Why AI needs a new kind of supercomputer network — the OpenAI Podcast Ep. 18

ℹ️ EPISODE INFO

Released

May 6, 2026

Duration

37m

Channel

OpenAI

Watch on YouTube

▶ Open ↗

📝 EPISODE DESCRIPTION

Training frontier models isn’t as simple as adding more GPUs—one small problem and the whole coordinated dance falls apart. OpenAI’s Mark Handley and Greg Steinbrecher discuss how a new supercomputer network design, used to train some of the company’s latest models, keeps the whole system moving in lockstep, even with record numbers of GPUs. They break down Multipath Reliable Connection, a new protocol OpenAI developed with AMD, Broadcom, Intel, Microsoft, and Nvidia, and why they’re making it available for the whole industry to use. Chapters 00:00 Intro 00:39 Greg and Mark's paths to OpenAI 04:34 Why training AI stresses networks differently 10:05 Bottlenecks, failures, and the cost of waiting 15:19 How Multipath Reliable Connection works 18:59 A protocol to route around failures 25:05 Why OpenAI is making MRC an open standard 35:09 Could AI compute move to space?

🎤 SPEAKERS

• Andrew Mayne

  host

  Host of the OpenAI Podcast.

• Greg Steinbrecher

  guest

  OpenAI workload/infrastructure systems engineer focused on efficient large-scale GPU training and reliability.

• Mark Handley

  guest

  Networking researcher at OpenAI and professor at University College London specializing in large-scale network design and protocols.

🧾 EPISODE SUMMARY

In this episode of OpenAI, featuring Andrew Mayne and Greg Steinbrecher, Why AI needs a new kind of supercomputer network — the OpenAI Podcast Ep. 18 explores openAI’s MRC networking makes massive GPU training faster, resilient, simpler AI training workloads stress networks differently than traditional internet/web traffic because thousands of GPUs must communicate in lockstep, making worst-case latency and congestion the true limiter.

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