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A CUDA out-of-memory (OOM) error means one allocation failed; it does not identify the cause. On a laptop GPU, the failure may come from live tensors exceeding usable VRAM, another process consuming the device, allocator fragmentation, or memory retained by CUDA Graphs or a model-serving stage. Capture the complete error, identify the stage that failed, then compare live allocation, reserved cache, and device-wide usage before changing settings. The sequence below applies generally, with PyTorch, NVIDIA NIM, and CUDA Graph behavior called out separately.
What a CUDA OOM actually tells you
The message “CUDA out of memory” reports a failed allocation, not a diagnosis. The same text can appear while loading model weights, allocating a key-value cache, compiling or warming up a CUDA Graph, or executing an ordinary tensor operation. Those stages have different memory lifetimes and remedies. NVIDIA’s NIM troubleshooting guidance separates weight loading, cache allocation, and CUDA Graph compilation or warmup for that reason: NVIDIA’s stage-specific OOM guidance.
First determine whether the requested allocation would exceed the laptop GPU’s usable capacity, whether another process has taken part of the capacity, or whether the framework has free blocks that cannot satisfy the request because of fragmentation. A device-wide “free” number and an application’s live-tensor total are different measurements.
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1. Capture the failure before changing anything
Save the full exception and surrounding log output. Do not copy only the final line: PyTorch errors often report the requested allocation, total device memory, currently free memory, allocated memory, and reserved memory. These values provide the initial evidence for distinguishing demand from allocator behavior.
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- Hardware: exact GPU model and the laptop’s operating system.
- Software: framework and version, CUDA version, driver version when available, and the application or serving stack.
- Operation: the layer, kernel, batch, input shape, iteration, model-loading step, cache allocation, compilation, graph capture, or warmup at which it failed.
- Reproducibility: whether it fails every run, only after several iterations, only with a particular input, or only when another GPU application is open.
NVIDIA’s debugging checklist likewise asks for the CUDA and PyTorch versions, GPU model, and complete error details when escalating an issue: NVIDIA debugging strategies.
2. Identify the failing stage
Use the log and the operation that immediately precedes the exception to classify the failure. This is the most important branch in the investigation.
Weight loading
If the process fails while moving weights to the GPU, the model’s parameters and any conversion or temporary buffers already exceed the available capacity. Reduce model size, use a supported lower-memory representation, or free competing GPU processes. Clearing an allocator cache cannot make the model’s live weights smaller.
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Serving systems may reserve a key-value or other inference cache after loading weights. A cache request can fail even when weight loading succeeds. Reduce the cache-related workload setting, such as context or concurrent-request capacity, using the serving product’s documented controls.
Compilation, graph capture, or warmup
Compilation and CUDA Graph capture can allocate temporary buffers, static inputs, and private graph pools. A model that runs eagerly may therefore fail during capture or warmup. Treat that as a graph-specific case rather than assuming every ordinary execution is fragmented.
Ordinary execution
If the error occurs during a regular forward pass, backward pass, or tensor operation, measure the live peak and allocator state around that operation. Input shape, batch size, sequence length, activation retention, and gradients commonly determine the peak.
3. Check device-wide GPU use and competing processes
Compare the application’s allocator statistics with the GPU’s device-wide memory report. On a shared laptop, a browser, desktop compositor, another Python process, or a second model can consume memory that your framework does not report as its own. PyTorch’s memory documentation recommends comparing its allocator view with raw CUDA allocation information when memory may be allocated outside PyTorch: Understanding CUDA Memory Usage.
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At a shell, run nvidia-smi while the workload is active and note the processes and total memory use. Close unneeded GPU applications, stop stale worker processes, and retry from a clean process. If the device report shows substantially less free memory than your application’s live-tensor total would imply, investigate those external allocations before changing allocator settings.
A clean device does not prove that the workload fits: the next allocation can still exceed the remaining capacity, and framework caches can make “used” look larger than live tensors. It does, however, remove one major source of misleading measurements.
4. Measure PyTorch memory at the failing operation
The following diagnostic pattern records current and peak values before and after the operation. It is a PyTorch example, not a universal CUDA API recipe.
import torch
print("allocated before:", torch.cuda.memory_allocated())
print("reserved before:", torch.cuda.memory_reserved())
print("max allocated before:", torch.cuda.max_memory_allocated())
print("max reserved before:", torch.cuda.max_memory_reserved())
# Run the operation that eventually fails here.
print(torch.cuda.memory_summary())
print("allocated after:", torch.cuda.memory_allocated())
print("reserved after:", torch.cuda.memory_reserved())
print("max allocated after:", torch.cuda.max_memory_allocated())
print("max reserved after:", torch.cuda.max_memory_reserved())Place the measurements around the smallest operation that reproduces the error. Reset peak counters between controlled trials when appropriate so that an earlier phase does not obscure the current peak. Keep the complete memory_summary() output with the exception.
For deeper analysis, PyTorch provides allocator statistics, memory snapshots, and a memory visualizer. The tools expose different aspects of state: a snapshot describes allocations tracked by the PyTorch allocator, while raw CUDA allocations and other processes may appear only in device-level information. Interpret them as complementary evidence rather than interchangeable totals: PyTorch’s memory-usage documentation.
5. Read allocated, reserved, and “reserved but unallocated” correctly
Allocated memory is live tensor demand
torch.cuda.memory_allocated() reports memory occupied by live PyTorch tensors; its peak counterpart, max_memory_allocated(), records the highest such value since the relevant reset. If the live peak approaches the GPU’s usable capacity, the workload itself is too large for that device at the tested shape.
Reserved memory is allocator-managed space
torch.cuda.memory_reserved() and max_memory_reserved() include blocks held by PyTorch’s caching allocator for reuse. Those blocks may be unused by live tensors. Consequently, nvidia-smi can show more memory as used than memory_allocated() reports.
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A gap is a clue, not proof of fragmentation
A large difference between reserved and allocated memory, or an error mentioning memory that is “reserved but unallocated,” can indicate cached blocks or fragmentation. Check allocator statistics and inactive split blocks before concluding that fragmentation is the cause. A gap can also reflect normal caching, not unusable memory.
6. Test whether live demand is simply too high
Run controlled experiments, changing one variable at a time. The aim is to discover which requirement crosses the capacity boundary, not to guess at a magic setting.
- Reduce batch size and rerun the same input.
- Reduce input dimensions, sequence length, or tensor sizes.
- Use a smaller model or temporarily disable a memory-heavy feature.
- Compare the peak allocated value and the point of failure after each change.
If a smaller batch or input reliably succeeds and the live peak tracks that change, the evidence points to genuine workload demand. Keep the smallest successful setting as a baseline, then increase one dimension until the capacity limit is understood. NVIDIA recommends reducing tensor or model size when a CUDA Graph OOM is not obvious; the same experiment is useful diagnostically for ordinary execution, but it does not prove that graphs caused the original error: CUDA Graph memory issues.
When live allocations alone exceed usable VRAM, empty_cache(), allocator tuning, or additional system RAM cannot make those tensors fit. The remedy must reduce the live peak or move the workload to a configuration with more usable GPU memory.
7. Use torch.cuda.empty_cache() for its limited purpose
In PyTorch, torch.cuda.empty_cache() releases unused blocks held in the caching allocator so that other GPU applications can use them. It does not delete live tensors, reduce a model’s parameter or activation size, or increase the amount of memory PyTorch can allocate for those live tensors. Official semantics are documented in PyTorch CUDA semantics.
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8. Investigate fragmentation only when measurements support it
Fragmentation occurs when free memory exists in blocks that cannot satisfy the particular contiguous or sized request. Look for a substantial reserved-versus-allocated gap, inactive split blocks, and an allocation request that fails despite apparently available cached space. Use the allocator statistics and memory snapshot tools rather than relying on the gap alone.
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PyTorch exposes allocator configuration, while NVIDIA’s CUDA Graph guidance discusses options such as expandable segments and max_split_size_mb in the relevant allocation contexts: NVIDIA CUDA Graph memory guidance. These are conditional tuning mechanisms, not universal fixes.
- Verify that the option is supported by the installed PyTorch version and applies to the allocator in use.
- Change one allocator setting at a time and record the same peak and summary values.
- Compare startup behavior, throughput, and peak reserved memory, not only whether one run succeeds.
- Remove a setting that changes performance without addressing the measured failure mode.
Allocator tuning cannot compensate for a live peak that is larger than the device. If the evidence instead shows another process consuming memory, stop that process; if it shows graph pools, follow the graph-specific branch below.
9. Handle CUDA Graph capture as a separate memory regime
CUDA Graphs change allocation lifetime. Static input tensors may need to persist for the graph’s lifetime, private graph pools can retain memory, and reuse can be deferred across pools. During capture, cached allocations may not be releasable in the same way as during ordinary eager execution. NVIDIA documents these rules and their fragmentation implications in its CUDA Graph memory troubleshooting guide.
Compare eager execution with capture
Run the same workload without graph capture, if the application permits it. If eager mode succeeds while capture or warmup fails, record the additional allocations and graph-pool behavior rather than applying ordinary cache advice.
Free allocations before capture
Release unneeded tensors and complete unrelated setup before capture begins. Keep only the static inputs and state that the graph actually requires. Because graph pools can preserve allocations, cleanup that would be effective between eager operations may not reduce the capture footprint.
Reduce the graph’s live requirement
Lower input or batch dimensions and test again. If capture still fails while eager execution fits, the graph’s static inputs, private pool, or deferred reuse are part of the measured peak. Address those requirements or use eager execution when the application’s performance trade-off is acceptable.
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10. Treat NVIDIA NIM controls as product-specific examples
NVIDIA NIM documentation distinguishes OOM during model-weight loading, cache allocation, and CUDA Graph compilation or warmup. It documents product options for those stages, including ways to disable graph use in NIM deployments. A setting such as NIM_DISABLE_CUDA_GRAPH or a serving option such as --enforce-eager belongs to that product’s configuration; it is not a generic PyTorch command.
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If you use NIM, follow the version-specific instructions at NVIDIA NIM memory troubleshooting. Disabling graphs can avoid a capture-time OOM but may reduce throughput, so compare the resulting memory and performance against the graph-enabled configuration. If you do not use NIM, do not add these flags to an unrelated CUDA application.
11. 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, and interconnect; it does not provide a blanket promise that any laptop workload can transparently expand into system RAM. NVIDIA explains these dependencies in the CUDA Programming Guide’s unified and system memory section.
Adding RAM, closing applications, or changing an operating-system swap setting may affect overall system stability, but none should be presented as a guaranteed fix for a CUDA allocation that exceeds usable device memory. Base the remedy on the measured GPU allocation pattern.
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12. Choose the remedy from the evidence
| Evidence | Most relevant action | What it changes | Main limitation |
|---|---|---|---|
| Live allocated peak approaches device capacity | Reduce batch, input or tensor size; use a smaller model | Actual workload demand | May reduce throughput, context length, or model capability |
| Another process consumes device memory | Close or stop the competing process and retry cleanly | Memory available to the workload | Does not reduce the workload’s own peak |
| Reserved memory is much larger than allocated, with inactive split evidence | Test compatible allocator fragmentation settings | Block reuse and allocator behavior | Version-dependent; can affect performance and cannot overcome live demand |
| Failure occurs only during graph capture or warmup | Free setup allocations, reduce graph inputs, compare eager mode | Graph pool and allocation lifetime | Eager mode or graph changes can reduce throughput |
| Failure occurs at a NIM loading, cache, or graph stage | Apply the NIM option documented for that exact stage | Serving configuration | NIM flags are not generic CUDA or PyTorch settings |
Evaluate each candidate on five questions: does the evidence show live demand, another process, fragmentation, or graph retention; does the action apply to the exact failing stage; is it compatible with the installed versions; does it reduce memory at a performance cost; and does it change the workload or merely allocator behavior?
13. Build a minimal reproducer when the cause remains unclear
After isolating the failing stage and collecting memory measurements, remove unrelated code until the smallest example that still fails remains. Keep the same GPU, input shape, batch size, framework and CUDA versions, and execution mode. Include:
- the full exception, including requested, total, free, allocated, and reserved values when shown;
- GPU model, operating system, framework and CUDA versions;
- the smallest input or model configuration that reproduces the failure;
- the allocator summary and peak allocated and reserved measurements;
- whether eager execution succeeds and whether graph capture, compilation, cache allocation, or warmup is involved;
- device-wide process information from the clean reproduction.
This package gives maintainers enough context to distinguish a capacity limit from allocator, graph, or external-allocation behavior. NVIDIA’s debugging guidance emphasizes the same version, hardware, and complete-error details when reporting CUDA Graph problems: NVIDIA debugging strategies.
Quick Recap
Practical checklist
- Save the complete OOM exception and identify the exact operation or stage.
- Record GPU model, operating system, framework, CUDA, and driver versions.
- Check device-wide usage and competing processes with
nvidia-smi. - Measure PyTorch allocated, reserved, peak, summary, and snapshot data around the failure.
- Run one-variable reductions of batch, input, tensor, or model size.
- Use
empty_cache()only for unused cache that another application needs. - Test allocator fragmentation settings only when statistics support fragmentation and the installed version supports the setting.
- Separate CUDA Graph capture and NIM stage-specific behavior from ordinary PyTorch execution.
- Do not assume system RAM transparently expands usable VRAM.
- Escalate with a minimal reproducer and the complete diagnostic record.
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