{"id":110,"date":"2025-07-16T11:04:00","date_gmt":"2025-07-16T11:04:00","guid":{"rendered":"https:\/\/vespertec.com\/nvidia\/?page_id=110"},"modified":"2025-10-09T08:54:01","modified_gmt":"2025-10-09T08:54:01","slug":"networking","status":"publish","type":"page","link":"https:\/\/vespertec.com\/nvidia\/networking\/","title":{"rendered":"Networking"},"content":{"rendered":"
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\nAI Factories\n<\/div>

\nNetworking:
The Information Highway
of Your AI Factory\n<\/h1>
\n \n \n \n \n \n \n \n \n \n\n \n \n \n \n \n \n\n \n Talk to us about AI Factories<\/span>\n\n \n \n <\/a>\n\n \n\n\n<\/div>\n<\/div>
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\nOverview\n<\/h1>\n<\/div>
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Your network defines your AI factory. Every workload relies on fast, efficient data transfers between \u2013 or within \u2013 systems.\u00a0 Whether you need the throughput of NVLink<\/a> and the scale of InfiniBand<\/a>, or an Ethernet solution like Spectrum-X<\/a> that slots into your existing layout, as an NVIDIA Elite Partner for Networking, we\u2019ll help you design the right topology for your workloads.<\/p>\n<\/div>\n<\/div><\/div><\/div>\n<\/section>

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When building any AI factory networking is key to maximising the potential from your compute. Begin by asking yourself a few key questions:<\/strong><\/p>\n

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  • What workloads are you running?<\/li>\n
  • What are the demands on storage?\u00a0<\/li>\n
  • Will you run a dedicated backend storage network?\u00a0<\/li>\n
  • Does your workload need access to a unified pool of GPU compute and span many distributed systems?\u00a0<\/li>\n
  • What are the requirements for connection to the front-end client network?\u00a0<\/li>\n
  • Do you need fully non-blocking topologies, or will a degree of oversubscription be sufficient?\u00a0<\/li>\n
  • Will your system stress intra-node communication, inter-node, or both?<\/li>\n
  • Should you implement a centralised networking rack or top-of-rack switching?\u00a0<\/li>\n
  • How will you build automation into your network design?<\/li>\n<\/ul>\n

    The answers to these questions will help define your network\u2019s speed, layout, and switching tier. The last thing you want is for your highly valuable compute and storage resources to be held back by inefficiencies in the network. For example, if most of your traffic stays inside a single rack, you may not need a rail-optimised\/non-blocking topology. On the other hand, if you\u2019re running multinode training with 8 GPUs per host, east-west traffic may require its own dedicated network.<\/p>\n

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    \nPractical
    Considerations\n<\/h2>\n<\/div>\n<\/div>\n<\/div><\/div><\/div>\n<\/section>
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    \nNo matter your needs, we\u2019ll help you spec a fabric that fits your cluster: from cable runs and power shelves to cooling requirements and network automation. \n<\/div>\n<\/div><\/div><\/div>\n<\/section>
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    \nNetwork Automation\n<\/h2>\n<\/div>\n<\/div>\n<\/div>
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    Few teams set out to build a cloud, but as an AI cluster grows, that\u2019s effectively what it becomes \u2013 and the network grows with it. What started as a 32-node cluster suddenly needs 256 cables and eight switches. At the same time, the need to support multi-tenancy for different users or departments with varying access requirements makes the \u2018chainsaw\u2019 approach of hard isolation via manual rewiring unworkable.<\/strong> <\/p>\n

    Network automation solves this by abstracting the switch fabric, letting you define tenants and apply policies without touching a single port. That\u2019s how hyperscalers operate, and with platforms like Netris, it\u2019s now accessible to everyone else, on standard hardware, without stitching together scripts, firewalls, and manual config.

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    \nNetworking
    Intensive Use Cases\n<\/h2>

    \nHere are some of
    the most common
    applications driving
    demand for networking
    across our customer
    base. \n<\/h2>\n<\/div>
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    \nResources\n<\/h4>\n<\/div>
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    \nPractical guidance to help you accelerate performance, simplify operations, scale efficiently, and optimise total cost of ownership from your compute:\n<\/div>\n<\/div><\/div>
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