OpenAI picks AWS to power ChatGPT with EC2 UltraServers and Nvidia GB200/GB300

OpenAI has chosen Amazon Web Services (AWS) as the primary backbone for scaling ChatGPT and its broader generative AI stack, committing $38 billion over seven years to tap into Amazon EC2 UltraServers and massive fleets of Nvidia accelerators. The partnership takes effect immediately, with the stated goal of expanding compute rapidly while leaning on AWS’s price, performance, scale and security guarantees. All contracted capacity is slated to come online by the end of 2026, with an option to extend and grow from 2027 onward.

What OpenAI gets from AWS

The centerpiece here is Amazon EC2 UltraServers, engineered for advanced AI training and inference at extremely large scale.

AWS is pairing hundreds of thousands of Nvidia GPUs with the ability to scale to tens of millions of CPUs for data prep, post-processing, and orchestration. Notably, the deployment clusters Nvidia GB200 and GB300 parts on the same high-speed network fabric to minimize latency across interconnected systems. That design matters because today’s frontier models increasingly span many nodes during both training and serving; reducing cross-node delays can lift throughput and shrink cost per token.

Why now – and why AWS?

OpenAI’s usage curve and product cadence demand elastic capacity that’s available in predictable waves. AWS emphasizes its experience operating very large AI clusters – described as topping the 500K-chip mark – alongside mature security, isolation, and reliability practices. For OpenAI, buying guaranteed access and predictable economics over multiple years reduces supply risk and helps plan model roadmaps, safety evaluations, and global rollouts.

Performance, price, and the architecture bet

The GB200/GB300 co-location on one network is a strategic bet: mix-and-match compute tiers for training, fine-tuning, and high-QPS inference without forcing a full redeploy. In practical terms, that can shorten time-to-ship for new capabilities, enable targeted upgrades as newer silicon lands, and keep hot paths close to memory and storage. Combined with AWS’s storage, networking, and observability stack, OpenAI can standardize pipelines while still chasing lower latency for ChatGPT’s interactive workloads.

Concerns readers are already raising

Some users worry about reliability, pointing to the fact that even hyperscale clouds occasionally suffer regional incidents. That is a fair concern at this scale, but multi-AZ designs, automated failover, and regional diversity are precisely what the deal is buying – resilience that’s expensive to build alone. Another theme is that ChatGPT has felt “colder” or more filtered lately. Hosting choice doesn’t set policy; guardrails and response style are product decisions that ride on top of infrastructure. The AWS move is about capacity, not content policy.

What this means for users and the ecosystem

In the near term, expect steadier availability during traffic spikes, faster rollouts of model variants, and more consistent latency as capacity ramps through 2026. For the industry, a $38B commitment underscores how capital-intensive state-of-the-art AI has become – and how much advantage there is for platforms that can aggregate chips, power, and networking at planetary scale. If OpenAI opts to expand after 2027, it will likely be to keep pace with model size, global usage, and the rising bar for safety and evaluation workloads.

Bottom line: this is a scale play. If AWS delivers the promised performance and OpenAI tunes workloads to the GB200/GB300 fabric, users should see a more responsive, more reliable ChatGPT experience – backed by a long runway of compute that aligns with OpenAI’s product ambitions.