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Nvidia challenger Cerebras says it's leaped Mid-East funding hurdle on way to IPO

Mar 31, 2025 · theregister.com
Nvidia challenger Cerebras says it's leaped Mid-East funding hurdle on way to IPO
AI chip startup Cerebras Systems has resolved concerns with the US Committee on Foreign Investment (CFIUS) regarding its funding sources, particularly its reliance on UAE-based G42, which accounted for over 87% of its revenues in early 2024. To address CFIUS's concerns, Cerebras amended its agreement with G42, limiting them to non-voting shares, thus avoiding further review. This resolution comes as Cerebras prepares for its IPO, expected to raise up to $1 billion at a valuation of $7 to $8 billion. The company aims to diversify its customer base and expand its infrastructure across North America and France, deploying over a thousand wafer-scale accelerators by the end of 2025.

Cerebras plans to enhance its high-performance inference-as-a-service platform, with new datacenters in America, Canada, and France. While G42 remains a major customer, Cerebras will maintain control over its Oklahoma City and Montreal sites. The company's wafer-scale chips are designed to deliver significant performance advantages, achieving up to 125 petaFLOPS at FP16, and are positioned as a key differentiator in high-throughput inference for large models.

Key takeaways

  • Cerebras Systems resolved CFIUS concerns by amending its agreement with G42, limiting the UAE-based firm to non-voting shares, clearing the way for its planned IPO.
  • G42 accounted for over 87% of Cerebras' revenues in the first half of 2024, but Cerebras aims to diversify its customer base with a high-performance inference-as-a-service platform.
  • Cerebras plans to deploy over a thousand wafer-scale accelerators across six new datacenters in America, Canada, and France by the end of 2025, with most sites operated in partnership with G42.
  • Cerebras' systems are designed to achieve up to 125 petaFLOPS at FP16, significantly outperforming conventional GPU-based providers in model serving speeds.
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