Is this a hardware or a software issue?

Optimizer 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 optimizer 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 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 #Numerical Underflow. 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 Numerical Underflow is upgraded. Pin compatible versions and rerun the prevention smoke test on every dependency change.

Is there a fingerprint I can monitor?

Yes. Gradient norm remains small and stable, but loss suddenly turns NaN. 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.

AdamW Epsilon Underflow in FP16

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 default epsilon value for adam/adamw is 1e-8. in float16, the smallest representable positive normal number is ~5.96e-8. thus, 1e-8 underflows to 0.0. during the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing nans.. 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 default epsilon value for adam/adamw is 1e-8. in float16, the smallest representable positive normal number is ~5.96e-8. thus, 1e-8 underflows to 0.0. during the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing nans. 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 default epsilon value for Adam/AdamW is 1e-8. In float16, the smallest representable positive normal number is ~5.96e-8. Thus, 1e-8 underflows to 0.0. During the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing NaNs.

Quick answer

The default epsilon value for Adam/AdamW is 1e-8.

Root cause: The default epsilon value for Adam/AdamW is 1e-8. In float16, the smallest representable positive normal number is ~5.96e-8.
Recommended fix: Increase AdamW epsilon to an FP16-safe value optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, eps=1e-5) A larger epsilon like 1e-5 or 1e-4 is representable in float16, preventing division by zero during the parameter update calculation.
How Denpex helps: Denpex matches AdamW Epsilon Underflow in FP16 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.

Optimizer#Numerical Underflow

What this failure is

AdamW Epsilon Underflow in FP16 is a Optimizer failure seen during ML training runs. The default epsilon value for Adam/AdamW is 1e-8. In float16, the smallest representable positive normal number is ~5.96e-8. Thus, 1e-8 underflows to 0.0. During the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing NaNs. Common tags: Numerical Underflow.

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Why it happens (the mechanism)

Engineers usually suspect exploding gradients and try to apply gradient clipping, which completely fails to fix an underflow-induced division by zero.

What you'll observe

Loss becomes NaN without gradient explosion
Optimizer state contains NaNs

Common symptoms and what they mean

Symptom	Why it happens
Gradient norm remains small and stable, but loss suddenly turns NaN.	The default epsilon value for Adam/AdamW is 1e-8. In float16, the smallest representable positive normal number is ~5.96e-8. Thus, 1e-8 underflows to 0.0. During the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing NaNs.
Model crashes during the optimizer step rather than the forward/backward pass.	The default epsilon value for Adam/AdamW is 1e-8. In float16, the smallest representable positive normal number is ~5.96e-8. Thus, 1e-8 underflows to 0.0. During the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing NaNs.

Which systems are affected

PyTorch
Hugging Face Transformers

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.

✓Disable AMP to see if the problem disappears.
✓Inspect optimizer parameters (`optimizer.param_groups[0]['eps']`).

The fix and the prevention pattern

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Diagnose this failure in VS Code

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Root cause

The default epsilon value for Adam/AdamW is 1e-8. In float16, the smallest representable positive normal number is ~5.96e-8. Thus, 1e-8 underflows to 0.0. During the optimizer update, this leads to a division by zero if the gradient variance estimate is also close to zero, producing NaNs.

The fix and how to prevent it

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References

↗Hugging Face AdamW FP16 Issue

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