Is this a hardware or a software issue?

Memory failures can look like either layer depending on the symptom. The discriminator is the resolution of the symptom to the recommended fix: if applying the steps under "How to fix it" eliminates the symptom, this is a software/configuration class issue regardless of the hardware it surfaced on. Repeat the fix on a fresh node to confirm.

How long does the fix typically take?

Most resolutions from this entry take 10 to 30 minutes of engineer time once the diagnosis is confirmed. Long fixes come from skipping the diagnosis step and chasing apparent symptoms (validation drift, memory growth, slow throughput) instead of the actual root cause listed above. Apply the fix in a small experiment first, then roll it forward.

Does this affect inference or only training?

Primarily this is a memory failure observed during training, but the same misconfiguration can leak into downstream evaluation and serving. Treat the prevention checklist as a guardrail: any pipeline that touches a trained checkpoint should re-validate the failure mode before promoting the model.

What's the cheapest way to confirm this is the problem?

Cheapest signal: reproduce the symptom on a 1-GPU toy run with the same configuration. If the toy run exhibits the same behavior, the cause is the configuration rather than the cluster. If only the multi-GPU run is affected, the failure is a distributed interaction, so PyTorch FSDP is the most likely layer to inspect.

Can the Denpex platform detect this automatically?

Yes. Denpex correlates the symptoms in this entry against your training logs in real time and surfaces the failure taxonomy, root cause, and exact remediation command on the console. Three full diagnoses are included free during signup, and the encyclopedia entry you are reading is generated from the same signal library that powers the platform.

What related failures should I check next?

Closely related failures cluster around #CPU OOM. After applying this fix, validate against the related entries below to confirm no overlapping cause is masking as this one. Engineers routinely discover that the dominant failure mode is actually a second, sibling failure compounded on top.

Will this recur after the fix?

Applied once with the prevention checklist, this failure does not recur under normal operation. The recurring variant most engineers encounter is the re-emergence of the same symptom on a different model or stack after CPU OOM is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. The training job suddenly gets OOM killed by the OS (dmesg shows python process killed) during the checkpoint saving phase. is the highest-signal fingerprint. Wire a metric or alert on that signal (or its absence after the fix) so you can detect the recurrence automatically. The Denpex platform encodes this exact fingerprint and surfaces it directly in the dashboard.

What is the official documentation that covers this?

Reference documentation is listed in the References section at the bottom of this entry. The most authoritative URLs are the upstream framework docs and the issue trackers cited; the Denpex entry synthesizes both into the executive, engineer, and on-call perspectives so each audience can act without reading every linked source.

Does this entry include working code?

Yes. The "Code Examples" section contains copy-paste-ready snippets that target the most common stacks (PyTorch, Hugging Face Trainer, DeepSpeed, Megatron-LM, vLLM/Transformers). Each snippet is annotated with why it works so you can adapt the pattern to your own framework without losing fidelity.

System Memory OOM on FSDP Checkpoint Save

Q: What does this failure look like in production?

In production this failure typically appears during the first tens-to-hundreds of training steps: loss curves become non-monotonic, validation metrics plateau above the baseline, and downstream checkpoints either diverge or fail to improve. Engineers frequently misread it as data drift when the underlying mechanism is when using `fullstatedictconfig(offload_to_cpu=true)` without setting `rank0_only=true`, fsdp instructs every single rank to independently gather and reconstruct the full model parameters into its own cpu memory. for a large model and multiple ranks on a node (e.g., 8 gpus), this creates 8 redundant copies of the full model in cpu ram, instantly exhausting system memory.. Validating against the symptom list in this entry is the fastest way to confirm the diagnosis.

Q: How does this scale from 1 GPU to 1000 GPUs?

At single-GPU scale the symptom usually looks like slow convergence or a single anomalous loss value. At 64+ GPU scale, distributed interactions amplify when using `fullstatedictconfig(offload_to_cpu=true)` without setting `rank0_only=true`, fsdp instructs every single rank to independently gather and reconstruct the full model parameters into its own cpu memory. for a large model and multiple ranks on a node (e.g., 8 gpus), this creates 8 redundant copies of the full model in cpu ram, instantly exhausting system memory. through collectives, and the failure can manifest as a full job abort. Always reproduce the fix at the smallest cluster configuration that still exhibits the symptom before promoting to production scale.

When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g., 8 GPUs), this creates 8 redundant copies of the full model in CPU RAM, instantly exhausting system memory.

Quick answer

Symptom: The training job suddenly gets OOM killed by the OS (dmesg shows python process killed) during the checkpoint saving phase.
Root cause: When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g.
Recommended fix: Enable rank0_only in FullStateDictConfig FSDP.set_state_dict_type(model, StateDictType.FULL_STATE_DICT, FullStateDictConfig(offload_to_cpu=True, rank0_only=True)) This restricts the full model gathering process to Rank 0 only, drastically reducing the CPU memory footprint during checkpointing.
How Denpex helps: Denpex matches System Memory OOM on FSDP Checkpoint Save across every rank in a distributed run and reports which rank failed first, so you act on the initiating node instead of the loudest one.

Memory#CPU OOM

What this failure is

System Memory OOM on FSDP Checkpoint Save is a Memory failure seen during ML training runs. When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g., 8 GPUs), this creates 8 redundant copies of the full model in CPU RAM, instantly exhausting system memory. Common tags: CPU OOM.

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Why it happens (the mechanism)

Engineers often assume `offload_to_cpu=True` automatically implies that only Rank 0 needs the state, or they monitor GPU memory for OOMs and get confused when the process is killed for System RAM OOM. PyTorch does not default to `rank0_only=True` because some users may want each rank to save a copy.

What you'll observe

Killed
MemoryError
out of memory in torch.cat

Common symptoms and what they mean

Symptom	Why it happens
The training job suddenly gets OOM killed by the OS (dmesg shows python process killed) during the checkpoint saving phase.	When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g., 8 GPUs), this creates 8 redundant copies of the full model in CPU RAM, instantly exhausting system memory.
GPU memory looks fine, but CPU memory spikes massively right before the crash.	When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g., 8 GPUs), this creates 8 redundant copies of the full model in CPU RAM, instantly exhausting system memory.
The crash happens simultaneously across multiple nodes or local ranks.	When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g., 8 GPUs), this creates 8 redundant copies of the full model in CPU RAM, instantly exhausting system memory.

Which systems are affected

PyTorch FSDP
CPU RAM

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.

✓Check `dmesg -T` for OOM Killer invoking on the python process.
✓Monitor `htop` or system memory metrics during the `model.state_dict()` call.
✓Verify the `StateDictConfig` passed to `FSDP.set_state_dict_type`.

Searchable error signature

search key

The training job suddenly gets OOM killed by the OS (dmesg shows python process killed) during the checkpoint saving phase.
out of memory in torch.cat

Use this text as a lookup key in logs and upstream issue trackers. It is not presented as a captured customer log. Confirm the cause from your own preceding events, versions, configuration and the cited references.

The fix and the prevention pattern

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Root cause

When using `FullStateDictConfig(offload_to_cpu=True)` without setting `rank0_only=True`, FSDP instructs every single rank to independently gather and reconstruct the full model parameters into its own CPU memory. For a large model and multiple ranks on a node (e.g., 8 GPUs), this creates 8 redundant copies of the full model in CPU RAM, instantly exhausting system memory.

The fix and how to prevent it

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References

↗FSDP state_dict OOM issues

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