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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driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteTwo AI workloads can share a GPU until one reaches a new memory peak and fails, even while the other keeps responding. The key is to distinguish live tensor allocations from memory reserved by a framework’s allocator, then identify exactly which stage of the failing workload ran out of room. An OOM message does not, by itself, mean the GPU is faulty—or that the visible memory total tells the whole story.
Why GPU memory can look occupied when it is not all in use
GPU memory holds more than model weights. A workload may also need memory for runtime data such as activations and, during language-model inference, a key-value (KV) cache. Memory use can rise after a model has loaded, so successful startup does not guarantee that a later request or second workload will fit.
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PyTorch’s caching allocator adds another wrinkle. torch.cuda.memory_allocated() reports memory occupied by tensors, while torch.cuda.memory_reserved() reports memory managed by PyTorch’s allocator. PyTorch notes that “The unused memory managed by the allocator will still show as if used in nvidia-smi.” As a result, the device-level display can show more memory in use than PyTorch reports as live tensor allocation. PyTorch CUDA semantics
Reserved-but-unused memory is not the same as live memory held by another process. Calling torch.cuda.empty_cache() releases unused cached blocks so other GPU applications can use them; it does not release memory occupied by live tensors or create more room for those tensors. It also cannot free another process’s live allocations. PyTorch CUDA semantics
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Why an allocation can fail despite apparently available memory
An out-of-memory error can have different causes. The useful question is not simply “How much VRAM is free?” but “What allocation failed, at what stage, and which process or allocator needed it?”
- Capacity shortage: The requested live allocation does not fit alongside existing allocations.
- Fragmentation: The allocator cannot find a sufficiently large contiguous block, even when aggregate memory figures appear to leave room. NVIDIA documents fragmentation-related OOM cases and ties possible workarounds to the observed deployment context; an allocator setting should not be treated as a universal fix. NVIDIA NIM memory troubleshooting
- Another process: A separate workload may have live allocations that PyTorch’s cache-clearing call cannot reclaim.
- Allocator reservation: Some memory shown as used by the device monitor may be cached by PyTorch rather than occupied by live tensors.
Find the failure stage before changing settings
Read the logs around the error. NVIDIA’s troubleshooting guidance distinguishes weight-loading failures from KV-cache allocation failures: a model may fail before it starts serving, or load successfully and then run out of room when the cache is allocated. Compilation, warmup, graph capture, or a later workload peak may also be the point where a particular setup fails; use the actual logs rather than assuming every OOM is a model-loading problem.
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- Weights: Check whether the selected model, precision, and parallelism fit the available memory.
- KV cache: Check the configured context length and the cache demand of the workload. A longer supported input-plus-output sequence can require more cache.
- Later peak or other stage: Identify what else was running and what allocation the framework reports at the moment of failure.
For scale, NVIDIA NIM documentation gives an example in which a 70-billion-parameter model in BF16 requires approximately 140 GB for weights. That is NVIDIA’s example for weight memory, not a total runtime budget: cache, activations, and overhead may require additional capacity. NVIDIA NIM memory troubleshooting
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A practical diagnostic sequence
- Identify the device and processes. Use
nvidia-smito inspect the GPU and processes using it. Treat its memory figures as a device-level view, not a breakdown of live PyTorch tensors versus allocator reservations. - Compare PyTorch’s allocated and reserved memory. In the failing process, inspect
torch.cuda.memory_allocated()andtorch.cuda.memory_reserved(). A substantial gap points to memory managed by the allocator but not currently occupied by tensors. - Look beyond PyTorch if totals do not line up. PyTorch’s memory guidance explains how to inspect allocator statistics and snapshots, and how to compare the allocator’s view with raw CUDA allocation information when external allocations are suspected. Understanding CUDA Memory Usage
- Pinpoint the failing operation in the logs. Determine whether the error arose during weight loading, KV-cache allocation, compilation or warmup, graph capture, or a later request. The relevant fix depends on that stage.
- Change the setting that matches the cause. For a weight-fit failure, consider a supported lower precision or an appropriate multi-GPU profile. For a KV-cache failure, reduce maximum context length if the shorter input-plus-output limit still meets the workload’s needs. NVIDIA describes both approaches for relevant NIM configurations. NVIDIA NIM memory troubleshooting
- Address concurrency or capacity only after measuring. Reduce simultaneous workloads, move work to a CPU or another GPU where the software supports it, or consider a higher-VRAM GPU if a measured capacity gap remains. For fragmentation, follow guidance for the specific framework version and deployment rather than applying an environment-variable recipe indiscriminately.
When memory offload is—and is not—a solution
Some systems can use CPU memory alongside GPU memory for large-model inference, but this is platform-specific rather than a general ability of desktop GPUs to borrow system RAM transparently at equivalent speed. NVIDIA’s memory-sharing article discusses Grace Hopper and Grace Blackwell systems; its GH200 example combines 96 GB of GPU memory with 480 GB of CPU LPDDR memory in a single address space. Those figures describe that platform, not a typical desktop configuration. NVIDIA Developer Blog: CPU-GPU memory sharing
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What to conclude from two workloads and one OOM
Do not assume two processes divide VRAM evenly: their memory needs can differ and change over time. One can keep working while the other hits a new allocation limit. Separate live allocations, PyTorch’s reserved cache, other processes, fragmentation, and the failing stage before choosing a remedy. If configuration and workload changes still leave a measured capacity gap, more VRAM may be appropriate; it is not the first diagnostic step.
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