Is this a hardware or a software issue?

Model 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 model 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 Megatron 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 #megatron, #user-report. 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 megatron is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. CUDA out of memory 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.

MoE Activation Recomputation Export OOM

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 the conversion script loads the optimizer states into vram alongside the model parameters. for dense models, this fits. for moe models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single gpu.. 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 the conversion script loads the optimizer states into vram alongside the model parameters. for dense models, this fits. for moe models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single gpu. 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.

The conversion script loads the optimizer states into VRAM alongside the model parameters. For dense models, this fits. For MoE models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single GPU.

Quick answer

The conversion script loads the optimizer states into VRAM alongside the model parameters.

Symptom: CUDA out of memory
Root cause: The conversion script loads the optimizer states into VRAM alongside the model parameters. For dense models, this fits. For MoE models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single GPU.
Recommended fix: Disable optimizer loading during conversion or execute on CPU. - python export.py --load_optim False
How Denpex helps: Denpex matches MoE Activation Recomputation Export OOM 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.

Model#megatron#user-report

What this failure is

MoE Activation Recomputation Export OOM is a Model failure seen during ML training runs. The conversion script loads the optimizer states into VRAM alongside the model parameters. For dense models, this fits. For MoE models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single GPU. Common tags: Megatron, User Report.

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Why it happens (the mechanism)

Since the model trained successfully on the exact same hardware, the engineer assumes the export script contains a memory leak. In reality, training distributed the optimizer state across the cluster, while the export script attempts to load it onto a single device.

What you'll observe

Training completes successfully.
The checkpoint conversion/export script enters an infinite loop of OOM errors and retries.
VRAM usage spikes to 100% instantly during the merge operation.

Common symptoms and what they mean

Symptom	Why it happens
CUDA out of memory	The conversion script loads the optimizer states into VRAM alongside the model parameters. For dense models, this fits. For MoE models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single GPU.
Infinite CUDA OOM during HF export with Activation Recomputation enabled	The conversion script loads the optimizer states into VRAM alongside the model parameters. For dense models, this fits. For MoE models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single GPU.

Which systems are affected

Megatron
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.

✓Identify if the model is an MoE architecture.
✓Check if `recompute_granularity=full` was used during training.
✓Examine the flags passed to the checkpoint conversion utility.

Searchable error signature

search key

CUDA out of memory

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

The conversion script loads the optimizer states into VRAM alongside the model parameters. For dense models, this fits. For MoE models trained with activation recomputation, the sheer parameter volume makes simultaneous optimizer loading mathematically impossible on a single GPU.

The fix and how to prevent it

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References

Don't just read the fix, diagnose your run

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