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

arXiv: https://arxiv.org/abs/2601.19132 · HTML: https://mlsanity.com/paper/2601.19132/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 27 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://mlsanity.com/paper/1910.02054/) (2019)
- [PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel](https://mlsanity.com/paper/2304.11277/) (2023)
- [Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks](https://mlsanity.com/paper/2102.00554/) (2021)
- [Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/1802.09941/) (2018)
- [Microscaling Data Formats for Deep Learning](https://mlsanity.com/paper/2310.10537/) (2023)
- [SparCML: High-Performance Sparse Communication for Machine Learning](https://mlsanity.com/paper/1802.08021/) (2018)
- [Near-Optimal Sparse Allreduce for Distributed Deep Learning](https://mlsanity.com/paper/2201.07598/) (2022)
- [Flare: Flexible In-Network Allreduce](https://mlsanity.com/paper/2106.15565/) (2021)
- [Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for Distributed AI](https://mlsanity.com/paper/2408.13356/) (2024)
- [sPIN: High-performance streaming Processing in the Network](https://mlsanity.com/paper/1709.05483/) (2017)

## Abstract (arXiv)

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.