SoftBank Acquires Ampere for $6.5B to Boost AI Infrastructure Amid Market Challenges

March 20, 2025
SoftBank Acquires Ampere for $6.5B to Boost AI Infrastructure Amid Market Challenges
  • Renée J. James, CEO of Ampere, has indicated that the acquisition will allow for the continued development of their Arm-based processor roadmap.

  • SoftBank has announced its acquisition of chip startup Ampere for $6.5 billion, a strategic move aimed at bolstering its investments in AI infrastructure.

  • As part of the deal, Oracle and Carlyle Group Inc. will divest their stakes in Ampere, which has faced challenges in gaining market traction despite its innovative technology.

  • SoftBank's Vision Fund has resumed its investment activities, participating in multiple funding rounds this year, signaling a renewed focus on technology investments.

  • Looking ahead, Ampere plans to launch CPUs with up to 256 cores produced on TSMC's advanced N3 process in 2025, with additional advancements expected in 2026.

  • The success of AWS's Graviton chips has prompted major cloud service providers to develop their own Arm-based processors, creating significant market challenges for Ampere.

  • Ampere competes with industry giants like Arm and Intel's Xeon CPUs, which jeopardizes its potential market share.

  • Although Google initially adopted Ampere's chips in 2020, it later shifted to developing its own custom Arm-based CPUs, further limiting Ampere's deployment.

  • The acquisition is pending regulatory approvals and is expected to finalize in the latter half of 2025, with Ampere continuing to operate as a wholly owned subsidiary.

  • Concerns have been raised about how SoftBank will manage its investments in both Arm and Ampere, given their potentially competing server CPU strategies.

  • This acquisition aligns with global trends, as the demand for AI capabilities is rapidly increasing across various industries, emphasizing the importance of energy-efficient designs.

  • SoftBank's investment reflects confidence in Arm's energy-efficient designs, which are crucial for the next generation of AI infrastructure.

Summary based on 26 sources


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