MRC Network Protocol Launch

The line-up on the MRC paper is the part of this launch that should not exist on paper. AMD and NVIDIA do not co-author specs together. Broadcom and NVIDIA are direct competitors in AI switching silicon. Intel is fighting both for NIC share. And yet the Multipath Reliable Connection protocol arrives with all six logos: OpenAI, AMD, Broadcom, Intel, Microsoft, NVIDIA, plus an OCP filing that hands the design to anyone who wants to implement it. The reason is visible in OpenAI networking lead Mark Handley's own framing — the work is explicitly positioned 'as opposed to each of these large companies doing their own thing.'

The choice to commoditize this layer is strategic, not generous. OpenAI does not win by selling networking IP; it wins by training larger models faster on whoever's silicon is cheapest. A proprietary multipath protocol locked to one vendor would slow OpenAI down at the procurement layer for years. A protocol that AMD, Broadcom, Intel, and NVIDIA all implement turns the AI back-end fabric into something OpenAI can dual-source on day one — which is exactly what is already happening, with Broadcom and NVIDIA hardware running MRC side-by-side inside the company's deployments. For Microsoft and OCI, who together host much of OpenAI's training compute, the same logic holds: Fairwater and Abilene each get a back-end fabric that is not hostage to a single switch vendor's roadmap. The signal to competitors is sharper still — by routing the spec through OCP rather than a closed alliance, the authors are daring Meta, Google, and the InfiniBand-heavy stacks to either adopt or explain why they didn't.

Mechanically, MRC is a rewrite of how an RDMA connection uses the network underneath it. Classic RDMA over Converged Ethernet pins a single connection to a single path; one congested link or one flaky transceiver and the connection stalls, dragging the whole synchronous training step with it. MRC instead does packet spraying — the same single connection scatters its packets across hundreds of paths simultaneously, so no individual link can become the bottleneck. The reordering and reliability work that this would normally break is pushed down into the NIC hardware on the new 800Gb/s interfaces.

The routing trick that makes this practical is IPv6 Segment Routing, or SRv6. Rather than letting intermediate switches choose paths via ECMP hashing — which is what causes hot-spots in the first place — the sender encodes the exact sequence of switch identifiers into each packet's destination address, so the path is chosen at source. Combined with hardware failure bypass that detects a dead path and reroutes traffic in microseconds, this turns the fabric into something that looks, to the GPU above it, like one fat reliable pipe even while individual links flicker. This is the part Ron Westfall of HyperFrame Research means when he says OpenAI is 'treating the entire AI fabric as a single fluid system instead of a series of isolated connections.' The protocol-level abstraction has moved up a layer.

MRC lands in a market that has been quietly tilting for two years. According to Dell'Oro Group's Sameh Boujelbene, 2025 was the year Ethernet sales and shipments to AI back-end networks surpassed InfiniBand — the first time the dominant RDMA fabric for HPC has lost its lead inside AI data centers. Meta had already telegraphed the move with its 2024 RoCEv2 build-out for distributed training, and NVIDIA's own Spectrum-X platform, originally a hedge, has become the lead vehicle for the MRC launch.

What the spec does to that trajectory is convert a procurement preference into a standards reality. InfiniBand's historical advantage was that it shipped end-to-end congestion control, lossless behavior, and adaptive routing as part of one closed stack. RoCEv2 on Ethernet got most of the way there but kept losing on multipath behavior at extreme scale — the exact gap MRC closes. Boujelbene frames it directly: hyperscalers are 'leaning harder into Ethernet for AI fabrics, especially as clusters push toward 100,000 to 500,000-plus GPUs.' For NVIDIA, which sells both InfiniBand (Quantum) and Ethernet (Spectrum-X), MRC is a controlled cannibalization — better to lead the Ethernet story than have Broadcom run away with it. For pure InfiniBand stacks, the runway just got shorter.

The 'why now' answer is in the scale numbers OpenAI is operating at. The company recently surpassed 10 GW of compute capacity and added more than 3 GW in just the prior 90 days, with frontier training runs spanning weeks across clusters that already touch hundreds of thousands of GPUs and are headed toward half a million. At that size, the failure mode dominates. OpenAI workload lead Greg Steinbrecher's phrase — large-scale AI training is a 'failure amplifier' for GPU clusters — is the literal description: a single link blip stalls a synchronous all-reduce, which stalls the step, which idles every GPU in the run.

MRC's economics fall directly out of this. Multi-plane support means a 100,000+ GPU cluster can be wired with only two tiers of switches instead of three, cutting power, component count, and the number of failure domains in the first place. Microsecond hardware failover means the failures that do happen don't propagate up into the training loop. And load balancing across all available paths means no GPU is starved of bandwidth mid-step. This is the layer of OpenAI's Stargate build-out that doesn't show up in renderings of new data centers but probably matters more than any individual building: the compute footprint only stretches as far as the network can keep it synchronized, and MRC is the protocol that lets the next doubling happen without re-architecting the fabric again. The community reaction has tracked this framing — the protocol's authors themselves headlined OpenAI's own podcast on the launch, and analyst commentary has converged on the 'fluid fabric at hyperscale' read rather than treating MRC as just another RDMA tweak.