🎀Speaking at Red Hat Summit 2026GPUs take flight: Safety-first multi-tenant Platform Engineering with NVIDIA and OpenShift AILearn More
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.

Want More Kubernetes Recipes?

This recipe is from Kubernetes Recipes, our 750-page practical guide with hundreds of production-ready patterns.

Luca Berton Ansible Pilot Ansible by Example Open Empower K8s Recipes Terraform Pilot CopyPasteLearn ProteinLens