Today’s AI Systems and need for High-speed interconnects Modern AI systems resemble supercomputers far more than traditional web-scale architectures. This is because AI workloads, especially distributed training using data-parallel, model-parallel, or tensor-parallel techniques, place enormous demands on east-west bandwidth between GPUs. These GPU-to-GPU exchanges include: Gradient sharing Parameter synchronization Checkpointing and state transfers Collective operations such
AI's Multiple Networks: Understanding the Layers of Connectivity Modern AI infrastructure uses multiple specialized networks, each with distinct performance, reliability, and cost requirements. These networks are typically organized into four or five tiers, ranked below from lowest to highest by speed, performance demands, and connectivity needs. Because each network tier has different requirements, they use different cabling and hardware at varying price points. Within each
The Critical Role of Networking in AI Beyond power and cooling, and beyond the specialized servers and AI accelerators themselves, one of the most essential, and often underestimated, components of any AI infrastructure is the network fabric that interconnects those systems. In NVIDIA’s AI Factory reference model, the network is not a supporting element; it is a core pillar that determines how efficiently AI workloads scale, communicate, and perform. Just as AI chips continue
