What should I use for multi-node training, InfiniBand or Ethernet?

For multi-node training, the right choice is usually:

• InfiniBand if you want the best performance, lowest latency, and highest scaling efficiency
• Ethernet if you want lower cost, simpler existing infrastructure, and “good enough” performance for smaller clusters

Quick rule of thumb

• Large-scale distributed training (many GPUs/nodes, heavy all-reduce, large models): InfiniBand
• Small to medium clusters, budget-conscious setups, or existing datacenter networks: High-speed Ethernet (25/50/100/200/400 GbE)

Why InfiniBand is often preferred

• Lower latency
• Higher and more consistent bandwidth
• Better support for GPU-to-GPU communication patterns
• Often yields better scaling efficiency as you add nodes

This matters a lot for training workloads that frequently synchronize gradients.

When Ethernet is fine

Ethernet can work well if:

• You have a small cluster
• Your training is not extremely communication-heavy
• You already have high-speed switching and NICs
• Cost and operational simplicity matter more than absolute peak performance

Modern Ethernet can be very capable, especially with:

• RDMA over Converged Ethernet (RoCE)
• Proper tuning
• Lossless or well-managed network configuration

Practical recommendation

• If you’re building a serious AI training cluster from scratch and budget allows: InfiniBand
• If you’re leveraging existing infrastructure or want a more general-purpose network: high-speed Ethernet, ideally with RDMA/RoCE

If you want, I can also give you a decision matrix based on cluster size, budget, and model type.