Your network defines your AI factory. Every workload relies on fast, efficient data transfers between – or within – systems. Whether you need the throughput of NVLink and the scale of InfiniBand, or an Ethernet solution like Spectrum-X that slots into your existing layout, as an NVIDIA Elite Partner for Networking, we’ll help you design the right topology for your workloads.
When building any AI factory networking is key to maximising the potential from your compute. Begin by asking yourself a few key questions:
The answers to these questions will help define your network’s 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’re running multinode training with 8 GPUs per host, east-west traffic may require its own dedicated network.
Few teams set out to build a cloud, but as an AI cluster grows, that’s effectively what it becomes – 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 ‘chainsaw’ approach of hard isolation via manual rewiring unworkable.
Network automation solves this by abstracting the switch fabric, letting you define tenants and apply policies without touching a single port. That’s how hyperscalers operate, and with platforms like Netris, it’s now accessible to everyone else, on standard hardware, without stitching together scripts, firewalls, and manual config.
NVIDIA’s acquisition of Mellanox in 2019 laid the groundwork for its networking strategy, enabling breakthrough advances in high-performance, low-latency connectivity to power modern AI workloads. This section breaks down the key architectural families and how we see them being used in practice.
The NVIDIA Quantum-2 InfiniBand Platform is the gold standard for AI networking. It brings together NVIDIA® ConnectX® network adapters, high-performance switches and a diverse range of interconnects into an end-to-end unified solution. The result is a best-in-class networking fabric designed for maximum throughput and ultra-low latency.
If your AI factory relies on tightly coupled, multi-node GPU clusters (such as for LLM training or large-scale simulation) nothing else comes close. InfiniBand is ideal for sovereign AI infrastructure or national research labs. But like any specialised architecture, it doesn’t fit everyone’s use case.
InfiniBand also relies on a fully integrated stack, so you’ll be working within NVIDIA’s ecosystem for fabric control, routing, and telemetry. If you’d like to keep your options more open, NVIDIA’s Ethernet-based designs may offer you more flexibility.
The NVIDIA Spectrum-X™ Networking Platform is the company’s Ethernet fabric for AI. It pairs Spectrum-4 switches with NVIDIA® BlueField®-3 SuperNICs to deliver predictable, high-throughput communication between GPU servers using a medium most teams are already familiar with.
It’s built for environments where GPUs are shared across teams or workloads, and where network behaviour needs to stay consistent under pressure. If you’re already running Ethernet, Spectrum-X gives you a way to scale without rearchitecting your whole stack. You keep your tools and protocols, but gain better control over performance. It’s not a limiting factor on scale either: Spectrum-X Ethernet is also powering xAI’s 100,000 GPU ‘Colossus’ cluster.
The key is to treat it as a system, not just a switch fabric. To unlock the full benefits of Spectrum-X, you’ll need at least ConnectX-7 or BlueField-3 SuperNICs because earlier adapters don’t provide the required RoCE and congestion-control capabilities.
Spectrum Ethernet is NVIDIA’s general-purpose switching platform. You can run it with NVIDIA® ConnectX® NICs, BlueField DPUs, or just the switches on their own. It’s fast, standards-based, and easy to integrate into existing environments.
The key difference is system design. Spectrum-X gives you consistency and workload isolation, but it asks you to buy into the full stack. Spectrum Ethernet is more flexible. You can scale it gradually, mix it with other vendors, and choose your own automation tools.
If you don’t need performance isolation or deterministic latency across AI workloads – or if your GPUs are mostly contained within single nodes – Spectrum Ethernet may give you all the speed you need without confining you to a vertically integrated stack. It also opens the door to rail-optimised topologies and hybrid builds, especially in mixed vendor or legacy environments.
The NVIDIA® BlueField® Networking Platform extends the reach of AI networking by moving more of the infrastructure load off the CPU and into the network itself. Whether you’re deploying Spectrum-X fabrics or scaling a full SuperPOD, BlueField acts as the intelligent engine behind secure, high-performance, software-defined networks.
There are two main variants. The BlueField DPU is a cloud infrastructure processor that offloads networking, storage, and security tasks, freeing up host CPUs and enabling zero-trust architecture across every node. The BlueField SuperNIC, on the other hand, is built specifically for GPU-to-GPU traffic, delivering 400Gb/s of RoCE connectivity and performance isolation between jobs or tenants.
BlueField is essential for large-scale AI factories that need predictable bandwidth, secure multi-tenancy, and fine-grained policy control at the hardware level. It’s also key in unlocking Spectrum-X performance.
Leaf-spine has been the foundation of data centre networking for years, but it wasn’t built for the extreme bandwidth and all-to-all communication patterns of some AI workloads. To address this, NVIDIA developed rail-optimised networking – a fabric design that uses Top-of-Rack switches to give every GPU a dedicated one-hop path across the cluster.
In practice, each GPU connects to its own NIC and switch port, which means more cables, more switches, and careful rack-level planning. Airflow and cable management also become critical in high-density environments. While it requires additional design discipline, the payoff is a network that delivers predictable performance and the ultra-low latency needed to scale demanding AI workloads efficiently. For lighter inference tasks, however, traditional leaf-spine topologies may still provide sufficient performance.
Our on-site AI Lab is set up to help you validate storage and networking designs under real AI workloads. Through Vespertec’s Test Drive programme, you can remotely access NVIDIA-certified infrastructure to test switch configurations, evaluate network automation, and understand how storage traffic flows under pressure.
Whether you’re testing InfiniBand or Ethernet, rail-optimised topologies or RoCE tuning, this hands-on environment gives you a practical way to de-risk decisions before full deployment.
