OpenAI Plans to Purchase TPU Chips from Google
According to people familiar with the matter, OpenAI plans to buy AI chips from Google, which is owned by Alphabet. The final terms of the deal are not yet clear, and both OpenAI and Google declined to comment. Google began selling TPUs directly to some customers this year. OpenAI previously announced plans to deploy its in-house chips later this year and is working with Broadcom to develop an internal chip, Jalapeno. Oracle also announced on Tuesday that it will, for the first time, offer customers computing power based on Google TPUs on its cloud platform.
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The report discloses that OpenAI intends to purchase Google TPUs, and adds details on its in-house chips and its supply commitments from Nvidia, offering a clear view of the direction of diversification in the compute supply chain.
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OpenAI Plans to Purchase Google TPUs Cailianshe Download the App for Exclusive Early Access October 28, 2026 13:08:47 From Beijing Updated October 28, 2026 13:23:36
According to people familiar with the matter, OpenAI intends to buy AI chips from Alphabet-owned Google, the latest step in the ChatGPT developer's sprawling buildout of its AI infrastructure.
The people said the final terms of any deal are not currently known. OpenAI and Google both declined to comment.
As companies seek cheaper chips suitable for running artificial intelligence models, Google's tensor processing units (TPUs) are becoming increasingly popular as an alternative to Nvidia's market-leading AI chips. Google began selling TPUs directly to some customers this year.
The move also reflects OpenAI's strategic intent to pursue multiple avenues in its urgent effort to boost its own AI computing power.
The company previously announced that it plans to begin deploying its own custom chips later this year to support the operation of its AI models. That means it has joined the "custom chip wave" set off by multiple companies, jointly challenging Nvidia's dominance in the computing power field.
It is worth noting that Jalapeno, the in-house chip OpenAI developed in partnership with Broadcom, was developed with the assistance of veteran Google employees. Richard Ho, who heads OpenAI's internal chip team, previously participated in the development of Google's TPUs.
Google began developing its TPUs more than a decade ago. Initially, these TPUs were used to accelerate the company's web search engine and improve efficiency. But the chips quickly proved extremely valuable for handling AI workloads and in recent years have become an alternative to Nvidia GPUs.
Nvidia's Computing Power Hegemony Faces a New Challenge
The purchase plan will deepen the ties between OpenAI and Alphabet-owned Google—even though the two companies are in direct competition in AI products and search.
For a long time, OpenAI's relationship with Nvidia has been quite close. From the training of the first-generation GPT model to ChatGPT sweeping the globe, almost all of OpenAI's core model evolution has been built on tens of thousands of Nvidia GPU clusters. Jensen Huang personally delivered the world's first DGX AI supercomputer to OpenAI, and OpenAI's explosive popularity directly pushed Nvidia to the top of global market capitalization.
In August this year, Nvidia CEO Jensen Huang said that OpenAI has committed to deploying Nvidia AI infrastructure on a large scale by 2030, with existing and planned deployments of about 12 gigawatts and the opportunity to expand to about 16 gigawatts.
However, that has not stopped OpenAI from seeking to diversify its supply chain. Facing increasingly high inference costs, a massive computing power gap that can run into millions of chips, and the bargaining disadvantages and supply-cut risks brought by over-reliance on a single supplier, OpenAI is clearly unwilling to stake its entire fortune on Nvidia alone.
It is not an isolated case. Oracle also announced on Tuesday that for the first time it will offer customers computing power based on Alphabet-owned Google TPU chips on its cloud platform.
Regarding this arrangement, Mahesh Thiagarajan, president of Oracle Cloud Infrastructure, said in a statement that different AI workloads have different computing power needs, and this choice gives customers the flexibility to select, for each AI workload, the infrastructure best suited in terms of performance, throughput and economics.
Google executive Amin Vahdat said in a statement announcing the Oracle deal that "the next generation of AI will require diverse computing power."
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