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Supporting the AI Data Center Evolution
Optimizing AI infrastructure investments, reducing testing complexity.
AI data center network workloads are rapidly growing as new models introduce billions and soon trillions of dense parameters and vast increases of mission-critical traffic. Ensuring data center infrastructures can meet high performance demands of expanding AI use cases is imperative.
AI model complexity drives exponential increases in data center requirements. AI-powered apps require thousands of GPU accelerators as cluster sizes multiply rapidly. The network bandwidth needed to support these operations will soon surpass 1Tbps, with traffic increasing ten-fold. Even slight increases in network latency or dropped packets can disrupt mission-critical AI operations.
AI investments are ramping quickly as stakeholders rush to innovate and prove AI use cases. A thriving ecosystem is developing, but AI opportunities are only as viable as the networking infrastructure that supports them.
Backend architecture requirements differ from traditional frontend access networks. The architecture must be scalable and provide low latency, near lossless communication, and high bandwidth connectivity between servers, storage and accelerators essential for AI training and inference. AI data center networks require continuous validation to ensure reliable performance and resilience.
AI Workload Emulation Platform
The rapid growth of AI and its intensive processing demands are pushing data center networks to their limits. Traditional validation methods – building large xPU server farms – are costly, resource-intensive, and difficult to implement. To meet these challenges, Spirent introduced the industry’s first AI workload traffic emulation solution, enabling realistic, scalable, and repeatable testing for next-generation AI infrastructures.
Spirent’s solution emulates highly realistic AI traffic patterns using RoCEv2 transport and integrated Collective Communications Library (CCL) support. It offers actionable KPIs, rapid diagnostics, and cost-effective testing, eliminating the need for extensive hardware setups. Designed for deployment in existing labs, it delivers significant energy savings while ensuring AI-ready Ethernet infrastructures are reliable, efficient, and scalable. Spirent’s innovative approach accelerates validation and optimization for the high-performance networks driving AI innovation.
AI Network Testing
Expert Services
AI Workload Emulation Platform
Related Use Cases
High-Speed Ethernet & IP Network Validation
Continuous Testing for CI/CD
All AI Networking Use Cases
Use Case Library
AIネットワークのテスト
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