AI cluster networking: Paving the way for a transformational 2025

Telecom and cloud providers urged to focus on optimisation as AI demands surge. Existing infrastructure must be maximised to support emerging AI workloads.

Keysight Technologies, Inc. and Heavy Reading have shared a pivotal 2025 report on AI cluster networking. As artificial intelligence adoption outpaces infrastructure development, telecom and cloud providers are urged to pivot from expansion to optimisation to handle next-generation AI tasks.

AI growth in various industries increases demands on data centres. However, traditional expansion initiatives seem inadequate. A significant 62% of respondents prefer maximising current infrastructure over new investments. This prompts operators to embrace performance optimisation strategies, such as real-world AI workload emulation to validate and enhance deployment efficiency for AI clusters.

The report, which drew insights primarily from industry respondents, showed 89% planning to either expand or maintain AI infrastructure investments. The predominant factors propelling this trend include cloud integration (on the rise at 51%), faster GPUs' deployment (49%), and high-speed network upgrades (45%).

Important findings from the report, titled Beyond the Bottleneck: AI Cluster Networking Report 2025, include

  • Optimisation First Approach: Investment persists, but 62% say they focus on extracting value from current infrastructure sans new capital expenditures.
  • Emulation Becomes Essential: A steep 95% emphasise the need for real-world workload emulation, despite lacking requisite simulation tools.
  • Rising Infrastructure Pressure: Budget constraints (59%), infrastructure limitations (55%), and talent shortages (51%) are major hurdles.
  • High-Speed Networking Expansion: Technologies like 800G, 1.6T, and Ultra Ethernet are explored or evaluated, reflecting growing momentum.
  • Network Bottlenecks at the Forefront: An increasing interest in 1.6T and extensive 400G deployments spotlight network capacity as crucial for scaling AI.

The research highlights a transformation in industry thinking: it's no longer solely about infrastructure capacity but about optimising efficiency and reliability. As sophisticated AI models become mainstream, the importance of real-world AI workload emulation is underscored, offering a way to unlock infrastructure potential while managing costs.

"AI data centres are reaching a tipping point where performance and scale alone are not enough. Operators need deeper insight, tighter validation, and smarter infrastructure choices," explained Ram Periakaruppan, Vice President and General Manager, Network Applications & Security Group at Keysight, indicating the criticality of optimising networks in the AI era.

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