OpenAI isn’t picking sides in a chip war so much as building a very large toolbox. Its partnership with Nvidia focuses on bringing huge amounts of GPU capacity online over time, without locking OpenAI into using only Nvidia hardware. At the same time, talks with Amazon around Trainium reflect a desire to spread workloads across different chips and clouds. By mixing suppliers, OpenAI reduces shortages, controls costs, and matches each job to the hardware that fits it best.
How can OpenAI agree to use Amazon Trainium chips, if it has already agreed to use Nvidia chips and accept Nvidia’s $100 billion investment? Because these arrangements govern capacity supply and deployment scope rather than mandating exclusive use of a single vendor’s silicon. Public disclosures indicate that the Nvidia partnership announced in September 2025 defines a framework under which Nvidia plans to invest up to $100 billion as OpenAI brings new data-center capacity online. The investment links to specific build-outs and milestones and does not constitute a single, finalized upfront commitment. Available summaries do not describe exclusivity requirements that would prevent OpenAI from sourcing compute elsewhere.
At the same time, OpenAI has expanded its use of AWS under a multi-year relationship, and reporting from late 2025 indicates that Amazon is in discussions about a significant investment tied to OpenAI adopting Trainium for selected workloads. That structure implies a multi-vendor strategy in which OpenAI allocates different classes of jobs across different accelerator platforms. Training large frontier models, fine-tuning, inference, embeddings, and internal batch processing place distinct demands on hardware, power efficiency, interconnects, and software tooling. Using a mix of GPUs and custom accelerators allows OpenAI to match workloads to the hardware that best fits their performance and cost profiles.
Compute availability remains a dominant constraint for large AI developers. Sourcing capacity from multiple clouds and multiple silicon providers reduces exposure to supply bottlenecks and deployment delays. It also provides leverage in commercial negotiations by ensuring that no single vendor controls OpenAI’s entire compute pipeline. The absence of contractual exclusivity enables OpenAI to rebalance workloads as pricing, performance, or availability shifts.
This flexibility aligns with OpenAI’s broader restructuring in 2025, which reduced reliance on a single primary compute partner and opened the door to additional relationships. OpenAI has since worked with multiple suppliers across processors, custom silicon, and data-center infrastructure, including arrangements involving AMD, Broadcom, Oracle, SoftBank, and AWS-hosted Nvidia systems. Similar multi-supplier approaches appear across the industry, where large AI developers combine general-purpose GPUs with custom accelerators to scale infrastructure efficiently. Within that context, the potential Trainium deployment complements, rather than conflicts with, OpenAI’s Nvidia partnership.
OpenAI’s heterogeneous AI processor zoo could become the testing ground for new models (beyond just OpenAI’s) to see if the new models play nice with all processors, or if some are better at accelerating the new models than others.
As is pointed out and illustrated in our 2026, 425-page AI processor report, not all of the 146 processors are equal, and some are being built for specific LLM models. If you’re in or interested in the AI market, you should have our bible of a report at your side.
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