Collection: Switches

The Spine of Your AI Cluster

Ask any network engineer who has built an AI training cluster what they wish they'd known before they started, and most of them will say the same thing: "I underspecced the switch." It's the most common and most expensive mistake in AI infrastructure design. You invest in the right GPUs, the right servers, the right storage — and then you connect them with a switch that becomes the bottleneck the moment you scale past a handful of nodes.

DVUN's switch collection is built to prevent that mistake. We stock high-radix, low-latency switching platforms specifically selected for AI cluster topologies — spine-leaf architectures, fat-tree fabrics, and rail-optimized designs that keep your GPU-to-GPU communication fast and your training jobs efficient. From 25GbE access-layer switches for smaller clusters to 400GbE spine platforms for large-scale private AI clouds, every switch in this collection has been evaluated for its role in a real AI networking environment.

What We Look for in Every Switch We Stock

• RDMA & RoCEv2 Support: GPU-direct communication requires lossless Ethernet with Priority Flow Control (PFC) and ECN. We only stock switches that support this correctly.
• Low Cut-Through Latency: Sub-microsecond port-to-port latency for all-to-all collective operations in distributed training.
• High Radix Port Counts: 32-port to 128-port configurations to minimize the number of switch tiers in your fabric and reduce hop count.
• Flexible NOS Options: Support for SONiC, Cumulus, and vendor-native operating systems so you can manage your fabric with the tools your team already knows.
• Buffer Depth for Bursty AI Traffic: AI collective operations generate highly bursty traffic patterns. Adequate shared buffer memory prevents packet drops that kill training efficiency.
• Hot-Swap PSU & Fan Modules: Production AI clusters run 24/7. Switches with redundant, hot-swappable power and cooling are non-negotiable for uptime.

Designing Your Switching Fabric

Small Cluster (4–16 GPU nodes): A single high-radix 100GbE switch with RoCEv2 support is typically sufficient for a non-blocking fabric. Our 32-port and 48-port 100GbE platforms are the right starting point. Pair with our NICs for end-to-end 100G connectivity.

Large Cluster (32+ GPU nodes): A two-tier spine-leaf architecture with 400GbE spine switches and 100GbE leaf switches provides the bandwidth and scalability for serious training workloads. Our 400GbE spine platforms support up to 128 ports of 400GbE, giving you room to grow without a forklift upgrade. See our full Networking collection for the complete fabric build.

Switch Specifications at a Glance

• Port speeds: 25GbE, 100GbE, 200GbE, 400GbE, 800GbE
• Port counts: 32 to 128 ports per switch
• Switching capacity: up to 51.2Tbps on high-end spine platforms
• Latency: as low as 300ns cut-through on select platforms
• Buffer memory: up to 64MB shared buffer on AI-optimized platforms
• NOS support: SONiC, Cumulus Linux, vendor-native options
• Management: SNMP, gNMI, OpenConfig, REST API
• Power: redundant hot-swap PSU, 80 PLUS Platinum efficiency

Switch Smart. Scale Without Regret.

The switch you choose today determines the cluster you can build tomorrow. DVUN's switching portfolio gives you the headroom to grow from a pilot cluster to a production AI fabric without replacing your core infrastructure. Request a quote for full fabric designs, or contact our network architecture team for a topology recommendation based on your GPU count and training workload.