Distributed Training Optimization
Multi-node training scaling at 30% instead of 90%. GPUs sitting idle during AllReduce. NCCL timeouts killing overnight runs. We fix the network and configuration layer that causes all of this.
What We Do
- NCCL tuning — algorithm selection (Ring/Tree/CollnetDirect), protocol tuning, buffer sizing, thread configuration for your specific topology
- RDMA/RoCE configuration — PFC, ECN/DCQCN, GID indexes, traffic class, DSCP marking, end-to-end lossless validation
- InfiniBand optimization — subnet manager config, adaptive routing, partition keys, rail-optimized topologies
- GPUDirect RDMA setup — zero-copy GPU-to-GPU transfers, peer memory modules, GDR copy validation
- Topology-aware optimization — NVLink/NVSwitch intra-node routing, PCIe affinity alignment, NUMA-aware process placement
- Profiling and diagnostics — perftest benchmarks, NCCL test validation, bandwidth calculators, bottleneck identification
Proof
We helped a computer vision company go from TCP-based multi-node training to GPUDirect RDMA over RoCE on bare-metal Kubernetes. Result: 8.5x training throughput improvement and 10x latency reduction.
How We Work
Assess
Profile your network fabric, NCCL config, and GPU topology.
Diagnose
Find the real bottleneck. It's usually the network.
Implement
Tune NCCL, configure RDMA, fix switch configs.
Transfer
Document everything so your team operates independently.
Technologies
Related
Frequently Asked Questions
What is distributed training optimization?
It's the practice of tuning GPU networking, NCCL, and collective-communication paths so multi-node training scales near-linearly with node count — RDMA/RoCE configuration, GPUDirect RDMA, NCCL algorithm tuning, and topology-aware process placement to eliminate network-induced GPU idle time.
How do I know if my multi-node training is network-bound?
If scaling from 8 to 64 GPUs delivers only 2-3x speedup instead of ~8x, or GPU utilization drops below 50% during AllReduce, the network is the bottleneck. Profiling with nccl-tests and per-iteration timing of AllReduce vs compute will confirm it.
What is the difference between NCCL over TCP and NCCL over RDMA?
NCCL over TCP goes through the kernel networking stack. NCCL over RDMA (RoCE v2 or InfiniBand) bypasses the CPU, uses zero-copy GPU-to-GPU transfers via GPUDirect RDMA, and delivers 5-10x higher effective bandwidth and an order-of-magnitude lower latency.
Do I need InfiniBand, or is RoCE enough?
Both work. InfiniBand is a lossless, purpose-built fabric standard in DGX SuperPOD deployments. RoCE v2 runs RDMA over Ethernet and achieves comparable throughput when configured correctly with PFC and ECN — often the better fit for cloud, colo, and bare-metal Kubernetes clusters.
Can you fix distributed training issues without changing hardware?
Often, yes. Many underperforming clusters are misconfigured rather than under-provisioned: NCCL falling back to TCP, PFC/ECN disabled, wrong IB_HCA selection, NUMA-misaligned processes. We frequently deliver 2-5x speedups using existing NICs and switches.
What results do BaaZ clients typically see?
3-8x higher training throughput moving from TCP to properly configured RDMA, inter-node latency dropping from hundreds of microseconds to single digits with GPUDirect, scaling efficiency above 90% across 8-16 nodes, and elimination of NCCL timeouts.
Struggling with Multi-Node Training?
No sales pitch. We'll look at your NCCL config and network fabric and tell you what's wrong.
Schedule a Call