OpenAI has publicly stated that its largest NVIDIA GB200 supercomputer has been used to train cutting-edge models, with the cluster connecting over 100,000 GPUs into a single training network through a self-developed multi-path reliable connection protocol.

The company's engineering documentation notes that a switch capable of connecting 64 800Gb/s ports was modified to have 512 100Gb/s ports, allowing approximately 131,000 GPUs to be fully connected using only two layers of switches; traditional 800Gb/s networking would require three to four layers. This two-layer structure reduces power consumption, points of failure, and network costs, while providing redundancy across multi-plane networks, allowing training jobs to continue running despite multiple link jitters per minute.

This protocol has been deployed on OpenAI's largest GB200 cluster, located in Abilene, Texas, and at the Stargate site co-built with Oracle Cloud, as well as Microsoft's Fairwater supercomputer. The hardware combination comes from NVIDIA and Broadcom, and the protocol has been open-sourced in collaboration with Supermicro, Intel, and Microsoft. During the training of recent cutting-edge models used for ChatGPT and Codex, operations had to restart four first-layer switches, which would have directly interrupted training under the old architecture.

The product line is tied to the cluster. GPT-5.5 has completed training on the GB200 system at the flagship site in Abilene and Oracle Cloud. The company has also separated the inference side: the self-developed Jalapeño chip has released the first batch of InferenceX data, and a super-fast mode for GPT-5.6 Sol has been launched in collaboration with Cerebras. The computing power combination has expanded from a single Microsoft and NVIDIA setup to include Amazon Cloud, Supermicro, Broadcom, Cerebras, CoreWeave, Oracle, SoftBank, and energy providers.

Disclosed power contracts specify demand in gigawatts. Third-party statistics indicate that OpenAI has contracted approximately 26 gigawatts of computing power, with about 10 gigawatts from NVIDIA and about 16 gigawatts from non-NVIDIA sources, including approximately 6 gigawatts from Supermicro MI450 and about 10 gigawatts from Broadcom's custom silicon; the non-NVIDIA portion is now greater than the NVIDIA portion and exceeds the total disclosed combination of Anthropic. xAI's Colossus has deployed 100,000 cards internally with a plan to double to 200,000, compared to installation speed; OpenAI uses a 100,000-card Ethernet structure that maintains training compared to available computing power.

Buyers are model labs and cloud providers, competing for synchronized whole network GPU hours over extended periods, rather than single card pricing. Sellers include switch, optical module, power, and cabinet supply chains. The driving force behind the event is whether the cluster can continue training without interruption during failures. Beneficiaries are networks and clouds that can turn 100,000-card level training into stable operations, while pressured suppliers can only provide fragmented card rentals that drop connections halfway through training.

Source: Public Information