Please recommend a GPU option for distributed training with high bandwidth networking

For distributed training with high-bandwidth networking, the strongest general recommendation is:

Best overall: NVIDIA H100 (SXM or PCIe) + InfiniBand / NVLink

• GPU: NVIDIA H100
• Networking: NVIDIA Quantum InfiniBand (200/400 Gb/s) or equivalent high-speed interconnect
• Why: H100 is currently one of the best choices for large-scale training because it offers:
  • Excellent FP8/FP16/BF16 performance
  • Strong scaling across nodes
  • Mature software stack (CUDA, NCCL, PyTorch/JAX/TensorFlow support)
  • Best-in-class ecosystem for distributed training

If you want the highest intra-node performance:

• Choose H100 SXM with NVLink/NVSwitch
• This is ideal if your training runs fit well within multi-GPU nodes and you still want strong multi-node scaling.

If you want a more practical/accessible option:

• NVIDIA L40S or A100 can be good alternatives, but they’re generally less optimal for cutting-edge large-model distributed training than H100.
• If budget is a major concern, A100 80GB + 200Gb InfiniBand remains a very solid distributed-training setup.

Recommended setup by scale

• Small/medium clusters:
  8× H100 SXM per node + InfiniBand
• Cost-conscious but strong:
  8× A100 80GB per node + 200Gb InfiniBand
• Very large training workloads:
  H100/H200 clusters with InfiniBand and NVSwitch

Key networking features to look for

For distributed training, prioritize:

• InfiniBand over standard Ethernet if possible
• 200 Gb/s or higher
• RDMA support
• Low-latency fabric
• NCCL-optimized topology

Short answer

If you want one clear recommendation:
NVIDIA H100 with InfiniBand (200/400 Gb/s), ideally in an NVLink/NVSwitch-enabled node.

If you tell me your budget, model size, and whether this is single-node or multi-node, I can recommend a more precise configuration.