DeepSpeed OOM
DeepSpeed OOM errors occur when ZeRO partitioning, CPU offload, or activation partitioning is misconfigured.
DeepSpeed OOM errors occur when ZeRO partitioning, CPU offload, or activation partitioning is misconfigured.
What this failure is
DeepSpeed OOM is a Memory failure seen during ML training runs. DeepSpeed OOM errors occur when ZeRO partitioning, CPU offload, or activation partitioning is misconfigured. Common tags: Deepspeed, Zero, Offload, Memory.
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Why it happens (the mechanism)
ZeRO stage not aggressive enough. CPU offload not enabled. Activation checkpointing not used. Sub-optimal config for model size. Offload too much to slow storage. 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
- DeepSpeed training OOMs
- ZeRO-3 OOM despite offloading
- DeepSpeed config doesn't reduce memory
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| CUDA OOM with DeepSpeed | ZeRO stage not aggressive enough |
| ZeRO-3 still OOMs | CPU offload not enabled |
| CPU offload still OOMs | Activation checkpointing not used |
Which systems are affected
- Large model training with DeepSpeed
- LLM training with ZeRO
- Memory-efficient training
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: CUDA OOM with DeepSpeed
- ✓Verified signal present: ZeRO-3 still OOMs
- ✓Verified signal present: CPU offload still OOMs
- ✓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 extensionDeepSpeed errors in context
DeepSpeed changes when parameters, gradients and optimizer state are created, partitioned, gathered and offloaded. The hub separates ZeRO, memory, checkpoint and pipeline failures by lifecycle phase.
Compare every deepspeed error side by sideRelated failures to investigate next
Root cause
- ZeRO stage not aggressive enough
- CPU offload not enabled
- Activation checkpointing not used
- Sub-optimal config for model size
- Offload too much to slow storage
The fix and how to prevent it
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Don't just read the fix, diagnose your run
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