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 DeepSpeed 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 #Checkpoint 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 Checkpoint OOM is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Training runs completely fine and stably, but suddenly crashes with a CUDA OOM precisely when attempting to save the first checkpoint. 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.

Rank 0 OOM During ZeRO-3 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 by default, deepspeed's zero-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. this instructs deepspeed to unshard and consolidate all model weights onto a single gpu (rank 0) to save a standard fp16 model file. for massive models, gathering the entire model state on a single gpu immediately exceeds its vram capacity.. 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 by default, deepspeed's zero-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. this instructs deepspeed to unshard and consolidate all model weights onto a single gpu (rank 0) to save a standard fp16 model file. for massive models, gathering the entire model state on a single gpu immediately exceeds its vram capacity. 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.

By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. This instructs DeepSpeed to unshard and consolidate all model weights onto a single GPU (Rank 0) to save a standard fp16 model file. For massive models, gathering the entire model state on a single GPU immediately exceeds its VRAM capacity.

Quick answer

By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true.

Symptom: RuntimeError: CUDA out of memory. Tried to allocate...
Root cause: By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. This instructs DeepSpeed to unshard and consolidate all model weights onto a single GPU (Rank 0) to save a standard fp16 model file. For massive models, gathering the entire model state on a single GPU immediately exceeds its VRAM capacity.
Recommended fix: zero_to_fp32.py
How Denpex helps: Denpex matches Rank 0 OOM During ZeRO-3 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#Checkpoint OOM

What this failure is

Rank 0 OOM During ZeRO-3 Checkpoint Save is a Memory failure seen during ML training runs. By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. This instructs DeepSpeed to unshard and consolidate all model weights onto a single GPU (Rank 0) to save a standard fp16 model file. For massive models, gathering the entire model state on a single GPU immediately exceeds its VRAM capacity. Common tags: Checkpoint OOM.

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Why it happens (the mechanism)

Users assume that if a model fits in VRAM for training (forward/backward passes), it should easily fit for checkpoint saving. They don't realize ZeRO-3 breaks the model across GPUs and reconstructs it on a single device for saving.

What you'll observe

RuntimeError: CUDA out of memory. Tried to allocate...
[Rank 0] OOM encountered during save_checkpoint

Common symptoms and what they mean

Symptom	Why it happens
Training runs completely fine and stably, but suddenly crashes with a CUDA OOM precisely when attempting to save the first checkpoint.	By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. This instructs DeepSpeed to unshard and consolidate all model weights onto a single GPU (Rank 0) to save a standard fp16 model file. For massive models, gathering the entire model state on a single GPU immediately exceeds its VRAM capacity.
Only rank 0 experiences the Out of Memory error; other ranks sit idle or timeout.	By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. This instructs DeepSpeed to unshard and consolidate all model weights onto a single GPU (Rank 0) to save a standard fp16 model file. For massive models, gathering the entire model state on a single GPU immediately exceeds its VRAM capacity.

Which systems are affected

DeepSpeed
PyTorch
CUDA

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 the DeepSpeed configuration JSON for `gather_16bit_weights_on_model_save`.
✓Monitor `nvidia-smi` during checkpointing and watch Rank 0 VRAM spike dramatically right before the crash.

Searchable error signature

search key

RuntimeError: CUDA out of memory. Tried to allocate...

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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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.

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DeepSpeed 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 side

Root cause

By default, DeepSpeed's ZeRO-3 config parameter `gather_16bit_weights_on_model_save` is often set to true. This instructs DeepSpeed to unshard and consolidate all model weights onto a single GPU (Rank 0) to save a standard fp16 model file. For massive models, gathering the entire model state on a single GPU immediately exceeds its VRAM capacity.

The fix and how to prevent it

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References

↗DeepSpeed Checkpointing Documentation

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