Cerebras Systems Inc. Class A Common Stock 2026 Q2 Earnings Call

Please go ahead. Thank you, operator.

Good afternoon, everyone, and welcome to Cerebras Systems Q2 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 roadmap, technology leadership, supply chain, operating model, and outlook for Q3 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, which are available on the investor relations page of our website. With that, I'll turn the call over to Andrew.

Thank you, Sean. Thank you all for joining us today. Q2 was a strong quarter. We completed our public offering, but we did not let that distract us from execution. We delivered record core revenue and beat guidance on all metrics: core revenue, core gross margins, and core operating margin. Looking forward, we see unbound demand for fast inference. The market is realizing that speed is not a benchmark item. Speed changes user engagement, it changes agentic performance, and it changes AI productivity. Fast inference unlocks new applications and new markets. As we've shared with you previously, 2026 is a foundation-building year for Cerebras. We've made excellent progress on multiple fronts in the past seven weeks since our last earnings call, preparing us for a massive 2027, 2028, and 2029 as we deliver on the $25 billion of RPO we currently have on our books.

With the benefit of that progress, we expect to more than triple our core revenues in 2027 and continue to grow at multiples in the years following. We think of progress in terms of capacity, capabilities, and customers. We are expanding capacity by adding new contracts for data centers around the world, expanding manufacturing capabilities, and collaborating with our vendors to ensure supply and to support our extraordinary growth. We are advancing our capabilities by inventing new technology that extends our performance and throughput and our power efficiency. We are expanding our customer base by accelerating AI productivity in existing markets like coding and agentic flows, and pioneering new areas like security, where speed opens up entirely new opportunities. On the capacity front, data center space continues to be the bottleneck for the entire industry, and we are no exception.

The faster we and our customers bring on new data centers, the faster we grow. Over the last seven months, we have had an all-out push to secure and build out data centers. We have two advantages. First, because we are serving inference, we do not need gigawatt footprint locations like those needed for training clusters. This gives us much more flexibility to scale up capacity across a multitude of locations around the world. Second, we have built a repeatable process for site selection, cluster deployment, and customer activation, which is an operational muscle required to turn gigawatts into production tokens at a global scale. I am pleased to report that our push has been very successful. We now have data centers either up or under contract in Alabama, Dallas, Denver, Minneapolis, Santa Clara, Stockton, and outside the U.S. in France, Finland, Manitoba, Montreal, Norway, Saskatchewan, and Toronto.

In total, over the last seven months, we have secured more than 600 megawatts of data center capacity that is either live now or will be delivered by the end of 2027. While this is not nearly enough to meet our demand, our data center pipeline of new opportunities for expansion continues to grow and is now measured in gigawatts. To put it in perspective, as we continue to build out our first-party cloud, it will be among the largest non-hyperscale AI clouds. Whereas at the end of 2025, we were on a steep learning curve, today I am happy to report that we are pretty good at data center build-out with a clear path to becoming excellent. Other key dimensions of capacity include manufacturing and supply chain.

Here we have successfully increased our manufacturing capacity and are building up new factories with Flex in Sanmina, and expect to increase our manufacturing capacity by more than 10x in 2026 and continue that expansion in 2027, again, preparing us for the exceptional growth expected in the years ahead. Our partnership with our supply chain vendors has also turned into a significant advantage. TSMC has once again come through, and we have the wafers needed to fuel our growth. Our ability to get wafer supply also benefits from the fact that we were able to deliver industry-leading performance while running on TSMC's five nanometer node, where wafers are less expensive and supply is less constrained. Our decades-long relationships with our supply partners reinforces our confidence that we can deliver on our growth plans going forward. These relationships are rare and valuable, particularly in times of short supply.

Finally, recall that most of the critical supply chain constraints currently faced by the industry don't apply to us. For example, we don't use HBM memory, CoWoS packaging, or require 3 nanometer fab capacity. On the capabilities front, in the second quarter, we delivered support for OpenAI's GPT-5.6 Sol, the largest and most capable of the frontier models. In fact, Cerebras serves 5.6 Sol at a speed that is 10X faster. With GPT-5.6 Sol, this lays to rest any of the remaining concerns regarding our ability to support large frontier models. Being a partner for the delivery of GPT-5.6 Sol and serving it to our cloud speaks to the maturity of our software stack. It takes millions of system hours of production hardening to get to the point where one can deliver hyperscale quality and reliability.

We're proud that our inference cloud can meet the requirements of the most demanding customers. Our collaboration on serving models at the frontier has opened up new and significant strategic advantage previously only available to NVIDIA. Closed-source frontier models include a continual stream of new insights and new AI techniques. Serving these models allows us to see into the future and to prepare for it. Our roadmap from the hardware through the software stack now reflects what we're seeing and will give us a compounding advantage in the years to come. Continuing on the theme of capabilities, let's turn to disaggregation. We now have disaggregated inference solutions with two of the leading chip companies, AMD with their Helios and AWS with Trainium. Disaggregation expands the market for both the GPU provider and for Cerebras. Disaggregation enables GPUs to participate in a market currently foreclosed to them, namely fast inference.

Disaggregation enables Cerebras to expand our opportunity to those customers who are more price sensitive and expands the profitability of our data centers. Let's see how this works. As with any compute market, as inference grows and matures, opportunities for specialization emerge. Disaggregation is a form of specialization that is particularly well-suited for workloads with well-known traffic patterns. In these cases, disaggregation delivers advantage by separating inference into two stages, prefill and decode, and using different processors for each stage. Prefill processes the input from the user or agent. It is a parallelizable workload. As a result, prefill is well-suited for GPUs and their HBM-based memory architectures. Decode generates the output tokens. It's the harder technical problem and is the bulk of the computational work in a disaggregated solution. It is sequential and memory bandwidth intensive, and is particularly well-suited for our Wafer-Scale Engine.

The prefill and decode processors need to be linked to create the end-to-end solution, and this is where standards-based IO and open engagement strategy has made integration easy and straightforward for Cerebras. A few weeks ago, we announced a partnership with AMD to build disaggregated inference solutions. The solutions combine their Helios racks with our CS systems. The combined solution maintains Cerebras' speed while increasing throughput by 5X. To understand how powerful this is, it's important to understand the difference between speed and throughput. Speed is a measure per user. It's measured in tokens per second per user. It is how fast your query is answered, or how long it takes an agent to finish a task. Here, it is on the x-axis. Throughput, on the other hand, is the total number of tokens the solution can produce per second.

It is measured by adding up all the tokens across all the simultaneous users. Here, it is shown as it is generally done on the y-axis. Speed is critical for user experience. Throughput is critical for inference economics. GPU solutions can support high throughput, but only at low speeds. When configured to support even moderate speeds, GPU throughput drops precipitously. This is true not just for GPUs, but also for ASICs and all solutions that use HBM. The HBM memory architecture forces a trade-off between throughput and speed. SRAM-based architectures like Cerebras' are the exact opposite. We support blisteringly fast tokens, but at moderate throughput. GPUs want to get faster without giving up throughput. Cerebras wants more throughput without giving up speed. Herein is the strength of our disaggregated solution. It delivers Cerebras speed with 5x higher throughput.