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InfiniBand vs Ethernet for GPU Clusters
Network comparison for distributed AI training — InfiniBand vs Ethernet (RoCE).
Overview
Choosing between InfiniBand and Ethernet (with RoCE) for GPU cluster networking is a critical decision for distributed AI training performance. This comparison covers bandwidth, latency, cost, and use cases.
InfiniBand (NDR 400G)
Pros
- Lowest latency (<1 microsecond)
- Native RDMA support
- Superior for distributed training
- Industry standard for AI supercomputers
Cons
- Higher cost
- Limited vendor options (NVIDIA/Mellanox)
- Requires specialized switches
Key Specs
Bandwidth400 Gb/s (NDR)
Latency<1 microsecond
RDMANative
Switch VendorsNVIDIA (Quantum-2)
CostPremium
Use CaseDistributed training, HPC
Ethernet (RoCE 400G)
Pros
- Lower cost
- Multiple vendor options
- Easier to manage
- Familiar to IT teams
Cons
- Higher latency than InfiniBand
- Requires RoCE for RDMA
- More complex tuning for AI
Key Specs
Bandwidth400 Gb/s
Latency2-5 microseconds
RDMAVia RoCEv2
Switch VendorsCisco, Arista, NVIDIA, Juniper
CostLower
Use CaseGeneral datacenter, mixed workloads
Verdict
For large-scale distributed training (>32 GPUs), InfiniBand is the clear choice — its lower latency and native RDMA deliver 20-30% faster training. For smaller clusters or mixed workloads, Ethernet with RoCE may be sufficient and more cost-effective. Harch Corp uses InfiniBand for its GPU cloud clusters.