A GPU out-of-memory error means the workload could not get the device memory it needed—but the remedy depends on when the failure occurs. First identify whether it happens while loading model weights, allocating an inference KV cache, or warming up or capturing CUDA graphs. Then check whether the issue is genuine capacity exhaustion, an oversized memory budget, or allocator fragmentation.
What a GPU out-of-memory error tells you
“CUDA out of memory” means a requested allocation could not be satisfied with the GPU memory available to the workload at that moment. It does not, by itself, tell you which part of the workload consumed the memory or whether the GPU simply lacks capacity. A mismatched model profile, a context length that demands too much KV cache, another process using the device, or fragmented allocator memory can all contribute.
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Model weights are only part of an AI workload’s VRAM footprint. Inference and training can also need memory for KV cache, activations, communication buffers, CUDA graphs, adapters, multimodal reservations, hybrid-model state, and runtime overhead. Consequently, a model that appears to fit based on its weight size can still fail during a later phase.
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Find the phase that failed before changing settings
Save the complete error output and note what the workload was doing at the time. Compare GPU use at the failure point with use earlier in the run: a single nvidia-smi reading is only a snapshot. NVIDIA’s mixed-precision guidance recommends monitoring the command while launching a run to observe device-memory use.
- Loading weights: Check whether the model’s precision, tensor-parallel degree, selected profile, and available GPUs can accommodate its weights.
- Allocating the KV cache: Check the configured maximum context or sequence length and the memory remaining after other allocations.
- Warming up or capturing CUDA graphs: Check whether the failure occurs only when graph-related allocations begin.
- During training or later inference: Look for changes in batch size, input shape, concurrent processes, or allocations that grow over time; use the framework’s profiler to locate memory peaks.
Also check whether another process is using the GPU and whether the configuration actually in effect matches the one you intended. NVIDIA’s NIM LLM/VLM troubleshooting guidance emphasizes matching the remedy to the failing phase; NIM-specific flags mentioned below apply to that deployment context, not automatically to every CUDA application.
Estimate the weight footprint—and leave room for everything else
NVIDIA’s NIM troubleshooting guide gives this rough estimate for weight memory on each GPU:
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weight_memory_per_gpu = total_parameters × bytes_per_parameter / tensor_parallel_degree
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Here, tensor_parallel_degree is the number of GPUs across which tensor parallelism distributes the model. The guide assigns the following bytes per parameter for its estimate:
| Weight format | Bytes per parameter in NVIDIA’s estimate |
|---|---|
| BF16 or FP16 | 2 |
| FP8 | 1 |
| INT4 or NVFP4 | 0.5 |
The formula estimates weights, not total inference memory. It assumes the stated precision and parallelism; it does not account for KV cache, activations, graphs, buffers, adapters, multimodal reservations, or runtime overhead. Check that the chosen model profile and GPU arrangement support the format and parallelism you plan to use.
NVIDIA’s current NIM troubleshooting guide gives two illustrative weight estimates: Llama 3.1 8B in BF16 with tensor parallelism of 1 is estimated at 16 GB on one GPU; Llama 3.3 70B in BF16 with tensor parallelism of 4 is estimated at 35 GB per GPU. These are guide estimates for weights, not complete VRAM requirements or guarantees that an inference configuration will fit. The guide does not state a publication year.
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Choose the fix for the failure phase
If the OOM occurs while loading weights
Compare the required weight footprint with the GPU arrangement and confirm that the selected model profile is intended for that hardware. For example, NVIDIA’s guide estimates that a 70-billion-parameter model in BF16 needs approximately 140 GB for weights before additional inference memory. If weights alone do not fit, consider a supported lower-precision profile or a profile distributed across more GPUs. Neither change guarantees that the full workload will fit; account for its other allocations as well.
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If the OOM occurs during KV-cache allocation
Inspect the maximum context or sequence length and how much memory remains after weights and other reservations. Longer contexts can require more KV-cache memory. If the configured context drives the cache beyond its available budget, reduce the maximum length to one that still serves the workload.
For NVIDIA NIM, the troubleshooting guide warns that lowering --gpu-memory-utilization reduces the budget available to the KV cache and can make a KV-capacity failure worse. Do not treat a lower utilization setting as a universal OOM fix: confirm the effective configuration and model profile, and use NIM-specific options only in the relevant deployment.
If PyTorch reports much more reserved than allocated memory
PyTorch’s reserved memory can exceed memory occupied by live allocations. If a large contiguous request fails despite apparent free space, the difference may reflect allocator fragmentation: free memory is split into pieces that cannot satisfy the request. NVIDIA documents PYTORCH_ALLOC_CONF=expandable_segments:True as a remedy for the fragmented-allocation case it describes. PyTorch also documents max_split_size_mb as a last resort when inactive split blocks are implicated; that option is meaningful with the native allocator backend.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThese settings change allocator behavior; they do not add physical VRAM or solve an OOM caused by live allocations already consuming the available memory. Use them when the allocator evidence points to fragmentation, not as a default response to every CUDA error.
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If the OOM appears only during CUDA graph capture or warm-up
Graph capture can require memory beyond the workload’s ordinary allocations. Inputs may persist, graph-private memory pools do not freely share cached blocks with the global pool, and allocations across streams or pools may not be reusable in the way you expect. CUDA frees are suppressed during capture, so empty_cache() cannot return cached blocks to CUDA at that point.
Release tensors and gradients that are no longer needed before entering capture, then check whether capture is required and whether the specific runtime offers a way to adjust its graph-memory reservation. In NVIDIA NIM, the guide documents options for disabling graphs or changing reserved-memory settings. Disabling graphs trades away potential throughput, and those NIM options are not universal CUDA settings.
Reduce memory demand without masking the cause
Consider mixed precision, then measure
Mixed precision can reduce the memory used by tensors and may allow a larger batch, but the saving depends on the model and framework; not every allocation necessarily uses the reduced-precision dtype. NVIDIA recommends observing use with nvidia-smi and profiling when automatic mixed precision provides little speedup. Validate output quality, numerical behavior, and actual memory use after changing precision.
For TensorFlow custom training loops using mixed_float16, the official guide calls for a LossScaleOptimizer and scaled and unscaled loss gradients, and advises keeping model outputs in float32. These are correctness requirements to account for when enabling mixed precision, not optional memory switches.
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Profile peaks, especially in multi-GPU runs
TensorFlow’s GPU profiler guide recommends using its memory profiler to see how close the program gets to peak memory use. For multi-GPU workloads, inspect traces for uneven work and communication behavior rather than assuming that adding GPUs will automatically double performance or evenly divide every allocation.
Gradient accumulation and activation checkpointing are training techniques readers may encounter, but the cited material does not establish a cross-framework prescription or quantified memory savings for them. Their effects and implementation depend on the framework, model, and version; do not assume they will fix a particular OOM without checking the relevant documentation and measuring the result.
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| Option | What it addresses | What to check or trade off |
|---|---|---|
| Reduce maximum context length | KV-cache demand driven by long contexts | Confirm the shorter limit still meets the workload’s needs. |
| Use a supported lower-precision profile | Weight and potentially tensor memory | Confirm hardware and runtime support; validate numerical behavior and output quality. |
| Distribute a supported profile across more GPUs | Per-GPU weight requirements in the stated parallelism arrangement | Check the profile, available GPUs, and communication behavior; other memory needs remain. |
| Adjust allocator settings | Some fragmentation-related allocation failures | Use only when allocator evidence supports fragmentation; this does not create capacity. |
| Disable or adjust graph capture | Memory pressure specific to graph warm-up or capture | Confirm the runtime supports the setting; disabling graphs can reduce throughput. |
| Use a GPU with more VRAM | A measured requirement that still exceeds available capacity after configuration and workload issues are addressed | Base the decision on the actual model, profile, and measured workload; there is no universal GPU recommendation. |
When more VRAM is the right answer
Consider hardware with more VRAM only after the logs and measurements show that the workload’s real requirement still exceeds the current GPU once profile, context length, concurrent use, and avoidable allocations have been checked. If a particular phase or allocator behavior is responsible, changing hardware may be unnecessary; if live requirements remain above capacity, software tuning cannot make that physical limit disappear.
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