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New interconnects aim to challenge Nvidia's AI networking dominance

Two companies, Cornelis Networks and Delos Data, have introduced new technologies designed to provide open alternatives for scaling AI systems, potentially challenging Nvidia's NVLink.

  • Cornelis Networks introduced the Active Compute Fabric (ACF), an open architecture for scale-up and scale-out networking.
  • Delos Data is developing new network reference designs, including I/O dies and near-packaged optics, to speed up data movement in AI systems.
  • Both companies aim to offer alternatives to Nvidia's proprietary NVLink Fusion technology for connecting multiple GPUs.

Two companies have unveiled new technologies aimed at providing open alternatives for scaling artificial intelligence (AI) systems, potentially challenging Nvidia's proprietary NVLink interconnects. Cornelis Networks, an Intel spin-off, and newcomer Delos Data, presented their offerings at the AI Infra Summit this week.

Cornelis Networks introduced its Active Compute Fabric (ACF), an open architecture designed for scale-up and scale-out networking. ACF seeks to integrate programmable compute into the network fabric and expand on protocols like Ultra Accelerator Link (UALink) and Ethernet for Scale-Up Networking (ESUN). The company plans to launch its 800 Gbps-capable CN6000-series switches and NICs, with a focus on bringing its technology to a wider audience through the open Ultra Ethernet protocol.

Delos Data, founded by former Barefoot Networks and Intel executives, is focusing on the physical layer with new network reference designs under its Nonstop AI portfolio. These designs include I/O dies capable of over 30 Tbps of aggregate bandwidth and near-packaged optics (NPO) technology. Delos states its chiplets are protocol agnostic, meaning they can work with various interconnect protocols, and aim to provide a system-level blueprint for faster data movement and larger compute domains.

Why this matters: These developments could offer AI developers and hyperscalers more choice and potentially reduce reliance on a single vendor for critical AI infrastructure.

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