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

Is there a fingerprint I can monitor?

Yes. CUDA OOM exception thrown even when nvidia-smi shows plenty of total free GPU 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.

PyTorch CUDA Memory Fragmentation

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 pytorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient.. 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 pytorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient. 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 PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. Over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient.

Quick answer

The PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations.

Symptom: RuntimeError: CUDA out of memory. Tried to allocate .* MiB .* .* GiB reserved in total by PyTorch
Root cause: The PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. Over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient.
Recommended fix: PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128
How Denpex helps: Denpex matches PyTorch CUDA Memory Fragmentation 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#Fragmentation

What this failure is

PyTorch CUDA Memory Fragmentation is a Memory failure seen during ML training runs. The PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. Over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient. Common tags: Fragmentation.

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Why it happens (the mechanism)

Users see 'CUDA out of memory' and assume their model/batch size is too large for the GPU. However, the total capacity and free memory numbers in the error message actually show that the physical memory exists, but it is too fragmented to allocate a contiguous block.

What you'll observe

RuntimeError: CUDA out of memory. Tried to allocate .* MiB .* .* GiB reserved in total by PyTorch
reserved memory is >> allocated memory

Common symptoms and what they mean

Symptom	Why it happens
CUDA OOM exception thrown even when nvidia-smi shows plenty of total free GPU memory.	The PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. Over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient.
The error message shows that 'reserved' memory is much larger than 'allocated' memory.	The PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. Over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient.

Which systems are affected

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 PyTorch OOM error message and compare 'allocated' vs 'reserved' memory.
✓Run torch.cuda.memory_summary() to inspect the allocator state and see the distribution of block sizes.

Searchable error signature

search key

RuntimeError: CUDA out of memory. Tried to allocate .* MiB .* .* GiB reserved in total by PyTorch
CUDA OOM exception thrown even when nvidia-smi shows plenty of total free GPU 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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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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CUDA errors in context

CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.

Compare every cuda error side by side

Root cause

The PyTorch caching allocator reserves large blocks of memory and splits them for individual tensor allocations. Over time, memory becomes fragmented into many small blocks, preventing the allocation of a new contiguous large block, even if the total free memory across all small blocks is sufficient.

The fix and how to prevent it

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References

↗GitHub Issue #123548 - CUDA Out of Memory due to fragmentation

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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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