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Do not change the batch size or buy a larger GPU first. Identify the exact failure stage, capture the workload and device state, then separate live tensor memory, allocator cache, and memory used by other CUDA components. That evidence tells you whether the fix is a leaking tensor, a fragmented allocation pattern, a workload setting, another process, or genuinely insufficient capacity.
Start with a precise failure record
A GPU out-of-memory message is an outcome, not a diagnosis. The same CUDA out of memory wording can result from a tensor that grows every step, a temporary workspace, a second process, allocator fragmentation, or a model that simply does not fit. Record facts from the failing run before changing its configuration.
Capture the run context
- Framework and exact version, including the PyTorch or TensorFlow build and CUDA runtime it uses.
- GPU model, total VRAM, and the device index selected by the job.
- The complete error text and stack trace, not just the last line.
- Where the failure occurs: process startup, data transfer, forward pass, backward pass, optimizer step, validation, or checkpoint save.
- Per-device batch size, input dimensions, sequence length, image resolution, number of workers, precision mode, optimizer, and enabled compiler, graph, or autotuning features.
- Whether the failure happens on every run, after a predictable number of steps, or only on particular batches.
- Device-wide usage immediately before and during the failure, plus the process list from the GPU monitoring tool.
Save one successful batch’s shape and the first failing batch’s shape. A sudden increase in sequence length or image dimensions often explains an apparently intermittent failure.
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| Failure point | What to investigate first |
|---|---|
| Startup or model load | Weights, optimizer-state initialization, framework workspaces, another process, and whether the model is being placed on the intended device. |
| Forward pass | Activation size, an unusually large input, temporary kernels, and features that retain intermediate tensors. |
| Backward pass | Saved activations, gradient tensors, sequence or image dimensions, and whether gradients from an earlier step remain attached to the graph. |
| Optimizer step | Optimizer state, gradient buffers, update-time workspaces, and the first step’s extra allocations. |
| Validation | Missing inference mode, retained outputs or losses, validation batches larger than training batches, and failure to release training-only state. |
| Checkpoint or save | Temporary copies, serialization buffers, and a checkpoint routine that briefly holds both the live model and a second representation. |
This staged approach follows the general troubleshooting principle used in NVIDIA’s NIM documentation, but NIM’s detailed examples concern inference-server startup. Do not apply its inference-specific settings to a training job without evidence.
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Read GPU memory correctly in PyTorch
PyTorch exposes several memory measurements that answer different questions. Looking only at nvidia-smi can make a healthy cache look like a leak; looking only at PyTorch can miss allocations made by CUDA libraries outside its allocator.
Compare allocated, reserved, and peak values
At a synchronization point around the suspected operation, record current and peak values:
import torch
torch.cuda.synchronize()
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())memory_allocated() is memory occupied by live tensors tracked by PyTorch. memory_reserved() is memory held by PyTorch’s caching allocator, including blocks that are currently unused but available for reuse. The peak forms show the highest observed value since the relevant peak counter was reset. Record these numbers at the same points in each run; a value captured after the failure may not describe the allocation that triggered it.
The caching allocator deliberately keeps blocks so later operations do not have to request them from the driver. Consequently, nvidia-smi can show substantial device usage even after tensors have been released. High reserved memory with comparatively stable allocated memory often indicates reusable cache, not live tensors consuming all of the VRAM.
Know what empty_cache() can and cannot do
torch.cuda.empty_cache() releases unused cached blocks so another GPU application can use them. It does not delete live tensors, shorten an autograd graph, or create additional capacity for those tensors. Calling it every iteration usually adds synchronization and allocation overhead while leaving the underlying live-memory problem untouched. Use it as a controlled diagnostic or when a completed workload must hand unused cache back to other processes, not as a substitute for finding retained objects.
Inspect allocation history when a snapshot is not enough
For a difficult case, collect PyTorch memory statistics and an allocator snapshot or allocation-history trace around a short reproducer. Look for blocks that grow after every iteration, a large peak during one operation, or many differently sized blocks that cannot be reused efficiently. Reset peak counters before a test so the next measurement represents that test rather than an earlier phase.
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. NCCL is a documented example. If the device monitor reports much more memory than PyTorch accounts for, investigate distributed-communication buffers, custom CUDA extensions, compiler workspaces, and other processes instead of assuming a PyTorch tensor leak.
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Run controlled workload tests before changing hardware
Change one variable at a time and keep a record of peak allocated, peak reserved, device-wide usage, step time, and training behavior. A test that merely avoids the crash but changes the optimization recipe is not an equivalent comparison.
- Reproduce with a fixed batch. Use the smallest script or data slice that still fails. Disable unrelated validation, checkpointing, compilation, graph capture, and autotuning temporarily so the peak has fewer possible causes.
- Reduce the per-device microbatch. Keep input shape and precision constant, then rerun. If the peak falls roughly with the number of examples, activations or per-example work are likely contributors.
- Reduce the input shape. Test a shorter maximum sequence, smaller image, or lower resolution. If only the largest examples fail, enforce a bounded shape, bucket similar lengths, or handle outliers separately.
- Test gradient accumulation deliberately. Several microbatches can produce a larger effective batch, but batch-dependent layers, optimizer update frequency, learning-rate schedules, gradient clipping, and convergence can change. Verify the resulting training recipe rather than claiming it is identical to one large batch.
- Restore features one by one. Re-enable validation, checkpointing, compilation, graph capture, autotuning, and distributed communication separately. The first feature that recreates the peak identifies a useful branch for investigation.
| Change | Likely memory effect | Behavioral trade-off |
|---|---|---|
| Lower per-device microbatch | Usually lowers activation and temporary-tensor peaks. | More optimizer steps or accumulation logic; throughput can change. |
| Shorter or bounded inputs | Reduces work that scales with sequence length, resolution, or other dimensions. | May discard, truncate, or rebucket data and affect model quality. |
| Gradient accumulation | Can preserve a larger effective batch without holding it all at once. | Update timing and batch-dependent layers are not automatically equivalent. |
| Disable a memory-heavy feature | Can remove a separate workspace, graph pool, or retained buffer. | May reduce throughput or remove a compiler/distributed optimization. |
Evaluate mixed precision with measurements
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.” That is a capacity opportunity, not a guaranteed multiplier. The saving depends on which parameters, gradients, activations, optimizer states, and workspaces remain in higher precision and on how the framework implements automatic mixed precision.
A safe AMP test
- Record a stable FP32 baseline: loss behavior, validation output, peak memory, and step time.
- Enable the framework’s supported automatic mixed-precision mode without changing batch size.
- Confirm from logs or profiling that relevant operations actually use the intended lower-precision formats.
- Compare peak allocated and device-wide memory after several representative steps, including the largest normal input.
- Check for overflow, underflow, NaNs, loss-scaling events, and unacceptable changes in validation quality.
- Only after the run is stable, test a larger microbatch and measure again.
Do not promise that AMP halves total training memory or fixes a leak. Optimizer state, full-precision master weights, activations that remain in FP32, kernel workspaces, and external allocations can dominate the result.
Investigate changing shapes and memory-affecting features
Variable-length inputs and allocator churn
PyTorch’s tuning guidance warns that variable-length inputs can produce allocator fragmentation and unexpected out-of-memory errors. Repeatedly requesting different block sizes may leave enough total free memory in aggregate but not a suitably sized contiguous block for the next operation.
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- Run one forward and backward pass using the maximum input size expected in normal training.
- Allow the allocator to obtain the large reusable buffers needed for that peak.
- Clear gradients without retaining the graph.
- Continue ordinary training with bounded or bucketed shapes.
This is not a universal fix. It can waste memory when the maximum shape is rare, and it must be tested with the actual model, optimizer, and data pipeline.
Compiler, graph, and autotuning changes
CUDA graph capture can increase device-memory use because captured operations and their pools must remain available. If the failure started after enabling graphs, isolate graph capture and compare a short run with it disabled.
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Highly variable input sizes can also complicate convolution autotuning. A new algorithm may select a larger temporary workspace for one shape. Compare a fixed-shape run, inspect the peak around the convolution, and test the feature in isolation before concluding that the GPU has permanently become too small.
Use framework-specific evidence
TensorFlow
TensorFlow’s official profiler provides a GPU-memory view for examining allocations and peaks over a trace. Use it to identify the operation and time window associated with the spike, then correlate that trace with batch shape, validation, and optimizer events. The available guidance supports the profiler and multi-GPU considerations; it does not establish a complete TensorFlow-specific OOM procedure, so avoid transplanting PyTorch allocator settings into TensorFlow.
Distributed training and NCCL
In multi-GPU jobs, account for communication buffers and process placement as well as model tensors. NCCL allocations may sit outside PyTorch’s allocator accounting. Check each rank, confirm that every process is bound to the intended device, and compare per-device usage rather than assuming rank zero explains the failure.
Decide whether you need more capacity
Choose a larger GPU or multiple GPUs only after measurements show that the intended model and training recipe do not fit after removing unintended users, retained tensors, external allocations, and avoidable peaks. Before purchasing or provisioning, compare:
- Usable VRAM, not just the advertised total.
- Parameter, gradient, optimizer-state, activation, and workspace requirements for the actual precision and batch.
- Interconnect bandwidth, host RAM, PCIe topology, and power or cooling limits.
- Framework support, distributed-launch requirements, and checkpoint compatibility.
- Expected throughput, communication overhead, and total infrastructure cost.
| Option | When it fits | Costs and risks to check |
|---|---|---|
| Configuration change | Measurements show a preventable peak, fragmentation pattern, or unused cache. | May reduce throughput, alter effective batch behavior, or require code changes. |
| Higher-VRAM single GPU | The workload is otherwise single-device and needs more contiguous capacity. | Hardware cost, system compatibility, power, availability, and a new device’s software support. |
| Multiple GPUs | The model or target throughput justifies distributed execution and each device can hold its assigned share. | Gradient communication, host-thread overhead, interconnect requirements, launch complexity, and less-than-linear speedup. |
| Hosted or shared accelerator | Local hardware is the constraint and usage is intermittent or scalable. | Data-transfer time, recurring cost, quota limits, and environment reproducibility. |
TensorFlow’s multi-GPU guidance describes extra gradient communication and host-thread overhead, so adding devices does not guarantee linear speedup. A distributed configuration can also fail if each rank still needs a copy of a model or optimizer state that exceeds the per-GPU budget.
Interpret NVIDIA’s capacity examples correctly
NVIDIA’s NIM troubleshooting documentation gives arithmetic examples for inference serving, not training. An 8-billion-parameter model at BF16 is estimated at 8 billion × 2 bytes, or 16 GB of weights on one GPU. The page illustrates that estimate fitting on a 24 GB RTX 4090 with room for inference KV cache and overhead. It also gives a 70-billion-parameter BF16 example spread across four GPUs: 70 billion × 2 bytes ÷ 4 equals 35 GB of estimated weights per GPU.
Those figures exclude the training states and activation peaks that make training memory a different calculation. They do not establish that a 24 GB card can train the 8-billion-parameter model, nor that four GPUs will train the 70-billion-parameter model efficiently. Use them only to explain weight-memory arithmetic and preserve their inference-serving context.
A repeatable incident checklist
- Save the full error, stack trace, framework version, GPU model, and total VRAM.
- Mark the exact lifecycle stage and failing batch or shape.
- Capture the process list and device-wide usage around the event.
- For PyTorch, log allocated, peak allocated, reserved, and peak reserved memory at matched checkpoints.
- Compare PyTorch totals with device-wide usage to expose NCCL, direct CUDA, third-party, or other-process allocations.
- Reproduce with one controlled change: microbatch, input shape, precision, or a feature toggle.
- Measure AMP rather than assuming a fixed saving, and validate numerical behavior.
- Test bounded shapes or the documented preallocation technique when variable lengths correlate with the failure.
- Re-enable compiler, graph, autotuning, validation, checkpointing, and distributed features one at a time.
- Escalate to higher-VRAM or multi-GPU infrastructure only when the measured, cleaned-up workload still exceeds usable capacity.
The useful outcome is not merely a run that survives. It is a run whose memory peak, training behavior, and infrastructure requirements are understood well enough that the next model or data change can be evaluated before it fails.
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