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ai intermediate ⏱ 20 minutes K8s 1.28+

Run NCCL AllGather Benchmarks for Model Parallel Validation

Use all-gather NCCL tests to evaluate GPU communication behavior and throughput for tensor-parallel and model-parallel distributed AI workloads on Kubernetes.

By Luca Berton β€’ β€’ πŸ“– 5 min read

πŸ’‘ Quick Answer: Execute all_gather_perf -b 8 -e 1G -f 2 -g 1 to validate communication efficiency for model-parallel patterns.

All-gather performance is important for tensor-parallel inference and training pipelines.

Benchmark Command

all_gather_perf -b 8 -e 1G -f 2 -g 1

Execution Tips

  • Keep pod CPU/memory limits realistic to avoid host bottlenecks.
  • Use fixed node placement between runs.
  • Run at least 3 iterations and compare averages.

Troubleshooting Signals

  • Large variance between runs suggests noisy neighbors or unstable links.
  • Sudden drops at specific sizes can indicate MTU or transport fallback issues.

Output to Store

Save logs with node names, GPU count, and NIC interface metadata for trend analysis.

#nccl #allgather #ai #model-parallel #performance
Luca Berton
Written by Luca Berton

Principal Solutions Architect specializing in Kubernetes, AI/GPU infrastructure, and cloud-native platforms. Author of Kubernetes Recipes and creator of CopyPasteLearn courses.

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