Even if there’s been a lot of excitement around AI in the last years, there’s been a very limited number of high-profile IPOs in the U.S. given that the market is dominated by a handful of players in computing (Microsoft, Amazon…), models/software (OpenAI, Anthropic…) and computing hardware (Nvidia, AMD). News that Cerebras, a US-based AI chip maker, filed for its IPO then made some noise, notably as it competes directly with Nvidia and boasts impressive chip performances and strong revenue growth.
Cerebras’ solution to solve the memory and communication bottlenecks in High Performance Computing is unique in the industry. Instead of linking together hundreds of GPUs sitting in servers, Cerebras’ solution lies in the manufacturing of a massive chip, the size of a full silicon wafer, holding thousands of computing cores, all interconnected at the wafer level and all having a direct access to a huge pool of on-chip memory. This onboard memory, 800x times bigger than competing products, means that the 900,000 cores can handle very large language models (LLM) without having to rely on a networking infrastructure to exchange and store data, hence materially optimizing the latency and power consumption of the whole system.
Measured by the number of processed tokens by seconds, Cerebras’ massively parallel wafer-scale computing engine clearly outperforms the competition both in the training and inference modes of LLMs. Most importantly, on a Total Cost of Ownership (including operating costs, software recoding…), Cerebras’ solution is clearly competitive with Nvidia’s latest top-notch solution.
Overall, as AI models evolve toward agentic systems and real-time inference, memory bandwidth and latency are becoming critical bottlenecks—an area where Cerebras appears particularly well positioned.
While Cerebras’ approach is interesting (massive single-chip approach vs. packaging of large numbers of GPUs for Nvidia), it faces two major challenges. First, manufacturing (in partnership with TSMC) will take time to scale as Cerebras’ chips are unique in the industry as we said above and require very specific processes, while Nvidia ships millions of chips each quarter.
Second, we believe that a migration out of Nvidia, if any, will take time as developers have a long history working on Nvidia’s CUDA software stack, the company’s proprietary programming language used by millions of coders around the world to write programs that run on GPUs and hence fully exploit their superior vector and parallel computing capabilities. CUDA has become the de-facto standard for GPU acceleration in deep learning and AI applications and the programs and libraries relying on CUDA are countless and can be found in every piece of AI/Machine learning software.
Despite these challenges, Cerebras has enjoyed some recent traction with hyperscalers that are critical for the company’s future. First, Cerebras announced a partnership with Amazon Web Services to provide its up-and-coming disaggregated inference solution. Second, Cerebras sealed a $20 billion deal with OpenAI to provide servers powered by its chips over the next three years.
Cerebras’ revenue, which increased 76% to $510m in 2025, are then likely to take off in coming months. Assuming revenue between $1 and $2 billion in the next 12 months and a P/Sales ratio of at least 50x (in line with Chinese AI chip start-ups such as Cambricon and MetaX), we wouldn’t be surprised to see Cerebras’ valuation skyrocket close to 100 billion on its first day of trading, compared to its last private valuation below $30 billion.
