Publishing the MRC specification is part of OpenAI’s overall compute strategy: shared standards in key infrastructure layers can help scale AI systems more efficiently, reliably, and across a broader partner ecosystem. In this post, we’ll cover the design of MRC, including: i) how it enables us to build multi-plane high-speed networks to create redundancy to ride out network failures, while using fewer components and less power ii) how MRC’s adaptive packet spraying virtually eliminates core congestion and iii) how our deployments use static source routing to bypass failures and eliminate whole classes of routing failure. In concert, these benefits allow us to deliver better models to everyone faster.

When training large AI models, a single step can involve many millions of data transfers. One transfer arriving late can ripple through the entire job, potentially causing GPUs to sit idle. Network congestion, link, and device failures are the most common sources of delay and jitter in transfers.

These problems get more frequent, and harder to solve, as the size of the cluster increases. This makes networking technology a key part of the design of Stargate.

To enable the current scale of Stargate supercomputers, we faced two key networking challenges. First, whenever possible, we should minimize the possibility of network congestion. There are unavoidable bottlenecks, such as two GPUs sending to the same destination at the same time. But outside of these cases, we should avoid congestion through design.