Introduction
Networking is often the bottleneck in distributed AI training. Choosing between InfiniBand and Ethernet significantly impacts performance, cost, and operational complexity.
Why Networking Matters for AI
Modern AI training distributes work across multiple GPUs:
- Data Parallelism: Replicate model, split data
- Model Parallelism: Split model across GPUs
- Pipeline Parallelism: Split by layers
All approaches require frequent communication:
- Gradient synchronization
- Activation exchanges
- Parameter updates
Network latency and bandwidth directly impact training time.
InfiniBand Overview
InfiniBand is a Linux Kernel Tuning for High-Performance Workloads" class="internal-link">high-performance interconnect designed for HPC:
Key Characteristics
- High Bandwidth: Up to 400 Gbps (HDR)
- Low Latency: Sub-microsecond
- RDMA: Remote Direct Memory Access
- Lossless: Credit-based flow control
InfiniBand Generations
| Generation | Speed | Common Use |
|---|---|---|
| QDR | 40 Gbps | Legacy |
| FDR | 56 Gbps | Older clusters |
| EDR | 100 Gbps | Current common |
| HDR | 200 Gbps | New deployments |
| NDR | 400 Gbps | Cutting edge |
NVIDIA Networking Stack
NVIDIA GPU clusters typically use:
- ConnectX adapters: GPU-direct RDMA
- Mellanox switches: High-radix switching
- SHARP: In-network computing for collectives
Ethernet Options
Modern Ethernet has evolved for demanding workloads:
RoCE (RDMA over Converged Ethernet)
- RDMA capabilities over Ethernet
- Requires Priority Flow Control (PFC)
- Lower cost than InfiniBand
- More operational complexity
High-Speed Ethernet
- 100 GbE widely available
- 400 GbE emerging
- 800 GbE on roadmap
Ethernet Advantages
- Familiar operations: Standard networking skills
- Ecosystem: Broad vendor support
- Flexibility: General-purpose infrastructure
- Cost: Lower per-port costs
Performance Comparison
Latency
InfiniBand typically delivers:
- 0.5-1 microsecond point-to-point
- Consistent, predictable latency
Ethernet with RoCE:
- 1-3 microseconds typical
- More variable under congestion
Bandwidth
For 8-GPU nodes (DGX-style):
| Network | Bisection BW | Notes |
|---|---|---|
| HDR IB | 1.6 Tbps | Full bisection |
| 100 GbE | 800 Gbps | With 8 NICs |
| 400 GbE | 3.2 Tbps | Emerging |
Real-World Impact
Training performance difference depends on:
- Model size (larger = more communication)
- Batch size (larger = less frequent sync)
- Cluster size (more nodes = more traffic)
Typical observations:
- Small clusters (≤16 GPUs): 5-15% difference
- Large clusters (100+ GPUs): 20-40% difference
Cost Analysis
InfiniBand Costs
- Adapters: $1,500-3,000 each
- Switches: $20,000-100,000+
- Cables: $200-500 per connection
- Specialized skills required
Ethernet Costs
- NICs: $500-2,000 each
- Switches: $5,000-50,000
- Cables: $50-200 per connection
- Existing skills applicable
TCO Considerations
For a 32-GPU cluster:
- InfiniBand: ~$150,000-250,000 networking
- Ethernet (RoCE): ~$50,000-100,000
Factor in operational costs and training time value.
Architecture Recommendations
When to Choose InfiniBand
- Large-scale training (100+ GPUs)
- Latency-sensitive models (LLMs)
- Maximum performance required
- Dedicated Cloud Services" class="internal-link">AI infrastructure
When to Choose Ethernet
- Smaller clusters (≤32 GPUs)
- Mixed-use infrastructure
- Cost-constrained environments
- Existing Ethernet expertise
Hybrid Approaches
Some deployments use:
- InfiniBand within GPU pods
- Ethernet between pods/facilities
- NVLink for intra-node communication
Implementation Tips
InfiniBand Best Practices
- Use fat-tree topology for full bisection
- Enable SHARP for collective operations
- Tune NCCL for your topology
- Monitor fabric health continuously
Ethernet (RoCE) Best Practices
- Configure PFC correctly
- Use dedicated VLANs for RDMA
- Enable ECN for congestion signaling
- Test thoroughly before production
Conclusion
InfiniBand delivers superior performance but at higher cost and complexity. Ethernet with RoCE provides a practical alternative for many AI workloads. Choose based on cluster size, performance requirements, and operational capabilities.