Amazon Web Services (AWS) is set to deploy two million Nvidia graphics processing units (GPUs) across its global data centre infrastructure in 2027 and 2028.
The rollout will include Nvidia’s Blackwell Ultra, Rubin and Rubin Ultra GPUs, aimed at accelerating AI workloads across various sectors.
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The planned expansion intends to meet ongoing demand for AI hardware, while AWS continues to invest in its own chip development through Annapurna Labs.
Nvidia’s GPUs are widely used for training and deploying large AI models and remain a central component of the AI infrastructure supporting cloud providers.
Neither company disclosed the financial terms related to the acquisition of the new GPUs.
Nvidia’s Blackwell Ultra and Rubin products are high-end chips, with typical list prices in the tens of thousands of dollars per unit, though volume discounts are common in large agreements.
The companies outlined additional collaborative efforts, such as bringing Nvidia Vera CPU-based infrastructure to AWS and expanding networking integration using Nvidia Spectrum technology.
AWS CEO Matt Garman said: “Customers want the freedom to choose the best tools for their AI workloads, and they want confidence that everything works seamlessly together.
“That’s why we’ve invested deeply with Nvidia to make AWS the best place to run Nvidia AI technologies, optimising performance across our infrastructure from networking and security to deployment.
“This expanded collaboration gives frontier labs, enterprises and governments even more ways to build and deploy AI on AWS.”
AWS reported that the additional GPU capacity would support a range of AI workloads, including scientific research, enterprise automation, and robotics.
The partnership will also enable government agencies to access secure infrastructure using Nvidia technology for sensitive workloads. The collaboration builds on previous projects dating back 16 years.
In March this year, AWS and Cerebras Systems announced a partnership to deliver accelerated AI inference capabilities for generative AI and large language model (LLM) tasks.
