Flex, Cerebras Expand Partnership to Scale American Manufacturing of Cerebras AI Supercomputers

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Flex and Cerebras Systems Inc. announced an expanded manufacturing partnership to scale production of the Cerebras CS-3, one of the world's most advanced AI accelerator systems, at Flex manufacturing facilities in Milpitas, California. As demand for AI infrastructure accelerates, the collaboration reflects a significant expansion of advanced manufacturing capacity in the United States.

The expanded operation is expected to increase CS-3 production capacity by approximately 7x through 2026, supported by new production lines, expanded floor space, advanced test infrastructure, and additional skilled manufacturing talent based in California.

At a time when electronics manufacturing is often associated with overseas supply chains, this partnership demonstrates that some of the world's most sophisticated AI systems are being designed, assembled, integrated, and tested in the heart of Silicon Valley.

"The CS-3 is unlike any computer system ever built, and scaling its production requires an extraordinary manufacturing partner. Flex brings the technical depth, operational rigor, and manufacturing expertise needed to support that scale," said Dhiraj Mallick, COO of Cerebras. "People often think the entire AI manufacturing and packaging supply chain lives overseas, but everyday across the U.S., teams of American engineers and technicians are building state-of-the-art AI systems that power frontier AI workloads around the world."

The CS-3 is built on Cerebras' industry-leading wafer-scale engine architecture, featuring a processor physically larger than any conventional AI chip. The system integrates advanced liquid cooling, high-density power delivery, precision mechanical assembly, and tightly coordinated networking infrastructure into a platform designed for large-scale AI training and inference.

Manufacturing the CS-3 presents challenges rarely encountered in traditional server production. Each system requires specialized handling processes, custom tooling, precision calibration, and extensive system-level validation. Flex engineers worked closely with Cerebras to develop dedicated assembly flows, automated test stations, and new manufacturing methodologies tailored specifically to wafer-scale computing systems.

"The CS-3 does not resemble a conventional server or rack-scale compute platform," said Rob Campbell, President of Communication, Enterprise and Cloud at Flex. "Every stage of the manufacturing process—from mechanical integration to thermal validation and final system qualification—required deep collaboration between our engineering teams. We thank Cerebras for their partnership in demonstrating what American advanced manufacturing can achieve when two highly technical organizations work side by side."

To support the ramp, Flex is expanding dedicated manufacturing operations for Cerebras in Milpitas, with multiple new assembly and integration lines coming online through 2026. The footprint devoted to CS-3 manufacturing is expected to grow substantially this year as production accelerates to meet customer demand from AI model developers, cloud providers, and enterprise customers.

The expansion is also contributing to growth in high-skilled manufacturing roles across the region, including manufacturing, systems integration, quality, supply chain, and testing.

Inside the Milpitas facility, production operations span precision mechanical assembly, high-power electrical integration, liquid cooling installation, optical networking validation, and full-rack system qualification. To support growing demand, the site has expanded into a high-throughput manufacturing environment with parallel integration lines, enhanced burn-in and validation areas, additional automated test infrastructure, and increased warehouse and logistics capacity for critical components and finished systems. Tooling and fixtures will enable multiple CS-3 systems to move through integration and testing simultaneously, which is expected to significantly increase throughput while maintaining the rigorous quality and reliability standards required for large-scale AI deployments.