Unlocking large scale AI training networks with MRC (Multipath Reliable Connection)

Frontier model training depends on reliable supercomputer networks that can quickly move data between GPUs. To make this faster and more efficient, OpenAI has partnered with AMD, Broadcom, Intel, Microsoft, and NVIDIA to develop MRC (Multipath Reliable Connection): a novel protocol that improves GPU networking performance and resilience in large training clusters. We released MRC today⁠(opens in a new window) through the Open Compute Project (OCP) to enable the broader industry to use it. 

With more than 900M people using ChatGPT every week, our systems are becoming core infrastructure for AI, helping people and businesses around the world build with increasingly capable models. Prior to the inception of Stargate⁠, we co-developed, brought up, and maintained our first three generations of supercomputers with great care and close collaboration with our partners over the span of a few years. This invaluable experience informed our strong belief that, to efficiently use compute at the scale of Stargate and succeed in our mission, we need to rethink and drastically reduce complexity in every layer of the stack – including network design. 

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.