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The network fabric isn't just a connection: It's the neural system affecting job completion time

  • Inference Clusters

    AI inference infrastructure transforms trained models into production-ready systems that deliver real-time insights and automation with Inference-optimized hardware.

  • Training Clusters

    AI training clusters are the backbone of modern machine learning operations, providing the computational power necessary to train complex advanced AI models.

  • Enterprise Clusters

    Converting AI models into production services requires sophisticated traffic management to ensure consistent performance under extreme load with mixed enterprise workloads.

The backbone of a modern AI stack requires specialized networking

  • RDMA (Remote Direct Memory Access)

    Integral to an AI stack, RoCE v2 technology that bypasses CPU involvement for memory transfers, reducing latency to microseconds for

  • Network Architecture Fundamentals

    Non-blocking network topologies, deterministic performance and 1.6 Tbps aggregate bandwidth per node required for a modern AI stack

  • Ultra Ethernet Consortium

    Ethernet based open, interoperable, high performance, full-communications for AI stack architecture to meet AI network demands at scale

200G and 400G for Inference and Training

OSFP PAM4 for 400G or 25G/10G NRZ for lower speeds

  • 1.6T Ethernet GPU Node

    1.6T Ethernet GPU Nodes combine four 400G NICs to deliver an unprecedented 1.6 Tbps of aggregate bandwidth—revolutionizing both AI training and inference operations. Advanced connectivity infrastructure provides the foundation for your AI stack initiatives to achieve breakthrough performance and a dramatic reduction in job completion time (JCT). The 1.6 Tbps aggregated connection delivers measurable improvements to your training workflows with Up to 70% reduction in training time for large language models vs. traditional 100G networks.

  • 800G Ethernet Service Node

    800G Ethernet service nodes represent the sweet spot for organizations balancing performance with investment. Up to 45% reduction in training cycles compared to single 400G configurations. 800G provides enhanced gradient synchronization efficiency during distributed training with balanced communication-to-computation ratio optimized so that modern AI frameworks Enterprises can take advantage of Superior scalability when adding nodes to clusters. Provides near-perfect GPU communications utilization without constraining compute on an AI stack.

  • 400G Ethernet Edge AI Node

    400G Ethernet Edge AI Nodes brings unprecedented computational power and network capacity to the edge of your infrastructure. Edge AI platforms enables real-time intelligence exactly where your data originates, eliminating backhaul latency while maintaining seamless integration with your core AI stack. The 400G connectivity fundamentally changes what's possible at the edge. Industry-leading bandwidth delivering 4x the throughput of traditional edge solutions providing ultra-low latency communication with core data centers and cloud resources.

Transformative Edge AI

  • First Mover Advantage

    Real-time quality control with microsecond response to detected anomalies with predictive maintenance detecting equipment failures before they occur

  • Seamless Integration

    Centralized management from the same tools that control your core AI systems. Model synchronization ensuring edge deployments remain current

  • Operational Excellence

    80% reduction in decision latency compared to cloud-dependent architectures. 95% decrease in data transfer costs through via processing and filtering

NVIDIA Blackwell Architecture Advancements

An integral part of the AI Stack is the NVIDIA DGX B200. Enterprises can provide engineers with a single, unified platform built to accelerate AI workflows. Ready for the demands of generative AI, the enterprise can accelerate AI into their daily operations for faster time to market and product differentiation.

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