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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallA CUDA out-of-memory (OOM) message means one GPU allocation failed; it does not identify the cause. First record the complete error and determine whether the failure occurred while loading weights, allocating a cache, running an ordinary operation, compiling or warming up CUDA Graphs, or capturing a graph. Then compare live tensor memory, allocator-reserved memory, other processes, and the workload’s peak demand. Reduce the demand when live allocations exceed the laptop GPU’s capacity; investigate fragmentation or graph-specific pools only when the measurements support those explanations.
What a CUDA OOM actually tells you
CUDA reports an OOM when an allocation request cannot be satisfied by the device’s usable memory at that moment. The message alone cannot tell you whether the device is genuinely too small, another process is using memory, a framework cache is holding reusable blocks, allocations are fragmented, or a special execution mode has retained memory.
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The point in the workflow matters. A model can fail while loading weights, while creating a key-value or other runtime cache, during compilation, during CUDA Graph capture, or during warmup. NVIDIA’s guidance for NIM separates those stages because each has different controls and explanations: its troubleshooting page is specific to NIM deployments, not a universal laptop setting.
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For a PyTorch workload, keep two measurements separate:
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- Allocated memory is memory held by live tensors.
torch.cuda.memory_allocated()andtorch.cuda.max_memory_allocated()report current and peak tensor use. - Reserved memory is memory held by PyTorch’s caching allocator, including blocks that are currently unused but available for reuse.
torch.cuda.memory_reserved()andtorch.cuda.max_memory_reserved()report that pool.
nvidia-smi can show memory used by the process that includes unused PyTorch cache. Conversely, allocations made outside PyTorch may not appear in PyTorch’s allocator counters. The PyTorch CUDA semantics documentation and CUDA memory usage guide explain how to compare those views.
Start with a disciplined triage
1. Save the complete failure
Copy the entire exception, not just the words “CUDA out of memory.” Preserve the requested allocation size and any reported total, free, allocated, and reserved values. Also record:
- GPU model and installed driver
- Operating system
- Framework and framework version
- CUDA toolkit or runtime version
- Application and model
- Input shape, batch size, sequence length, image size, or other relevant dimensions
- The exact operation, iteration, or startup phase at which the error appears
These details make an error reproducible and distinguish a capacity problem from a version- or stage-specific problem. NVIDIA’s debugging checklist likewise asks for the CUDA and PyTorch versions, GPU model, and full error details: Debugging Strategies.
2. Identify the failing stage
Write down the last successful event and the first failing event. A failure before the first request is different from one that appears only after several batches.
| Observed point of failure | What to investigate first |
|---|---|
| Model or weight loading | Whether the model, precision, and device fit before any inference or training work begins; also check memory already occupied by other processes. |
| Cache allocation | Requested cache dimensions, sequence or input limits, and whether the cache is created for every concurrent request. |
| Ordinary eager execution | Peak tensor demand, batch or input dimensions, retained tensors, and competing processes. |
| CUDA Graph compilation, capture, or warmup | Static input lifetime, graph-private pools, deferred reuse, and whether eager execution succeeds with the same inputs. |
Do not apply a graph-specific workaround to a weight-loading failure, or a cache-size change to an allocator-fragmentation problem.
3. Check device-wide usage and competing processes
Inspect the GPU with the monitoring tools available on your operating system and compare the result with your framework counters. Look for desktop compositors, browsers, other Python processes, containers, notebooks, or a previous crashed job that still owns a context. A process can also consume device memory through CUDA libraries or extensions without that memory appearing in PyTorch’s tensor counters.
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Stop only processes you recognize and can safely terminate. If another application must remain, treat its usage as part of the laptop GPU’s current capacity; do not assume the amount shown as “free” in a framework report is available to every allocation.
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If the workload uses PyTorch, collect measurements immediately before and after the operation that fails. Reset the peak counters at a controlled point so the numbers describe the relevant section rather than the entire process lifetime.
import torch
# Place this immediately before the suspected operation.
torch.cuda.reset_peak_memory_stats()
print("allocated before:", torch.cuda.memory_allocated())
print("reserved before:", torch.cuda.memory_reserved())
# run one controlled operation here
print("allocated after:", torch.cuda.memory_allocated())
print("reserved after:", torch.cuda.memory_reserved())
print("peak allocated:", torch.cuda.max_memory_allocated())
print("peak reserved:", torch.cuda.max_memory_reserved())
print(torch.cuda.memory_summary())Run a representative, repeatable input rather than an arbitrary sample. A peak that grows on every iteration suggests retained tensors or an accumulating workload; a one-time spike points toward a particular operation, input shape, compilation step, or cache allocation.
For deeper diagnosis, PyTorch provides allocator statistics and memory snapshots that can be opened in its memory visualizer. The visualizer describes allocator-managed blocks and their history; raw CUDA allocation information can reveal memory obtained outside the PyTorch allocator. Treat these as complementary evidence, not interchangeable totals. Follow the current procedures in Understanding CUDA Memory Usage.
Decide which pattern the evidence supports
| Evidence pattern | Likely interpretation | Next test |
|---|---|---|
| Allocated memory rises close to the GPU’s usable capacity and reserved memory is similar | Live tensors, model state, activations, or caches need more memory than the device can provide. | Reduce one workload dimension—batch, input or sequence length, tensor size, or model size—and rerun. |
| Reserved memory is much larger than allocated memory, with inactive split blocks in allocator statistics | Unused blocks or fragmentation may prevent a large contiguous request from fitting. | Capture allocator statistics, then test a compatible allocator configuration in a controlled run. |
| Device-wide usage is high but PyTorch allocated memory is modest | Another process or a non-PyTorch CUDA allocation is consuming the remainder. | Inspect processes and compare framework and device-level reports. |
| The error occurs only during graph capture or warmup | CUDA Graph pools, static inputs, or capture lifetime rules are retaining memory. | Run the same workload eagerly and compare peaks before changing allocator settings. |
| The peak grows between otherwise identical iterations | Tensors, outputs, gradients, or references may be retained unintentionally. | Check what is appended to lists, logged, cached, or kept in Python scope between iterations. |
A gap between reserved and allocated memory is a clue, not proof of fragmentation. Confirm it with allocator statistics and inactive split-block information before changing configuration.
Reduce live memory demand when capacity is the problem
If the live peak is near the device limit, the reliable fix is to make the operation require less memory. Change one variable at a time so you know which constraint mattered.
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Lower batch or input dimensions
- Reduce training or inference batch size.
- Shorten sequence length or context limits for language workloads.
- Use smaller image, audio, or video dimensions.
- Reduce intermediate tensor dimensions where the application permits it.
Record the smallest setting that succeeds and the setting that first fails. This gives you a practical operating boundary for the particular model and laptop, rather than a generic VRAM claim.
Use a smaller model or a lower-memory representation
Test a smaller model, fewer layers, or a representation that the application explicitly supports. Precision changes can reduce tensor storage, but they may alter numerical behavior and are framework- and model-dependent; validate outputs rather than assuming a lower-precision mode is interchangeable.
Separate training and inference effects
Training normally needs memory for gradients, optimizer state, and saved activations in addition to model weights. Inference can still allocate large temporary tensors or caches. Measure the failing mode directly; do not infer training requirements from a successful inference run.
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Use empty_cache() for unused cache only
In PyTorch, torch.cuda.empty_cache() releases unused blocks held by the caching allocator so that other GPU applications can use them. It cannot free live tensors and does not increase the amount of memory PyTorch can use for those live tensors. Calling it repeatedly inside a hot loop can add synchronization and allocation overhead without solving a capacity problem.
import torch
# Use at a deliberate boundary after references to temporary tensors are gone.
del temporary_result
torch.cuda.empty_cache()The del statement matters only if no other Python reference keeps the tensor alive. If allocated memory remains high, the memory is still live; clearing the allocator cache cannot make that workload fit.
Investigate fragmentation only when measurements point there
Fragmentation occurs when free memory exists in blocks that cannot satisfy a particular large allocation. A large reserved-versus-allocated gap can accompany it, but the diagnosis should come from allocator statistics, including inactive split blocks and the allocation history, not from one number in nvidia-smi.
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PyTorch and NVIDIA document allocator controls such as expandable_segments and max_split_size_mb. These are conditional tuning options: they can change allocation behavior, but they do not reduce the memory required by live tensors. Configuration names, syntax, and support depend on the installed PyTorch and CUDA versions, so verify the current documentation for your exact environment before testing a setting. Make one change in an isolated run, capture the same memory statistics, and compare both success and performance.
If the same input fails with allocated memory already near capacity, allocator tuning is the wrong first remedy. Return to reducing the workload’s peak demand.
Handle CUDA Graphs as a separate case
CUDA Graph capture changes allocation lifetime and pool behavior. Static input tensors may need to remain alive for the graph’s lifetime, graph-private pools can retain blocks separately from ordinary allocations, and reuse can be deferred. During capture, cached memory cannot always be released in the usual way.
NVIDIA’s CUDA Graph memory guidance recommends freeing unneeded allocations before capture and examining graph-specific pools and fragmentation. Use this sequence:
- Run the workload eagerly with the same model and input dimensions, and record the allocated and reserved peaks.
- Remove temporary tensors and other allocations that are not required by the graph before capture begins.
- Ensure static input and output objects have the intended lifetime; do not recreate or discard them unpredictably during capture.
- Capture one controlled iteration and compare its peak and reserved memory with eager execution.
- If only capture or warmup fails, investigate graph pools and capture lifetime rather than repeatedly calling
empty_cache().
For an application that offers an eager-mode switch, disabling graphs can be a useful diagnostic comparison, but it may reduce throughput. A graph failure is not evidence that ordinary eager execution is inherently out of memory.
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NVIDIA NIM documentation distinguishes OOM during weight loading, cache allocation, and CUDA Graph compilation or warmup. It documents product-level options, including NIM_DISABLE_CUDA_GRAPH and an --enforce-eager mode, for applicable NIM deployments. Those names are not universal PyTorch commands or CUDA environment variables. Use them only when the NIM version and deployment instructions explicitly support them, and account for the possible throughput trade-off of eager execution.
If your application is not NIM, translate the diagnostic principle rather than copying the flags: identify the failing stage, compare eager and graph execution when relevant, and change the setting provided by your own application or framework.
Do not treat laptop system RAM as spare VRAM
A laptop’s system memory is not automatically interchangeable with dedicated GPU VRAM. CUDA unified-memory behavior depends on the operating system, kernel, hardware, driver, and interconnect. The CUDA Programming Guide’s unified and system memory section does not support a blanket promise that moving data to system RAM transparently expands usable GPU memory for every laptop workload.
Consequently, adding RAM, changing a cooling pad, or attaching a physical accessory is not a general remedy for a CUDA allocation that exceeds the GPU’s usable memory. First establish whether the failure is live demand, another process, allocator behavior, or graph retention. Hardware changes can affect a particular system, but the measurements must show that they address the diagnosed constraint.
Compare remedies by evidence, compatibility, and cost
| Action | Use it when | What it changes | Main trade-off |
|---|---|---|---|
| Reduce batch, input, tensor, cache, or model size | Live allocated memory approaches device capacity. | Actual workload demand. | Lower throughput, shorter context, smaller inputs, or reduced model capability. |
torch.cuda.empty_cache() |
Unused PyTorch cache is blocking another application or a deliberate phase boundary needs to return it. | Releases unused cached blocks; live tensors remain. | It may add allocation overhead and cannot solve live-demand OOM. |
| Allocator configuration | Statistics show fragmentation or unsuitable block splitting. | How the allocator obtains and splits blocks. | Version compatibility, altered allocation performance, and no reduction in live tensor size. |
| Graph-specific cleanup or eager comparison | Failure occurs during capture, compilation, or warmup. | Graph pool lifetime and execution mode. | Eager execution can reduce throughput; graph pools have their own retention rules. |
| Terminate a competing process | Device-wide usage shows another application owns the required memory. | Available device capacity. | That process loses its GPU context or work. |
Build a minimal reproducer before escalating
If the error persists after stage isolation and measurement, reduce the program to the smallest example that still fails:
- Use one GPU and one fixed input shape.
- Remove unrelated logging, data loading, augmentation, and services.
- Keep the exact model, precision, batch or input dimensions, and execution mode that trigger the error.
- Print allocated, reserved, and peak values around the failing operation.
- Include the complete exception, GPU model, operating system, framework and CUDA versions, driver version, and whether eager execution succeeds.
This package lets a framework maintainer or application vendor distinguish a reproducible allocator issue from an environment-specific conflict. NVIDIA’s debugging guidance describes the same expectation for a useful report: provide versions, GPU details, and complete error information.
A compact decision procedure
- Capture: save the full error and environment details.
- Locate: identify loading, cache allocation, eager execution, compilation, capture, or warmup as the failing stage.
- Compare: inspect device-wide usage alongside PyTorch allocated and reserved memory.
- Measure: record current and peak allocator values and, when necessary, a memory snapshot.
- Test capacity: lower one input, batch, cache, or model dimension and rerun.
- Test fragmentation: only when allocator evidence shows unused or split blocks preventing a request.
- Test graphs: compare eager execution and account for static inputs and graph-private pools.
- Escalate: submit the minimal reproducer with versions, hardware, and the complete diagnostics.
This order prevents the most common misdiagnosis: treating every CUDA OOM as a cache-clearing problem. The correct fix follows the allocation that failed and the memory evidence surrounding it.
Frequently Asked Questions
Why does nvidia-smi show more memory used than PyTorch allocated memory?
PyTorch’s caching allocator can reserve unused blocks for reuse, and other CUDA allocations may occur outside PyTorch. Compare memory_allocated(), memory_reserved(), allocator statistics, and device-wide process usage rather than treating one number as the complete picture.
Can torch.cuda.empty_cache() make a model that exceeds VRAM fit?
No. It returns unused cached blocks to the device allocator or other applications, but it cannot release live tensors or increase PyTorch’s capacity for them.
Should I disable CUDA Graphs whenever a laptop reports OOM?
Only if the failure occurs during graph compilation, capture, or warmup, or if an eager comparison shows graph-specific retention. Graph controls are stage-specific, and eager execution can trade memory behavior for lower throughput.
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