In order to build its own AI data center in the next few years, OpenAI conducted in-depth discussions with NVIDIA, planning to obtain high-performance NVIDIA GPUs through rental rather than direct purchase.

Purchase hardware in a more one-time or rent computing power from cloud service providers such as Micro and Oracle. OpenAI plans to obtain NVIDIA GPU rights in the form of a 5-year lease, thereby reducing the cost pressure of the hardware and transferring the old risk to the supplier. Foreign media The Information quoted people familiar with the matter as saying that OpenAI estimates that this rental method can reduce the overall hardware cost by about 10% to 15%. The two sides are still discussing this proposal, and there may be changes in the end.

The report pointed out that NVIDIA considers setting up a special financing platform to provide funds for this rental method. The financing platform proposes funds to help OpenAI purchase GPUs and network equipment, and uses the GPU itself as collateral, and then OpenAI's rent will be compensated for a loan within a 5-year period.

This discussion is part of a major strategic cooperation announced by both parties on the 22nd of this week. OpenAI will deploy at least 10 GW of power-scale NVIDIA systems for OpenAI's next-generation AI-based facilities, for which NVIDIA will provide financial support and dedicated hardware. In addition, NVIDIA invested in OpenAI for about $10 billion and replaced about 2% of its shares.

Although OpenAI valuation and acquisition are extremely potential, the company does not have the ability to purchase thousands of billions of dollars of hardware at a time. Direct rental allows OpenAI to have greater control over data center address selection, machine room configuration and workload optimization, ensuring long-term use of NVIDIA's new generation GPU.

If OpenAI and NVIDIA discuss leasing cooperation, it is expected to change the fund arrangement method, NVIDIA's inventory allocation method and new architecture introduced by AI basic construction, and at the same time affect the competitors' layout strategy of AI chips.

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