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SoftBank Acquires Ampere Computing to Solidify AI Strategy

TradingKeyMar 20, 2025 9:12 AM

TradingKey - SoftBank Group announced on Wednesday that it will acquire Ampere Computing, a startup focused on designing server chips based on Arm architecture, for $6.5 billion. The Japanese conglomerate anticipates that the transaction will be completed in the second half of 2025.

Founded by former Intel President Renee James, Ampere specializes in providing support for artificial intelligence infrastructure—a revolutionary technology that SoftBank founder Masayoshi Son has increasingly bet on. Ampere produces data center central processing units (CPUs) based on Arm’s architecture, with its chips already utilized by Oracle for cloud computing infrastructure.

SoftBank has been expanding its investments in artificial intelligence infrastructure, including a recent announcement of a collaboration with OpenAI to develop enterprise-grade AI solutions, as well as participation in the $500 billion private AI investment initiative dubbed "Stargate" facilitated by the U.S. President Donald Trump. This acquisition positions Ampere and Arm as central assets in SoftBank's AI chip strategy.

Chips utilizing Arm’s instruction set provide an alternative to the x86 architecture chips offered by Intel and AMD. Arm-based processors typically exhibit lower energy consumption. These chips, paired with graphics processing units (GPUs), are known as "host" processors and are employed to manage AI tasks, such as constructing specialized software programs referred to as models. It is common for every four Nvidia GPUs sold to require one microprocessor.

However, Nvidia has started to aggressively promote Arm processors as a viable alternative to traditional host microprocessors, which aligns with the direction of Ampere's output in replacing Intel or AMD chips.

Analysts suggest that upon the completion of its acquisition of Ampere, SoftBank's AI strategy will establish a comprehensive closed loop encompassing "chip design, computing infrastructure, and large model applications."

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