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arXiv · 2601.19132

In-Network Collective Operations: Game Changer or Challenge for AI Workloads?

Torsten Hoefler·Mikhail Khalilov·Josiah Clark·Surendra Anubolu·Mohan Kalkunte·Karen Schramm·Eric Spada·Duncan Roweth·Keith Underwood·Adrian Caulfield·Abdul Kabbani·Amirreza Rastegari
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Abstract

This paper summarizes the opportunities of in-network collective operations (INC) for accelerated collective operations in AI workloads. We provide sufficient detail to make this important field accessible to non-experts in AI or networking, fostering a connection between these communities. Consider two types of INC: Edge-INC, where the system is implemented at the node level, and Core-INC, where the system is embedded within network switches. We outline the potential performance benefits as well as six key obstacles in the context of both Edge-INC and Core-INC that may hinder their adoption. Finally, we present a set of predictions for the future development and application of INC.

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Torsten Hoefler, Mikhail Khalilov, Josiah Clark, Surendra Anubolu, Mohan Kalkunte, Karen Schramm, Eric Spada, Duncan Roweth, Keith Underwood, Adrian Caulfield, Abdul Kabbani, Amirreza Rastegari. 2026-01-27. In-Network Collective Operations: Game Changer or Challenge for AI Workloads?. https://doi.org/10.1109/mc.2025.3616048

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