Building data centre networks for AI workloads: key requirements and considerations for CSPs
11 October 2023 | Research and Insights
Strategy report | PPTX and PDF (6 slides) | Cloud and AI Infrastructure
"For AI workloads, the performance of back-end networks in data centres will have a significant impact on GPU usage and consequently job completion time."
As communications service providers (CSPs) train continuously larger AI models (especially generative AI models), models must be trained on an increasingly large number of graphics processing units (GPUs). CSPs that want to train these models in their own data centres will need to upgrade their back-end networks, that is, the networks which connect different GPUs within a data centre.
Information included in this report
- Insights into the back-end data centre networking requirements of AI workloads
- Analysis of the approaches to AI networking that CSPs can adopt in their data centres
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Author
Joseph Attwood
Senior AnalystRelated items
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