torch.OutOfMemoryError: CUDA out of memory. Tried to allocate

GPU ran out of memory during training. This entry explains how to confirm the cause, apply the fix, and separate it from adjacent pytorch-fsdp-ddp failures.

Quick answer

torch.OutOfMemoryError: CUDA out of memory. Tried to allocate means GPU ran out of memory during training. Preserve the first preceding error, then run the targeted control below.

Symptom: torch.OutOfMemoryError: CUDA out of memory. Tried to allocate
Root cause: GPU ran out of memory during training. The decisive evidence is the first log line that precedes "torch.OutOfMemoryError: CUDA out of memory.
Recommended fix: PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
How Denpex helps: Denpex investigates torch.OutOfMemoryError: CUDA out of memory. Tried to allocate using the evidence you provide or your connected workload collects. Earlier rank, host or application evidence is needed to distinguish an initiating failure from a downstream report.

Distributed Training#pytorch-fsdp-ddp#pytorch#cuda#out#memory#tried

What this failure is

The literal signature is "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate". It is a distributed training failure associated with PyTorch Distributed, FSDP, DDP, and TorchElastic. The line identifies the failing operation or subsystem, while the surrounding evidence decides whether it is the initiating fault or a downstream symptom.

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Why it happens (the mechanism)

GPU ran out of memory during training. The failure becomes visible at this call site because the operation first requires the missing resource, valid state, healthy peer, or correct result. Earlier log lines and a known-good control carry more causal value than the final wrapper exception.

What you'll observe

The workload stops or loses forward progress after emitting "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate".
A retry on the same configuration reproduces the failure because the causal state has not changed.
The outer framework exception can hide the rank, node, allocation, or dependency that failed first.
Increasing timeouts or reducing workload size can suppress the symptom without correcting the cause.

Common symptoms and what they mean

Symptom	Why it happens
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate	GPU ran out of memory during training.
The same operation fails at a consistent stage of PyTorch Distributed, FSDP, DDP, and TorchElastic.	The decisive evidence is the first log line that precedes "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" and differs from a healthy run.
The first related warning appears before the final exception and names the causal subsystem.	A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.
A known-good control changes one variable and either reproduces or clears the failure.	GPU ran out of memory during training.

Which systems are affected

PyTorch Distributed, FSDP, DDP, and TorchElastic
production-shaped multi-accelerator workloads
containerized and bare-metal deployments of the same stack

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.

✓Find the first occurrence of "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" and preserve at least 100 lines before it.
✓Identify which rank, node, device, or process emitted the first related warning.
✓Compare the failing rank, node, input, or configuration with one known-good control.
✓Repeat the same input after the targeted change and require the signature to disappear.
✓Resume from latest checkpoint only after the control passes.

Root cause

GPU ran out of memory during training.
The decisive evidence is the first log line that precedes "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" and differs from a healthy run.
A nearby failure remains a competing hypothesis until a control separates configuration, capacity, transport, and hardware causes.

The fix and how to prevent it

Searchable error signature

search key

torch.OutOfMemoryError: 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

The root cause is on this page and stays free. A free account adds the exact remediation steps, saved history, and the fix on every entry in the encyclopedia.

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Why the recommended fix works

Reduce batch size, enable gradient checkpointing, or use PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True This changes the condition in the causal diagnosis instead of hiding the outer exception. The repeated control proves ownership before recovery from latest checkpoint.

Code examples

snippet

# Preserve evidence before restarting
rg -n -i 'error|exception|timeout|failed' <log-file>
nvidia-smi
python -m torch.utils.collect_env

# Find the exact signature in the complete log
rg -n -F -- "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" <log-file>

Adapt the snippet to your framework. The same pattern holds for PyTorch Lightning, Hugging Face Trainer, DeepSpeed, Megatron-LM, and vLLM training wrappers. Where the wrapper exposes a config flag (for examplelr_scheduler_type in Trainer), prefer the flag over the imperative API to keep the schedule declarative and reproducible.

Best practices by model family

Model / Stack	Recommendation	Notes
First response	Preserve the first failure	Keep the context before "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" so aggregation does not erase causality.
Confirmation	Change one variable	Use a known-good node, rank, input, or configuration as the control.
Recovery	Resume from latest checkpoint	Resume only after the literal signature no longer appears in the same control.

With the fix vs without the fix

Dimension	With the fix	Without the fix
Evidence	First preceding error and one controlled comparison	Only the final aggregated exception
Fix	Reduce batch size, enable gradient checkpointing, or use PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True	Retrying the unchanged workload
Exit criterion	"torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" is absent in the repeated control	The job happened to run once

Diagnostic note

“Treat "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" as a search key and an investigation checkpoint, not as proof of every cause associated with the phrase. The high-value evidence is what changed immediately before it and whether the failure follows the workload, node, or configuration.”

Visual fingerprint

Decision path for torch.OutOfMemoryError: CUDA out of memory. Tried to allocate

literal error captured
        |
        v
find first preceding failure
        |
        v
run one known-good control
        |
        +-- follows workload --> inspect input or configuration
        +-- follows node ------> inspect hardware or platform
        +-- disappears --------> validate the targeted fix

The control separates workload, configuration, and node ownership before recovery from latest checkpoint.

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

Related failures to investigate next

Frequently asked questions

Questions engineers and on-call staff commonly ask about this failure.

What does "torch.OutOfMemoryError: CUDA out of memory. Tried to allocate" mean?

GPU ran out of memory during training.

Is this line always the root cause?

No. It can be the direct failure or the point where an earlier failure becomes visible. The first preceding error and a controlled comparison decide which.

What should I collect before restarting?

Collect complete log context, the emitting rank or node, component versions, resolved configuration, and the diagnostic output shown above.

What is the fastest confirmation?

Compare the failing rank, node, input, or configuration with one known-good control.

How do I prevent it from recurring?

Apply the smallest causal change and repeat the same workload.

References

Don't just read the fix, diagnose your run

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