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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchA GPU out-of-memory (OOM) error is a symptom, not a diagnosis. Before reducing the batch size or purchasing hardware, identify the exact training stage that failed, record the workload and GPU state, and reconcile framework-level memory with total device usage. A forward-pass OOM caused by activation size needs a different fix from an optimizer-step OOM, allocator fragmentation, or another process consuming VRAM.
The procedure below prioritizes measurements, then changes one variable at a time. PyTorch receives the most detailed allocator guidance; TensorFlow coverage is limited to its memory profiler, and NVIDIA NIM examples are explicitly for inference startup rather than training.
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1. Pin down exactly when and why the job failed
Save the complete error text and the run configuration before changing anything. “CUDA out of memory” alone does not identify the allocation that exceeded capacity. The failure stage narrows the search substantially.
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- Startup or model loading: weights, libraries, graph capture, or another process may consume most of the device before the first batch.
- Forward pass: activations, attention workspaces, convolution workspaces, or an unexpectedly large input are common suspects.
- Backward pass: saved activations and gradient buffers raise the peak beyond the forward pass.
- Optimizer step: optimizer state or temporary update buffers can create a new peak after gradients exist.
- Validation: an evaluation path may use different sequence lengths, retain tensors, or accidentally run with gradients enabled.
- Checkpointing: serialization, parameter copies, or an asynchronous save can briefly increase memory.
Also write down the framework and version, GPU model and total VRAM, per-device batch size, input dimensions or sequence length, precision mode, optimizer, number of workers, and whether the failure is repeatable or occurs only on particular batches. Capture device-wide usage immediately before and during the failing step, and check whether another process owns the GPU. These observations establish what happened; they do not, by themselves, prove the cause.
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2. Reconcile PyTorch’s memory numbers with what the GPU reports
PyTorch exposes several different quantities. Confusing them is one of the fastest ways to apply the wrong remedy.
Allocated versus reserved memory
torch.cuda.memory_allocated() reports memory currently occupied by live PyTorch tensors. Its peak counterpart records the highest such value since the relevant reset. torch.cuda.memory_reserved() reports memory held by PyTorch’s caching allocator, including blocks that are currently unused but retained for reuse. Peak reserved memory records the largest allocator reservation.
Because the allocator keeps reusable blocks, nvidia-smi can show substantial usage after tensors have been released. That display is not proof that all shown memory is live tensor data. Compare current and peak allocated with current and peak reserved at the point of failure.
import torch
print('allocated:', torch.cuda.memory_allocated())
print('max allocated:', torch.cuda.max_memory_allocated())
print('reserved:', torch.cuda.memory_reserved())
print('max reserved:', torch.cuda.max_memory_reserved())
print(torch.cuda.memory_summary())Reset peak counters immediately before a controlled trial if you need a clean comparison:
torch.cuda.reset_peak_memory_stats()
# run one representative training step
print(torch.cuda.max_memory_allocated())
print(torch.cuda.max_memory_reserved())What empty_cache() can and cannot do
torch.cuda.empty_cache() releases unused cached blocks so other GPU applications can use them. It does not delete live tensors, reduce the memory required by the current model, or make more memory available to those tensors. Use it as an experiment when another process needs reclaimed cache, not as a cure for a genuine peak-capacity problem.
Inspect allocation history and outside allocations
For a difficult failure, collect PyTorch memory statistics, an allocator snapshot, or allocation-history data around the failing step. Look for tensors whose lifetimes are longer than intended, a steadily rising allocated baseline, and large temporary blocks that are not reused.
Then compare PyTorch’s totals with device-wide usage. PyTorch’s memory profiler does not see every direct CUDA allocation or every allocation made by a third-party library. The PyTorch memory guide identifies NCCL as a documented example. If total usage is much higher than PyTorch’s allocated plus reserved figures, investigate distributed communication buffers, custom CUDA extensions, data-loader components, and other processes rather than repeatedly shrinking the model batch.
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When measurements show that live tensors reach the device limit, change the smallest workload variable that addresses the failed stage. Rerun a representative batch after each change and record peak allocated, peak reserved, step time, and output behavior.
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Per-device microbatch
Lowering the per-device microbatch usually reduces activation and gradient peaks. It can also reduce throughput, alter batch-dependent layers such as batch normalization, and change optimization behavior. Keep a record of the effective batch and learning-rate schedule so a memory fix does not silently become a different experiment.
Input dimensions and sequence length
Reduce image resolution, crop size, token count, or maximum sequence length only when that change is acceptable for the task. Attention and other operations can grow sharply with sequence length, so a small shape increase may create a disproportionate peak. If only occasional batches fail, log their exact shapes; a single unusually long or large example may be the trigger.
Gradient accumulation
Gradient accumulation can preserve a larger effective batch while using a smaller microbatch on each device. It does not make every training recipe equivalent: batch-dependent layers see the smaller microbatch, optimizer updates occur less often, loss scaling and learning-rate schedules may need adjustment, and accumulation itself can affect step time. Validate convergence rather than assuming that an accumulated run matches the original.
| Change | Likely memory effect | Trade-offs to measure |
|---|---|---|
| Lower per-device microbatch | Usually lowers activation and gradient peaks | Throughput, batch-dependent layers, effective-batch settings |
| Shorter or smaller inputs | Lowers shape-dependent activations and workspaces | Model quality, coverage of long examples, task accuracy |
| Gradient accumulation | Allows a smaller microbatch for a similar effective batch | Update frequency, convergence, scheduler and normalization behavior |
| Disable a memory-heavy feature temporarily | Removes that feature’s buffers or captured state | Performance, compiler behavior, graph compatibility |
4. Measure mixed precision instead of assuming a fixed saving
NVIDIA’s Train With Mixed Precision documentation states: “Mixed precision models use less memory than FP32, so it is possible to increase the batch size when running with AMP.” The practical implication is conditional, not a promise of a particular percentage or a doubled batch.
Memory savings depend on which tensors use reduced precision, how the framework keeps master weights, optimizer state, gradients, and normalization operations, and which workspaces a kernel selects. Enable the framework’s supported automatic mixed-precision mode, then compare peak memory on the same representative steps. Confirm that the intended operations actually run in reduced precision, and monitor loss scaling, NaNs or infinities, validation metrics, and checkpoint compatibility.
Do not use AMP to mask a leak. If allocated memory rises every step, or if an external library consumes the missing capacity, precision changes may only postpone failure. Treat the measured peak and numerical behavior as the acceptance criteria.
5. Investigate changing shapes and allocator fragmentation
Variable-length inputs
PyTorch’s tuning guidance warns that variable-length inputs can cause allocator churn and unexpected OOMs. Repeatedly requesting differently sized blocks can leave free memory split into pieces even when the total free amount appears sufficient for the next request.
The guide describes a workload-specific preallocation technique: run a forward and backward pass using a maximum-size input so reusable buffers are allocated, clear gradients, and then continue ordinary training. This is not a universal fix. Test it with your actual maximum shape, verify that the preallocation itself fits, and compare reserved-memory behavior with and without it.
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CUDA graphs, compilation, and autotuning
CUDA graphs can increase device-memory use. Highly variable input sizes can also complicate convolution autotuning, which may select and retain different workspaces. If OOM began after enabling graph capture, a compiler, autotuning, or another performance feature, isolate that feature in a controlled run. A run that succeeds with the feature disabled identifies a configuration interaction; it does not automatically mean the GPU is undersized.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.6. Check framework boundaries and distributed components
PyTorch
Use allocator statistics and snapshots to separate live tensors, cached reserve, and unexplained device usage. In distributed training, account for communication buffers and process placement. Confirm that each process is assigned the intended device and that stale worker processes have exited.
TensorFlow
The available TensorFlow guidance supports using its GPU memory profiler to observe allocation behavior, but it does not establish a complete, universal TensorFlow training-OOM procedure. Use the profiler with the exact TensorFlow version and distribution strategy in your job, and record the failing operation, peak usage, and other processes. Do not transfer PyTorch allocator names or remedies directly to TensorFlow.
NVIDIA NIM examples
NVIDIA’s NIM troubleshooting material starts by locating the startup stage that failed. Its detailed examples concern NIM and vLLM inference startup, not model training. The staged-diagnosis principle is useful for training, but inference-specific settings and capacity conclusions should not be applied to a training job without separate evidence.
7. Decide whether more hardware is justified
Consider a higher-VRAM GPU only after measurements show that the model, optimizer, activations, and required features still exceed usable capacity after reasonable configuration changes. Compare the capacity you can actually use, supported precision, interconnect, host power and cooling, software compatibility, and total cost—not just the number printed on a product page.
Multiple GPUs can distribute model or data memory, but they add gradient communication and host-thread work. TensorFlow’s official multi-GPU guidance describes these overheads, and scaling is not guaranteed to be linear. Check the interconnect, PCIe topology, communication library, CPU capacity, and data pipeline before treating another GPU as a drop-in fix.
| Option | Capacity question | Performance and engineering considerations |
|---|---|---|
| Configuration change | Can the measured peak fit after reducing the offending allocation? | Usually lowest cost; may change throughput, convergence, or model behavior |
| Higher-VRAM single GPU | Does one device provide enough headroom for the full training state? | Avoids cross-device communication; requires compatible system, power, cooling, and budget |
| Multiple GPUs | Can the framework and strategy partition memory effectively? | Communication, host utilization, interconnect, placement, and software complexity increase |
Keep inference arithmetic in its proper context
NVIDIA’s NIM documentation gives a worked inference-serving illustration: 8 billion parameters multiplied by 2 bytes per BF16 parameter is an estimated 16 GB of weights on one GPU. The page says that estimate fits on a 24 GB RTX 4090 example with room for inference KV cache and overhead. That is not a training-memory estimate and does not establish that this GPU is suitable for a particular training workload.
The same document illustrates a 70-billion-parameter BF16 model spread across four GPUs: 70 billion multiplied by 2 bytes and divided by four gives an estimated 35 GB of weights per GPU. This is also an inference-serving arithmetic example. Training adds gradients, optimizer state, activations, workspaces, and framework overhead, so these figures must not be used as a training sizing rule.
8. A repeatable OOM runbook
- Copy the complete error and mark the failing lifecycle stage.
- Record framework and version, GPU model and VRAM, batch and input shapes, precision, optimizer, and distributed configuration.
- Check device-wide processes and capture usage near the failure.
- For PyTorch, record allocated, peak allocated, reserved, peak reserved, and a memory summary around a representative step.
- Compare framework accounting with total device usage to identify external allocations such as NCCL or another process.
- Reproduce with one controlled change: microbatch, input shape, precision, or a newly enabled feature.
- Measure peak memory, step time, and numerical or validation behavior after each change.
- Investigate variable-shape fragmentation and test preallocation only if the workload matches that technique.
- Escalate to a higher-VRAM device or multi-GPU design only when the measured workload still cannot fit and the surrounding system can support it.
Common wrong turns
- Reading
nvidia-smias live tensor memory: PyTorch’s reserved cache and other libraries can account for part of the display. - Calling
empty_cache()and expecting a larger model to fit: it releases unused cached blocks, not live tensors. - Assuming AMP always halves memory: savings vary by model, framework, optimizer, and operation.
- Changing several variables at once: you lose the evidence needed to identify the cause and may alter training behavior unnoticed.
- Applying inference sizing examples to training: serving estimates for weights and KV cache omit training gradients, optimizer state, and activations.
- Adding GPUs without checking communication and host overhead: capacity may improve while throughput or reliability worsens.
Make the next change evidence-based
The useful question is not simply “How much VRAM does this GPU have?” It is “Which allocation exceeded usable VRAM, at what stage, and why?” A stage-specific record, separate accounting for live tensors and allocator reserve, and a controlled one-change-at-a-time experiment will usually identify whether the remedy is a workload adjustment, allocator investigation, feature isolation, cleanup of external users, or additional capacity.
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