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ACE-AI delivers a unified fabric across the network for Distributed AI, from Datacenter to Edge to Multi-cloud

Distributed AI offers significant computational efficiency, scalability, security, and latency benefits. AI workloads are increasingly distributed. Examples of how AI is being distributed across the network include Distributed Model Training, where AI/ML models are trained on multiple nodes within the network, enhancing efficiency and performance for large, complex models. Additionally, Inferencing at the Edge involves deploying inferencing models closest to end users, with reduced latency and improved performance for applications. Key requirements for networks supporting distributed AI include high performance and lossless connectivity, predictable latency, high availability and resiliency with zero impact failover, and fabric-wide visibility.