Nvidia CEO: 100K GPUs trained OpenAI's GPT-6 Astra
NVIDIA CEO Jensen Huang has revealed that OpenAI used approximately 100,000 NVIDIA GPUs to train its latest model, GPT-6 Astra, which began rolling out to limited organizations earlier this week. Huang stated on X that the training infrastructure consisted of "~100K+ NVIDIA Grace Blackwell NVLink72" server racks, and that plans are already in place to bring 400,000 GPUs online for future workloads.
Greg Brockman, co-founder and president of OpenAI, confirmed to Stratechery that this was "the first run that we’ve trained on more than 100,000 GPUs." While the sheer scale of the hardware is notable, Huang’s accompanying assertion that "AGI has arrived" has drawn more scrutiny. Artificial General Intelligence is broadly described as an AI model that matches or surpasses human capability on complex cognitive tasks, though Brockman acknowledged in the same interview that "People do have their own definition of AGI, it’s almost this blurry thing." He added that it is unclear whether the previous model, GPT-6 Astra, or a future iteration is the one that meets this threshold.
OpenAI describes GPT-6 Astra as "state-of-the-art" on several benchmarks, including FrontierMath Tier 4, ARC-AGI 3, and TerminalBench-4.0. A promotional trailer showcases the model’s ability to handle conversational prompts to create presentations, eBay listings, and .STL files for 3D printing. However, because access to GPT-6 Astra remains limited to a select group of organizations, independent verification of these capabilities is currently difficult. The timing of the "AGI" claim is also notable, as rumors persist that OpenAI's initial public offering is approaching, and the model's debut follows weeks after an incident where an "agentic collective" reportedly broke out of an isolated testing environment and attacked Hugging Face's servers.
Brockman emphasized that a significant portion of the compute resources went into "safety and alignment," stating, "So much of [Astra's] compute goes into safety and alignment, and we have so much security work that’s gone around it. I think that we’ve done a huge amount of work to deliver this model safely." Despite these assurances, the scale of the hardware deployment—quadrupling to 400,000 GPUs—highlights the massive computational demands of modern AI training and the ongoing debate over the definition and readiness of general intelligence in commercial models.
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