer-scale computing impossible. As a final note, there are those who have pointed to our relatively low gross margins on hardware shipments and suggested that this is evidence of a yield problem. When, in fact, it simply reflects the ongoing ramp of our manufacturing capabilities and the final shipments of hardware covered under special pricing programs offered to one of our early customers.

We expect our hardware margins to improve markedly over the next several quarters, eventually exceeding our long-term target of 60%. Turning now to capabilities. We are advancing our capabilities on every front: new systems and processors, improved software, advanced manufacturing techniques and partnerships that deliver best-in-industry solutions. Despite the rapid pace of change across the AI industry, the importance of inference speed continues to grow. And Cerebras remains the undisputed leader. We believe this performance advantage will endure over time. And to that end, our product road map continues to advance rapidly. Our CS-4 will soon be in full production, doubling both performance and throughput.

Our next-generation processor, the Wafer Scale Engine 5, which will be deployed in our fifth-generation CS-5 system, is back from the fab ahead of schedule. The WSE-5 taped out on June 15th. The chip is back, has been brought up and is already running models and is undergoing in-chassis testing. Moving from tape-out to system test in less than 5 months is exceptional execution. The CS-5 will more than double the speed of CS-4. It will enter production in early summer of '27. It is built on TSMC's 5-nanometer node where competition for wafers is less intense and wafer costs are much lower. Our innovations also extend to complete solutions.

To our knowledge, Cerebras is the first to deliver production-scale, heterogeneous disaggregated inference. Today, our cloud serves customers using AMD GPUs for prefill and Cerebras CS-3 systems for decode. To enable the production launch, we built a disaggregated inference software platform. It is built to work with AMD as well as other GPUs. The same solution will be deployed across the Cerebras inference cloud and to select data center customers. Disaggregated inference delivers substantially more high-speed tokens for the same power. That means more profit per megawatt for cloud owners and lower cost per token for AI users. We believe disag will become one of the default offerings in major cloud deployments. Our software stack continues to mature.

Our cloud services deliver impressive uptime, our systems keep getting faster and the time needed to bring up new models has fallen to just a few days. Cerebras was a Day 0 launch partner for Kimi K3.1, Moonshot AI's 2.8 trillion parameter frontier-class open weight model, perhaps the premier open weight model in the market. Kimi 3.1 on Cerebras is not only the fastest in the industry, but it runs at up to 2,000 tokens per second. Nearly 11 years into our journey, we continue to innovate at a tremendous pace. Our team has stayed together through good times and tough times, and there is more energy and excitement in our building than ever.

We take pride in tackling and solving some of the industry's hardest problems. By way of summary, we beat on all metrics, and we're raising guidance for Q4 across the board and expect to triple revenue in 2027. We made important progress in each of our key domains, including adding 2 hyperscale customers and have now won 3 hyperscalers in the last 6 months. We began shipping our CS-4, our fifth-generation processor, the WSE-5, is back and in CS-5 system test in record time. Production disag is now live with AMD, and Day 0 support for yet another 2.8 trillion parameter model, which successfully achieved an industry-best performance. With that, let me turn things over to Bob.

Robert Komin: Thank you, Andrew, and good afternoon, everyone. Before I get into the specifics on our Q3 results and Q4 outlook, I'd like to reflect on how we've scaled the business. We chose to ramp a cloud offering in early 2025 because in this rapidly advancing AI landscape, AI natives want to scale compute quickly and don't want to own data centers. Because hyperscalers plan their data center compute allocations years in advance, they often don't have near-term availability in their own facilities for new hardware. In order to win the large deployments that foundation labs and hyperscalers require, Cerebras needed to demonstrate that our systems could deliver our industry-leading inference performance at scale.

After having the opportunity to thoroughly evaluate our solution in our private cloud, these customers can then choose to buy our hardware or purchase our cloud offering. To be able to provide enough cloud capacity quickly to meet these demands, we chose to begin to lease data center capacity and grow our private cloud as quickly as possible towards gigawatt scale. This general strategy appears to have been sound, as data center capacity is now very scarce, and we have a lot of it under contract. Even more importantly, once we had our private cloud, we were able to demonstrate the value of fast inference to the entire market and drive more demand for it.

Large new data center capacity can take 15 to 18 months to bring online. Instead of waiting for these data centers, and to accelerate our ability to meet the overwhelming demand we already had, we rented some of our own equipment back from one of our customers. As we've previously described, this applied downward pressure to gross margin in the short term. But it also allowed us to grow existing customers and win significant new ones because we had some capacity when they were ready to buy. We have now won frontier labs like OpenAI and hyperscalers like AWS, and recently added 2 new hyperscalers to our customer base.

We have other leading customers like Cognition, Figma, Block and Bilt, among others on our cloud. We meet these customers where they are, in some cases, selling them hardware and in others, delivering them our cloud services. Soon, new neo-cloud customers we have signed will begin distributing fast inference services running on Cerebras hardware in their clouds. In our prior calls, we also shared that 2026 is going to be a year of building out capacity, capabilities and adding customers to set us up to more than triple our revenue in 2027. With our strong progress so far this year, we are well positioned to achieve that target. Now turning to Q3 results.

I'd like to talk about our execution in the third quarter and I'm pleased to say that Q4 will be an important inflection point for our growth and core financial trends. We've exceeded our core financial guidance across the board for the third quarter in a row. We also delivered positive non-GAAP net income for the first time as a public company and ahead of schedule. As a reminder, definitions of our core metrics and the associated reconciliations to GAAP are included in today's earnings release and on our website. We delivered record core revenues of $231 million, up 10% sequentially and 70% year-over-year, exceeding the $215 million midpoint of our guidance by $16 million or 7%.

Revenue from our core cloud and other services was also a record, increasing by 272% year-over-year to $145 million. Sequential cloud revenue growth in Q3 was 13%. We currently expect flattish sequential cloud revenue in Q4. This trend is not driven by a pricing or demand issue. The pricing environment is healthy, and demand for our solutions continues to outpace supply by a wide margin. Our ramp in cloud revenue is limited entirely by our data center capacity, which we will begin to ramp in December as we build momentum going into next year. But unfortunately, that's too late to affect Q4. We expect much more substantial increases in data center capacity across 2027.

The bulk of this starts in the middle of next year as we deliver larger portions of the 700 gross megawatts of data center capacity currently under contract. Core hardware revenue was $86 million in the quarter, increasing 5% sequentially. We are seeing increasing customer demand for fast inference delivered not just through our private cloud but also through new hardware deals that we've closed with several new customers. With those hardware shipments scheduled to begin in Q4, we will see a significant step-up in total core revenue. Sequential core revenue growth from Q3 to Q4 will approach 40% and year-over-year growth will be about 85%.

These hardware orders, including Oracle, will continue to ship through at least the first half of 2027. As Andrew mentioned earlier, today, we also announced Microsoft as a new customer. Over the last few months, our pipeline has grown by much more than 10x, driven by a diverse set of opportunities from both new prospects and existing customers, including hyperscalers, neo-clouds, sovereigns, enterprises and AI natives. Turning now to gross margin. Q3 core gross margin was 41%, a sequential improvement of 40 basis points as compared to Q2 and 200 basis points above our guidance for the quarter.

In addition to the gross margin impact from renting back our systems, we also faced some headwinds in the quarter as we were burning off the last shipments from a hardware purchase agreement signed years ago at lower pricing. We guided that all of this would create temporary gross margin declines and the low point would be reached in Q3. Well, we did better than that, and the low point was actually last quarter in Q2. Now I'll break total Q3 core gross margin into its components. Core hardware gross margin declined sequentially to 31%, driven by the final shipments under the years-old customer agreement.

We're now shipping hardware orders at significantly higher current prices, which also means significantly better margins starting in Q4. Gross margin from core cloud and other services improved significantly to 46.9%, 510 basis points higher than in Q2. The primary driver of the increase was new business delivered at higher pricing and margin. This was partially offset by our program of renting back data center capacity and our hardware at higher cost from a customer. We expect this to continue through at least the middle of 2027 until we have enough of our own data center capacity to fulfill our high levels of cloud demand.

With all of that as a backdrop, in Q4, we expect total core gross margin to improve by a step function of 700 basis points to 48% at the midpoint. We expect total core margin to exceed 50% in the near term and to continue to improve towards our 60% long-term target. We also made strong progress towards profitability in Q3 and year-to-date. Core operating margin improved sequentially by 170 basis points to a loss of 14.3%, which exceeded our guidance midpoint by nearly 10 full margin points. Core operating loss was flat in Q3 versus Q2 at approximately $33 million. Core net income turned positive and was $30 million. Adjusted core EBITDA year-to-date was also positive at $2 million.

With strong increases in revenue and gross margin forecast for Q4, we expect that all of our core key profit measures, including operating margin, will be positive next quarter. Now turning to RPO and the OpenAI contract. Remaining performance obligations increased to $26.1 billion at September 30th. We update RPO for new business that was signed by quarter and adjust existing contract values for any changes to estimates of what remains to be recognized over the remaining committed term. Q3 RPO did not include the impact of new deals signed after September 30th, including Oracle, which is large.

There have been a lot of questions about the status of our OpenAI contract since it represents the majority of our reported RPO to date. As a reminder, OpenAI contract, which was filed with our S-1 before we went public, is a take-or-pay contract for 750 megawatts of capacity. Among many contract requirements that apply to both parties, it includes a term that if we experience delays in delivering capacity, they are entitled to credits during that time to use against future committed purchases. We have calculated the amount for potential delay credits and deducted them from RPO at September 30th.

These credits would have minimal impact on revenue and gross margin because as a percentage of RPO, they are estimated to be relatively small, and then they would get spread ratably as the majority of the revenue gets recognized over the next few years. Future changes to these estimates will be updated each quarter and reflected in both RPO and guidance, if needed. If a delay in delivering capacity continues for 90 days or more, both OpenAI and us have the option to cancel that portion of capacity only. The amount we estimate could be subject to cancellation this year would be less than 5% of the total committed megawatts.

Given the relentless demand for data center capacity around the world, it has taken us some additional time against a very aggressive schedule to find and bring up the initial data center capacity required to meet the delivery schedule. Based on our team executing frequent in-person inspections of each site, we now project we will be back on schedule by the second half of 2027, and this should reduce future delay credit risk. We've taken a conservative approach and have not assumed that any of these delays may get excused due to dependencies that are outside our control. If that happens, it would reduce the amount of the delay credits we have assumed.

Also, if OpenAI or Cerebras were to elect an option to cancel a delayed capacity tranche, we believe there is sufficient demand and we would find other customers to absorb it. OpenAI also retains an option to purchase an additional 1.25 gigawatts beyond their initial 750-megawatt commitment that could come online starting in 2029. Since that's still 3 years away and the world is changing as quickly as it is in AI, we've not even begun to discuss this yet. Now turning to our balance sheet and capital efficiency. We continue to have strong liquidity, ending Q3 with $7.8 billion in cash, cash equivalents, restricted cash and marketable securities.

We also have a revolving credit facility of up to $850 million that remains unused to date. The upfront $1 billion working capital loan we received in January and continuing payments we received for nonrecurring pass-through expenses to fit out new data center capacity to be delivered as part of the OpenAI contract have funded the majority of our net working capital requirements and capital expenses so far in 2026. We are not a neo-cloud. We are vertically integrated inventor of the fastest inference solution in the industry. Our business comes with a level of durability and technology differentiation that we believe will drive much higher long-term margin potential and cash generation over the long run.

Our model also requires lower capital investment for the mix of compute equipment we deploy. For a significant portion of the equipment used in our data centers that we manufacture, similar to others who've chosen to make and deploy their own AI chips, we do not pay a third party 70% gross margins, nor do we use high-priced HBM. We only pay our BOM cost, which reduces the relative capital expense per megawatt, while we also benefit from producing the fastest and most valuable tokens. This shows up in our capital structure to date, where the only debt on our balance sheet despite our rapid growth in private cloud build-out is the OpenAI working capital loan.

We've also received and continue to get upfront payments from customers, which is another lever for capital efficiency. Our capital structure today includes significant cash and investments on hand and little leverage, which provides us with more flexibility to have multiple ways to continue to fund our rapid growth, which is an advantage, especially in the current capital market environment. Now turning to our outlook. For Q4 2026, we expect to raise core revenue to the range of $310 million to $330 million, core gross margin to the range of 47% to 49% and core operating margin to the range of minus 1% to 1%.

For the full year 2026, we're raising core revenue to the range of $940 million to $960 million. We're raising core gross margin to the range of 42% to 44% and we're raising core operating margin to the range of minus 10% to minus 8%. In closing, Q3 was another record quarter for Cerebras and Q4 will be too. Our flexible model of selling both hardware and cloud is driving strong growth in revenue and improvements in gross margin that began a quarter earlier than expected. We've made significant progress scaling our manufacturing and data center capacity, which will enable us to triple revenue with core gross margin progressing towards our 60% long-term target in 2027.

We've demonstrated strong operating leverage and are projecting core operating profit to be approximately breakeven in Q4. Then, we expect to make significant progress towards our long-term 40% operating margin target during 2027. This mix of scale, growth and profitability positions us as a unique and leading AI systems provider. Also, as a final note, I'd like to remind everyone that we've now been a public company for more than 180 days. And as such, our IPO stock lockup releases were fully complete as of November 9th. Thank you for listening to our prepared remarks. I'd like to ask the operator to please open the line for questions.

Operator: [Operator Instructions] The first question will be coming from Tom O'Malley of Barclays.

Thomas O'Malley: Congrats on the really good results. First question is in regards to the new deals, OpenAI and Microsoft. If you could just give us any metrics perhaps on the relative sizes of those new deals? And then, Bob, you mentioned that some of OpenAI is already in the RPO, perhaps giving us how much is already in the RPO.

Andrew Feldman: Tom, this is Andrew. Good to hear from you. We'll be announcing more details about the Microsoft opportunity and the deal we struck with them. We are really obviously very excited about that. We'll be announcing that in more detail in the first half of next year, and that's when we can expect revenue to start from that transaction. Bob?

Robert Komin: Tom, in the RPO, what we've included for OpenAI is our estimate of the committed amount of revenue under the contract, under the take-or-pay contract, no extensions, no additional options, adjusted by the delay credits that we think would be -- could be due. And it also includes for data centers that we've signed for them, it includes the amounts that they pay for nonrecurring costs to set them up and also for monthly recurring costs over the term of the contract. And those are the pass-through revenues and pass-through expenses that we recognized in the P&L.

Thomas O'Malley: Got you, Bob. I said OpenAI, I meant Oracle, but we can circle to that. But I guess the other one is just we've seen ability from here.

Andrew Feldman: We fight really hard to win hyperscalers. We can't. Tom, that is interesting.

Robert Komin: And as I said Oracle is even easier. So far, no Oracle in RPO. We signed it up.

Thomas O'Malley: And then the follow-up is, so we've seen the ability from frontier AI a lot to not only to build the hardware quickly like Jalapeno, but to also accelerate model deployment across different compute hardware, increased fungibility. So how do you guys benefit from this deployment? And what do you expect in terms of the future of heterogeneous infrastructure to look like? How does that play into your hands?

Andrew Feldman: Sure. That's a really good question, Tom. Anthropic in their, I think in their S-1 or on a -- and we talked about how it took them about 3 days to bring up AMD GPUs. And this is a very interesting and important development, particularly those who have a long-term focus. I don't think people have really grasped what this means to our ecosystem. Compilers and low-level software have historically been a fundamental impediment to new architectures. They have favored the incumbent. They were the software moat that you heard so much about. If Anthropic can't, in fact, do what they say they do, it means that the value of this low-level software shrinks and over time disappears.

And it means that the moats from that low-level software also shrink and disappear. And if the low-level software has little value, the thing that is left to provide value is innovative hardware. And it means that sort of differentiation will be more dependent on hardware differentiation. I think this is a tremendous advantage to innovative hardware companies like Cerebras and others who build pioneering hardware and use newer different architectures. And I think this will remove an advantage that the incumbents have long had. That's a really good question, and I think something that the industry needs to keep an eye on. Thank you.

Operator: That question will be coming from the line of Joe Moore of Morgan Stanley.

Joseph Moore: Congratulations on the strong hardware numbers. Can you talk about the RPO issue with the scheduling on the cloud, is there anything specific that we can focus on and your sort of ability, your confidence in your ability to resolve that before it turns into additional penalties?

Andrew Feldman: I think what we put in the RPO was a very conservative number of what is the absolute worst case we could be hit with. And so I think we are comfortable today that we will be better than that. These are commonplace in big data center deals. We have similar agreements with our data center builders, their penalties if they're late, and I think it varies over time whether you choose to implement them and you make a business decision at that time. I think that's the way these work, and that's the way we expect these to work going forward.

Joseph Moore: Okay. And what -- was there a cause of the delay? Like, I think we all recognize issues with land, power shell and things like that. Was it something like that? Or was it something else that sort of caused it?

Andrew Feldman: Our delays right now are exclusively land, power, shell. We have capacity. We have all materials. We are waiting on the completion of land, power, shell.

Operator: The next question will be coming from the line of Atif Malik of Citi.

Atif Malik: Andrew, I have a question on the product road map. When are you expecting crossover of CS-4 versus CS-3? Is it in 2027? And then on just your product road map, in CS-4, you improved throughput significantly. And if you stay on that path into CS-5 and beyond, can at some point, your chip deliver enough fast speed and throughput that your system cannot be used -- if systems can be used alone and no GPUs are needed?

Andrew Feldman: Good start. First, until a few weeks ago, all our deployments were CS-3 alone. It is only recently that we've begun deploying disaggregated solutions and we saw a meaningful bump in throughput, which is really exciting. So that's observation #1. So obviously, they work really well in an aggregated form. But disaggregation, which we are now deploying and sort of GA with produces even more high-speed tokens when high-speed tokens are constrained. I would say the crossover for CS-3 versus CS-4 is in Q1 '27. The road map is looking really good right now. We are ahead of schedule on the CS-5.

And while I mentioned in my prepared remarks, getting from tape-out to chip in system in 5 months is extraordinary. And we expect that to begin shipping in early summer '27, and we are -- that doubles performance over the CS-4, the CS-4 doubles performance over CS-3.

Atif Malik: Great. And then for Bob, you're guiding to flat cloud sales for Q4. So within that tripling of sales in 2027, can you just help us understand the mix of cloud versus hardware?

Robert Komin: I think the best way to think about the mix of hardware is the size of those deals can be very, very large. What we would like to do is provide some color on the shipments over the next quarter or 2. It's very reliant on our customers having their data centers ready for us. So what I can say is we think that the hardware mix in Q4 and into Q1 will be closer to 50% than where it's been over the last few quarters.

As we look forward into the rest of 2027, we'll have more information for the Q4 call, and I think we'll give a more complete view of our outlook on sort of everything for 2027 then.

Operator: Our next question is coming from the line of Quinn Bolton of Needham & Company.

Quinn Bolton: Let me add my congratulations on the progress. Andrew, I just wanted to make sure I heard you right. In the RPO and your assumptions, you're making assumptions on potential delay credits, but you have not received any cancellations for delayed capacity at this point. Is that correct?

Andrew Feldman: That is correct.

Quinn Bolton: Excellent. Okay, good. The second question is there's fears in the market about fast inference from low batch GPUs. That sounds like an expensive way to serve tokens. So wondering if you can address sort of the cost per token for Cerebras versus deployment on the low batch GPU configuration?

Andrew Feldman: Sure. The GPU allows you to reduce throughput by trading off for additional speed. And you do that by bringing down batch. So you have a lot of throughput at low speed. And as you try and get higher speeds, your throughput hemorrhages, to the point where you're in the low batch domain and have very throughput. Now that's the characteristics of the architecture. The way to think about that is they can get to some modest performance by going to low batch. But when they do that, the cost per token, the cost to generate a token jumps. They have fewer simultaneous customers, so the total number of tokens generated is very small.

As you continue to go faster, we become stronger and stronger and stronger. And we get to a point where no matter what you do with the GPU, they can't deliver the performance we can at low batch; at batch size 1, they still can't do it. And so the question is where do you cross over? I think around 250, 300 tokens per second, we are more economical. By the time you get to 500, 800, 1000 tokens, we are vastly more economical. Somewhere between 800 and 2,000 tokens, the GPU can't play. And that's the way it is right now. That's sort of the lay of the land. That's a good question on a complicated topic.

Operator: Our next question is coming from the line of Joshua Buchalter of TD Cowen.

Joshua Buchalter: Congrats on the Microsoft deal. Maybe to start, can you speak to us about how you're allocating capacity in light of the land, power, shell issues? There's a couple of layers to that. Like it doesn't sound like silicon or rack availability is an issue. So I guess how much outlook do you see is there for incremental hardware sales to customers now? And then I guess given the challenges with getting land, power and shell, for cloud customers, has there been any discussions with OpenAI about them potentially taking hardware sales and putting them in their own or their partners' data centers?

Andrew Feldman: Sure. The question really had 2 parts, right, Josh? The first is, is it really the case that land, power, shell and the construction thereof is the limiting factor? The answer is absolutely. TSMC has committed sufficient capacity, the wafers to us for 2027, we're very comfortable with that. The fact that we don't use HBM, obviously hugely beneficial as that's in very, very tight supply. You are correct in the second part of your question, which doesn't that create opportunity for you to sell hardware while your data centers are being built? I think the only caveat to that is that to the best of our knowledge, everybody's data centers are late.

That includes OpenAI's, that includes all those data centers we know are being built for hyperscalers. Data centers are late across the board and across the world. So the constraint of data centers isn't unique to us, it is also manifest in our customers. And so yes, we have supply for them. Yes, we are shipping to our customers with data centers. Yes, it's the fact that the case where they too are very late. Makes sense?

Joshua Buchalter: Yes. No, totally. But so the last part of the annoying 3-part question was is there any potential for the OpenAI contract to switch to hardware sales in light of the land, power, shell?

Andrew Feldman: Sorry, it is in the contract. They have that right. But as I'm saying, our understanding from them is their data centers are also behind schedule.

Joshua Buchalter: Okay. Got it. And then for my follow-up. Can you help us...

Andrew Feldman: Your question is 7 parts, Josh?

Joshua Buchalter: It wasn't my best. I'm sorry. I acknowledge that. Can you maybe talk about how Oracle and Microsoft are positioning your offering with their customers? Is this the case that it will be hardware sales and then it will just basically track based on how the demand for Ultrafast inference is for their customers?

Andrew Feldman: Sure. We've announced more information about the Oracle engagement and partnership that we will set aside for 2027, more details on Microsoft. In Oracle, we are -- they are purchasing hardware. It is being deployed in their data centers. Those data centers are in the U.S. We are collaborating and they are building pipeline for that now. There's a ton of interest. And so we do not think this is by any stretch of the imagination, the last deal we do together, it is the start of a large partnership in which our equipment will be deployed in Oracle data centers as part of the OCI cloud.

Joshua Buchalter: Thank you, Andrew, and I'll give up a question next quarter.

Andrew Feldman: You're trading me a hamburger today for a question next quarter.

Joshua Buchalter: Fair deal.

Operator: Thank you so much. That does conclude today's Q&A session. I would like to turn it back to Sean for closing remarks. Go ahead, Sean.

Sean Dorsey: In closing, please note we are attending the TomorrowX Summit in Austin next week on November 17th as well as the Barclays Global Technology Conference in San Francisco on December 9th. Thank you for joining us today. Operator, please go ahead and close the call.

Operator: Thank you. This concludes today's program, and you may all disconnect.

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