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

Is there a fingerprint I can monitor?

Yes. The model loads fine. The forward pass runs fine. The backward pass (`loss.backward()`) completes. The process crashes immediately when `optimizer.step()` is called. 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.

Optimizer State Memory Explosion

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 optimizers like adam and adamw maintain moment states (running averages of gradients and squared gradients) for every parameter. for a model with n parameters, the optimizer state requires 2 * n memory (often in fp32). the initialization of these state tensors happens lazily on the first `optimizer.step()`, suddenly doubling or tripling the memory footprint of the model weights.. 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 optimizers like adam and adamw maintain moment states (running averages of gradients and squared gradients) for every parameter. for a model with n parameters, the optimizer state requires 2 * n memory (often in fp32). the initialization of these state tensors happens lazily on the first `optimizer.step()`, suddenly doubling or tripling the memory footprint of the model weights. 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.

Optimizers like Adam and AdamW maintain moment states (running averages of gradients and squared gradients) for every parameter. For a model with N parameters, the optimizer state requires 2 * N memory (often in FP32). The initialization of these state tensors happens lazily on the first `optimizer.step()`, suddenly doubling or tripling the memory footprint of the model weights.

Quick answer

Optimizers like Adam and AdamW maintain moment states (running averages of gradients and squared gradients) for every parameter.

Symptom: RuntimeError: CUDA out of memory. (Occurs on the line `optimizer.step()`)
Root cause: Optimizers like Adam and AdamW maintain moment states (running averages of gradients and squared gradients) for every parameter. For a model with N parameters, the optimizer state requires 2 * N memory (often in FP32). The initialization of these state tensors happens lazily on the first `optimizer.
Recommended fix: Use an optimizer with a smaller memory footprint, such as 8-bit Adam. import bitsandbytes as bnb optimizer = bnb.optim.AdamW8bit(model.parameters(), lr=1e-3) Quantizes the optimizer state to 8-bit, reducing the optimizer memory footprint by ~75%.
How Denpex helps: Denpex matches Optimizer State Memory Explosion 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#Optimizer State

What this failure is

Optimizer State Memory Explosion is a Memory failure seen during ML training runs. Optimizers like Adam and AdamW maintain moment states (running averages of gradients and squared gradients) for every parameter. For a model with N parameters, the optimizer state requires 2 * N memory (often in FP32). The initialization of these state tensors happens lazily on the first `optimizer.step()`, suddenly doubling or tripling the memory footprint of the model weights. Common tags: Optimizer State.

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Why it happens (the mechanism)

Users calculate memory based on model size and activations. They are surprised when memory spikes at the very end of the training step, unassociated with the batch size.

What you'll observe

RuntimeError: CUDA out of memory. (Occurs on the line `optimizer.step()`)
OOM exactly after the first backward pass is completed.

Common symptoms and what they mean

Symptom	Why it happens
The model loads fine. The forward pass runs fine. The backward pass (`loss.backward()`) completes. The process crashes immediately when `optimizer.step()` is called.	Optimizers like Adam and AdamW maintain moment states (running averages of gradients and squared gradients) for every parameter. For a model with N parameters, the optimizer state requires 2 * N memory (often in FP32). The initialization of these state tensors happens lazily on the first `optimizer.step()`, suddenly doubling or tripling the memory footprint of the model weights.

Which systems are affected

PyTorch
Adam
DeepSpeed

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.

✓Verify the exact line of the OOM crash is `optimizer.step()`.
✓Calculate total parameter count and multiply by 8 bytes (for Adam's two FP32 states) to see if it exceeds available VRAM.

Searchable error signature

search key

RuntimeError: CUDA out of memory. (Occurs on the line `optimizer.step()`)

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

Optimizers like Adam and AdamW maintain moment states (running averages of gradients and squared gradients) for every parameter. For a model with N parameters, the optimizer state requires 2 * N memory (often in FP32). The initialization of these state tensors happens lazily on the first `optimizer.step()`, suddenly doubling or tripling the memory footprint of the model weights.

The fix and how to prevent it

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References

↗DeepSpeed Issue #112 - Optimizer OOM

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Related Memory errors

Activation Memory Spike

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Memory Summary Tool Usage

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PyTorch Caching Allocator Debug

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CPU RAM Exhaustion During Training

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