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OpenAI Debuts First Model Using Chips From Nvidia Rival Cerebras

Rachel Metz
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OpenAI has debuted its first AI model powered by chips from Cerebras Systems, marking a strategic shift away from exclusive reliance on Nvidia’s hardware for training and inference. The move signals OpenAI’s effort to diversify its semiconductor supply chain amid global chip shortages and Nvidia’s dominant but costly market position in AI accelerators. Cerebras, a Silicon Valley startup, specializes in wafer-scale chips designed for large-language-model training, offering an alternative to Nvidia’s H100 and upcoming B100 GPUs. This partnership could accelerate AI development by reducing dependency on a single supplier, potentially lowering costs and improving access to high-performance computing resources. The announcement reflects broader industry trends as tech giants seek chip suppliers beyond Nvidia to mitigate risks and foster innovation in AI hardware ecosystems.
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TechnologyAIGiftExpandOpenAI has been adding new partners to meet its growing computing needs.Photographer: Andrey Rudakov/BloombergGiftGift this articleAdd us on GoogleContact us:Provide news feedback or report an errorConfidential tip?Send a tip to our reportersSite feedback:Take our SurveyNew WindowGiftBy Rachel MetzFebruary 12, 2026 at 11:30 PM GMT+5:30BookmarkSaveTranslateTakeaways by Bloomberg AISubscribeOpenAI is releasing its first artificial intelligence model that runs on chips from semiconductor startup Cerebras Systems Inc., part of a push by the ChatGPT maker to broaden the pool of chipmakers it works with beyond Nvidia Corp. The model, GPT-5.3-Codex-Spark, is intended to be a less powerful but speedier version of its most recent Codex software for automating coding. The Spark option, slated to be released Thursday, lets software engineers quickly complete tasks like editing specific chunks of code and running tests. Users can also easily interrupt it, or order the model to complete something else coding-related without having to wait for it to finish a lengthy computing process.

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Source: Bloomberg Technology

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