NCCL Rank Stuck / Straggler
NCCL rank stuck errors occur when one rank cannot keep up with the collective operation, slowing down all ranks.
NCCL rank stuck errors occur when one rank cannot keep up with the collective operation, slowing down all ranks.
What this failure is
NCCL Rank Stuck / Straggler is a Communication failure seen during ML training runs. NCCL rank stuck errors occur when one rank cannot keep up with the collective operation, slowing down all ranks. Common tags: Nccl, Straggler, Rank, Communication.
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Why it happens (the mechanism)
Straggler GPU has higher latency or hardware degradation. Network between straggler rank and others is slower. Straggler rank has contention with other workloads. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- One rank takes 10x longer than others
- Performance degrades over time as ranks fall behind
- GPU utilization varies wildly across ranks
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| Per-rank NCCL collective time varies significantly | Straggler GPU has higher latency or hardware degradation |
| Straggler rank has lower GPU utilization | Network between straggler rank and others is slower |
| Training throughput plateaus despite more compute | Straggler rank has contention with other workloads |
Which systems are affected
- Heterogeneous GPU clusters
- Multi-node training with varying network latency
- Mixed hardware generations in same training run
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: Per-rank NCCL collective time varies significantly
- ✓Verified signal present: Straggler rank has lower GPU utilization
- ✓Verified signal present: Training throughput plateaus despite more compute
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Diagnose this failure in VS Code
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
Install the free VS Code extensionNCCL errors in context
NCCL is where a distributed job reports failure, which is not the same as where it failed. The hub lists every common NCCL error next to what it actually indicates, and the environment variables that tell them apart.
Compare every nccl error side by sideRelated failures to investigate next
Root cause
- Straggler GPU has higher latency or hardware degradation
- Network between straggler rank and others is slower
- Straggler rank has contention with other workloads
The fix and how to prevent it
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