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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesA GPU out-of-memory (OOM) error means the workload requested a memory allocation that the GPU could not satisfy. The failure may happen while loading weights, during a training forward or backward pass, when an inference server builds a KV cache, or later when a temporary operation needs a large workspace. The useful fix is therefore diagnostic: identify which phase and which allocation consumed VRAM, then reduce that demand, move part of the workload elsewhere, or add capacity.
A model fitting in VRAM is not the same as the complete job fitting. Training also needs gradients, optimizer state, saved activations and temporary tensors; inference needs context/KV-cache space, active requests, output buffers and runtime overhead. The steps below show how to tell those cases apart and what to change without treating every OOM as a batch-size problem.
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What a CUDA out-of-memory error actually means
PyTorch may display torch.OutOfMemoryError: CUDA out of memory or the older cuda runtime error(2): out of memory. In plain terms, the allocator was asked for a block that was not available under the current conditions. NVIDIA describes the same situation as a model needing more VRAM than the GPU provides: NVIDIA NIM troubleshooting.
The failed request is not necessarily the model weights. A model can load successfully and then fail on the first training backward pass, on a longer inference prompt, when several requests share a server, or during graph capture. Conversely, a load-time failure usually points to weight storage, precision, device placement, another process, or insufficient contiguous space.
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| Failure phase | What is commonly being allocated | Useful first question |
|---|---|---|
| Model loading | Weights, quantization buffers, loader workspace and runtime reservations | Do the selected model and precision fit on the intended device after leaving headroom for the runtime? |
| Training forward pass | Activations, temporary operator buffers and sometimes a larger-than-expected batch or sequence | Did batch size, sequence length or an operation change from the previous successful run? |
| Training backward or optimizer step | Gradients, saved activations, optimizer state and temporary gradients | Is the model fitting only until backward or optimizer state is created? |
| Inference prefill | Prompt processing, attention workspaces and initial KV-cache allocation | Is the prompt or maximum context longer than the server’s tested configuration? |
| Inference decode or later request | KV cache for active sequences, output buffers and per-request temporary memory | Did concurrency, output length or accumulated sessions increase? |
| Graph capture or warmup | Captured graphs and their workspaces, in addition to normal model memory | Does the failure occur only during startup or capture rather than ordinary execution? |
| Later unrelated operation | A temporary allocation, another process, or allocator fragmentation | What changed in system-level use and in framework-level allocated/reserved memory immediately before the request? |
NVIDIA’s phase-specific examples apply to NIM deployments; flags and recommendations shown for NIM should not be assumed to be universal settings for every inference server.
Diagnose the failing allocation before tuning anything
Changing several memory settings at once can hide the cause. Capture the phase, process usage and framework counters first.
- Record the exact operation. Save the complete traceback and note whether the failure occurs while loading, in forward, in backward, at the optimizer step, during inference prefill/decode, at graph capture, or on a later request. Also record model revision, precision, batch or concurrency, input and maximum context length, and the GPU model.
- Check every process on the GPU. Run
nvidia-smiand inspect the process list, total memory in use and free memory. Stop or reconfigure an unrelated process only when you know it is safe to do so. A full device caused by another process is different from a single process whose own working set is too large.
- Compare PyTorch’s two basic counters. During the run, log
import torch print('allocated', torch.cuda.memory_allocated() / 1024**3, 'GiB') print('reserved', torch.cuda.memory_reserved() / 1024**3, 'GiB') print('free,total', [x / 1024**3 for x in torch.cuda.mem_get_info()])memory_allocated()is live tensor memory tracked by PyTorch.memory_reserved()includes blocks held by PyTorch’s caching allocator, including blocks currently unused but retained for reuse. - Inspect statistics when the simple counters are not enough. Use PyTorch’s memory statistics and snapshots to see allocation sizes, inactive blocks and stream history. The snapshot tools cover allocations tracked by PyTorch’s allocator; memory allocated by another library or by custom CUDA code may require a separate investigation. See Understanding CUDA Memory Usage.
- Interpret a reserved-but-unallocated gap cautiously. A large gap is a clue to investigate caching or fragmentation, not proof that fragmentation caused the error. A request can fail when enough total free bytes exist but no suitable contiguous block is available. NVIDIA documents this as a particular fragmentation case in its troubleshooting guide.
- Repeat with one controlled change. Lower one workload dimension, such as per-device batch or maximum context, and record whether the failing allocation moves or disappears. This produces more useful evidence than enabling several allocator variables simultaneously.
PyTorch’s memory-management documentation explains the distinction between tensors and cached blocks in CUDA semantics—Memory management. The cache can make nvidia-smi look nearly full even after tensors have been released, because the process retains blocks for reuse.
Where the VRAM goes
Training memory is more than model weights
A training process may hold all of the following at once:
- Parameters: the model weights, often in a lower-precision format when mixed precision is enabled, plus any higher-precision copies required by the implementation.
- Gradients: one or more gradient tensors created during backward.
- Optimizer state: momentum, variance and other state; adaptive optimizers can require multiple additional tensors per parameter.
- Saved activations: intermediate values retained for backward. Their size changes with per-device batch size, sequence length, model depth and hidden size.
- Temporary workspaces: scratch memory requested by attention, matrix multiplication, convolution and other kernels.
- Framework and runtime overhead: CUDA context, kernels, communication buffers, dataloader staging and allocator reservations.
In attention implementations where score tensors are materialized, the score-memory term can grow with the square of sequence length. That is why doubling context can cost much more than doubling a short input, even when parameter count is unchanged.
Hugging Face’s Transformers v5.12.0 memory guide gives a named example: training a 4-billion-parameter model in mixed precision at batch size 16 is estimated at roughly 85 GB of GPU memory. That is the guide’s example estimate, not a universal requirement; architecture, optimizer, sequence length, implementation and activation strategy all change the result. The versioned explanation is at GPU memory usage. The page notes that a newer v5.19.0 version exists, so verify version-sensitive details against the documentation for the library you are running.
Inference has a different budget
Inference normally avoids training gradients and optimizer state, but its working set still includes:
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- model weights in the chosen precision;
- the KV cache for every active sequence, which grows with context and generated tokens;
- prompt-prefill and decode workspaces;
- concurrent requests, batching and output buffers;
- graph-capture or warmup allocations, when the server uses them; and
- runtime, communication and allocator overhead.
Consequently, a model that loads for one short prompt may fail with a long context, a larger maximum sequence setting or several simultaneous users. NVIDIA’s NIM documentation specifically warns that reducing its --gpu-memory-utilization setting can shrink the KV-cache budget and make a KV-cache capacity failure worse. In a documented graph-capture case, lowering that setting can instead leave the headroom needed for capture. Those are NIM phase-specific behaviors, not a general cure for every server.
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Reduce memory during training or fine-tuning
Lower per-device batch size, then recover effective batch with accumulation
Per-device batch size directly affects activation and temporary memory. Reduce it until a forward and backward pass fit. If the optimization recipe requires a larger effective batch, process several microbatches before the optimizer update with gradient accumulation. The effective batch can remain similar, but execution takes more steps per update and throughput, synchronization and optimization behavior can change.
for step, batch in enumerate(loader):
loss = model(**batch).loss / accumulation_steps
loss.backward()
if (step + 1) % accumulation_steps == 0:
optimizer.step()
optimizer.zero_grad(set_to_none=True)Use the accumulation setting supported by your training framework and confirm that loss scaling, scheduler stepping and distributed synchronization match the intended effective batch.
Shorten sequences where the task permits
Trim padding, cap input length, pack examples efficiently or use a smaller maximum sequence length when the application allows it. This reduces activation and attention work, and can be particularly valuable when materialized attention scores dominate. A shorter context can alter task quality, truncation behavior and evaluation results, so measure the effect rather than treating it as a free optimization.
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Use mixed precision only with compatible hardware and software
Mixed precision stores some tensors in lower precision and can reduce memory, but savings depend on which weights, gradients, accumulators and temporary tensors remain in higher precision. Enable it through the framework’s supported path for the GPU and model, then check numerical stability and validation quality. A lower-precision setting is not guaranteed to fit every architecture or to preserve identical training behavior.
Checkpoint activations to trade compute for memory
Activation (gradient) checkpointing stores selected inputs instead of every intermediate activation and recomputes the missing values during backward. It can substantially reduce activation memory, but adds forward computation and may lower throughput. PyTorch describes this explicitly as a memory-for-compute trade in its Frequently Asked Questions; the Transformers guide also covers the option.
Do not retain autograd history accidentally
A common leak is appending a loss, output or metric tensor that still requires gradients to a Python list on every iteration. The list then keeps the entire computation graph alive. Store a detached value or a scalar instead:
loss_values.append(loss.detach().item())Also review custom logging, exponential moving averages and validation code for tensors that should not participate in the next backward pass. Use inference-mode or no-grad evaluation paths where appropriate.
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Optimizer choice and state precision can dominate fine-tuning memory. The Transformers memory guide discusses quantized Adam as one implementation-dependent way to reduce optimizer-state storage. It may introduce compatibility, precision or performance trade-offs, so verify support for the exact optimizer, hardware and training stack rather than assuming every quantized optimizer behaves identically.
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Check distributed and staging overhead
Data-parallel training can replicate parameters and optimizer state on each GPU, while communication buckets and input staging consume additional memory. If the model fits on one device but fails after enabling distributed training, inspect per-rank allocation and communication settings; reducing the local batch does not remove replicated state.
Reduce memory for inference and serving
Lower concurrency, batch size or maximum context
Reduce the number of active sequences and the batch size first when the product can tolerate lower throughput. If the error names KV-cache capacity or appears after long prompts, lower the maximum context or the allowed output length. NVIDIA notes that a maximum-context limit constrains the combined input-plus-output token length per request; make that limit visible to clients so truncation or rejection is predictable.
Do not lower a server’s memory-utilization setting reflexively. In NIM, a lower --gpu-memory-utilization value can reduce the space reserved for KV cache. It is useful only in the documented situations where runtime or graph-capture headroom is the problem.
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A smaller model, supported quantization or lower-precision profile can reduce weight memory enough to leave room for KV cache and runtime allocations. Compatibility is hardware- and model-specific: confirm that the selected kernels and tensor types are supported and that quality, latency and throughput meet the service requirement. NIM’s examples also use higher tensor or pipeline parallelism to distribute a model across GPUs; the appropriate profile depends on the model and hardware.
Place weights across devices or offload them
Hugging Face Accelerate’s device maps can place layers on available GPUs and offload some weights to CPU or disk. Its Big Model Inference guide cautions that nominal GPU memory is not all available for weights because kernels and outputs need space too.
Offload requires sufficient host RAM or fast storage and transfers data across the bus or storage path. It can make an otherwise impossible model run, but latency and throughput may degrade substantially. Reserve host capacity for the operating system and other processes; a CPU-offloaded model can simply exchange a GPU OOM for host-memory pressure if the placement is too aggressive.
Use multiple GPUs only with supported placement
Multiple GPUs add aggregate capacity when the framework and model support tensor, pipeline or layer placement. They do not automatically pool VRAM for an arbitrary single allocation. Inter-GPU transfers, topology, synchronization and replication can affect performance, and a model may still fail if one layer or temporary workspace must fit entirely on a single device.
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Account for warmup and graph capture
If loading and ordinary requests work but startup capture fails, leave additional headroom or follow the inference server’s documented capture settings. Do not infer from that symptom that the model weights alone are too large. Capture can reserve buffers for a representative shape, so maximum batch, context and output dimensions matter.
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What empty_cache() can and cannot do
torch.cuda.empty_cache() releases unused blocks held in PyTorch’s caching allocator so they can be returned to the driver or used by another GPU application. It does not free live tensors and does not increase the amount of VRAM available for those tensors. PyTorch states this distinction in its memory-management documentation: occupied memory held by tensors is not freed by the call.
del temporary_tensor
torch.cuda.empty_cache()The call is useful after genuinely deleting temporary objects when another process needs the unused cache, or as a controlled experiment. Calling it after every operation can add allocation overhead and does not fix a working set that is intrinsically too large. If memory rises every iteration, find the retained tensor or graph rather than placing empty_cache() in the loop.
When fragmentation or allocator behavior is the suspect
Fragmentation is plausible when a large reserved-but-unallocated amount persists, allocations vary widely, and the failing request needs a block that cannot be assembled from the available pieces. Confirm that pattern with allocator statistics or snapshots and the exact failing allocation. A full-looking nvidia-smi display alone is not enough.
For PyTorch’s native allocator, max_split_size_mb is documented as a last resort for situations with many inactive split blocks. It is allocator-, version- and workload-sensitive; choose a value from measured allocation patterns, not a generic internet recommendation. It cannot create physical VRAM.
NVIDIA documents expandable_segments:True for a specific NIM fragmentation scenario. Apply that setting only when the NIM documentation and your deployment match the scenario. Environment variables and allocator backends differ across PyTorch and serving stacks, so verify the active backend before changing them.
Choosing a remedy: what you give up
| Remedy | Memory saved at the failed stage | Behavior or quality constraint | Speed and resource cost | Implementation and compatibility |
|---|---|---|---|---|
| Smaller per-device batch | Activations, gradients and batch-dependent temporaries | Lower local batch; effective batch may require accumulation | More steps and possible throughput loss | Usually simple and broadly compatible |
| Gradient accumulation | Peak memory per microbatch | Same nominal effective batch only if update and scheduler logic are adjusted | More forward/backward passes per optimizer update | Requires correct framework configuration |
| Shorter sequence or context | Activations, attention work and KV cache | Less input/output token capacity and possible truncation | Often faster, but may reduce task quality | Simple when product limits permit |
| Mixed or lower precision | Selected weights, activations or cache tensors | Numerical and model-quality behavior can change | May improve speed; hardware support is required | Precision support is model- and GPU-specific |
| Activation checkpointing | Saved training activations | Same mathematical target in supported implementations, with recomputation | Extra compute and often lower training throughput | Needs framework/model support |
| Detach retained tensors | Unintentionally retained autograd graphs | No intended model-quality change | Usually reduces overhead rather than adding cost | Requires finding the retaining code |
| CPU or disk offload | GPU weight or layer residency | Transfers can increase latency; host capacity becomes a requirement | Uses RAM, storage and interconnect bandwidth | Needs supported device mapping and enough host resources |
| Multi-GPU placement | Per-device weight and working-set pressure | Placement and communication constraints remain | Interconnect and synchronization overhead | Requires supported parallelism and topology |
| Allocator tuning | Potentially unusable fragmented blocks | No change to genuine tensor demand | Can alter allocation behavior or overhead | Use only after allocator diagnosis |
| Higher-capacity GPU | Raises the physical VRAM ceiling | Does not fix software leaks or unsupported kernels | Hardware, power, cooling and platform cost | Match capacity to the measured workload |
Practical troubleshooting playbooks
If training fails during backward
- Confirm that model loading and the forward pass succeed.
- Log allocated and reserved memory before forward, after forward and before backward.
- Reduce per-device batch size; if necessary, shorten sequence length.
- Enable supported mixed precision and activation checkpointing one at a time.
- Check for lists, logs or metrics retaining tensors with autograd history.
- Review optimizer state and consider a supported lower-state-memory optimizer.
- Re-test with the same data shape and record throughput and validation behavior.
If inference fails only with long prompts or many users
- Separate load-time success from prefill and decode behavior.
- Measure the maximum input-plus-output token length and active concurrency.
- Lower concurrency, batch size or context/output limits to identify the controlling dimension.
- Check whether the server is reserving KV-cache space and whether a utilization setting is shrinking that budget.
- Choose a supported lower-memory precision or model profile.
- Use device mapping, CPU/disk offload or supported multi-GPU placement when host resources and latency targets allow it.
If the error appears after repeated requests
- Compare live allocated memory after each request and after cleanup.
- Look for retained outputs, references, CUDA graphs or caches that grow with request count.
- Compare reserved versus allocated memory to distinguish retained allocator blocks from live tensors.
- Use memory statistics or snapshots, then test an allocator setting only if inactive split blocks or another documented pattern is present.
When the GPU simply does not have enough VRAM
If the model weights plus the required working set exceed available VRAM after reasonable workload reductions, no cache-clearing call can make that configuration fit. The remaining choices are a smaller model, a supported lower-memory precision, CPU or disk offload, supported distribution across GPUs, or hardware with more VRAM.
There is no universal minimum VRAM number for an AI workload. Match capacity to the specific model and revision, precision, context length, batch or concurrency, framework, runtime overhead and speed target. A hardware upgrade is justified only after measuring that profile; otherwise, a software leak, oversized context or another process may be the cheaper fix. The available placement and offload patterns are described in Accelerate’s guide, while model- and server-specific capacity examples are covered in NVIDIA’s troubleshooting documentation.
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Documentation links for version-sensitive details
- PyTorch Frequently Asked Questions (created or updated May 8, 2026).
- PyTorch CUDA semantics—Memory management.
- PyTorch Understanding CUDA Memory Usage.
- Hugging Face Accelerate Big Model Inference.
- Hugging Face Transformers v5.12.0 GPU memory usage.
- NVIDIA Troubleshooting GPU Memory Out-of-Memory Errors, updated October 6, 2026.
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