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Two AI workloads can compete for one GPU even while both appear operational: one may keep responding until the other reaches a new memory peak and fails. The key is to identify what the memory figures represent and where the failure occurs. In PyTorch, live tensor allocations and memory held in the caching allocator are different; device-level tools such as nvidia-smi do not make that distinction on their own.
Why GPU memory can look occupied when tensors are not
GPU memory holds more than model weights. An AI process may also need runtime data, including a key-value (KV) cache during inference. If a later operation needs more memory than is available, it can fail even though the model previously loaded and appeared to work.
PyTorch distinguishes memory occupied by tensors from memory managed by its caching allocator. torch.cuda.memory_allocated() reports memory occupied by tensors; torch.cuda.memory_reserved() reports memory held by the allocator. PyTorch explains that unused memory managed by the allocator can still appear as used in nvidia-smi (PyTorch CUDA semantics).
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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driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteThis reservation is not the same as another process’s live allocation. A second workload can genuinely consume device memory, and one process cannot free another process’s live tensors by clearing its own cache. The apparent mismatch between a framework’s tensor count and device-level usage is a clue to investigate, not proof that all the reported memory is available.
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Find the stage where the allocation fails
An out-of-memory error is more useful when tied to the operation that triggered it. NVIDIA separates weight-loading failures from KV-cache allocation failures in its NIM troubleshooting guidance. Other peaks, such as graph compilation or warmup, can also matter in a particular runtime.
Model weights do not fit
If failure occurs while loading weights, compare the model’s weight requirement at the chosen precision and parallelism with the GPU memory available to that process. NVIDIA gives the example that a 70-billion-parameter model in BF16 requires approximately 140 GB for weights. That is a weight-memory example, not a complete inference budget: runtime data, cache, and overhead can require additional memory.
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Review whether a lower precision or a supported multi-GPU profile with more tensor or pipeline parallelism is available for the software and hardware in use. These options have compatibility and workload trade-offs; they do not guarantee that a particular model will fit.
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KV-cache allocation fails after loading
A model can load successfully but fail when the runtime allocates its KV cache. Cache demand can depend on context length, so inspect the configured maximum context and the requested input-plus-output sequence length. NVIDIA lists reducing maximum context length as a way to reduce KV-cache demand; the trade-off is that longer sequences may no longer be supported.
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A later peak or fragmented allocation fails
Some failures arise when a workload requests a large contiguous block that the allocator cannot provide, even if aggregate memory figures seem to suggest room remains. NVIDIA describes fragmentation as a possible cause and ties workarounds to the observed failure and deployment context. Check the framework’s reserved-but-unallocated memory and follow guidance that matches the framework version and workload rather than treating an allocator setting as a universal fix.
Diagnose two workloads on one GPU
- Identify the device, processes, and failing workload. Use
nvidia-smifor a device- and process-level view. Its usage figure alone does not distinguish PyTorch tensors from memory reserved by PyTorch’s allocator. - Compare PyTorch’s allocated and reserved memory. Check
torch.cuda.memory_allocated()andtorch.cuda.memory_reserved()in the affected process. If needed, inspect allocator statistics or a memory snapshot using PyTorch’s CUDA memory guidance. - Investigate usage PyTorch does not account for. If device-level use exceeds the affected process’s allocator accounting, other processes or allocations outside that allocator may explain the gap. PyTorch’s memory guidance discusses comparing allocator reports with raw CUDA allocation information when external allocations are suspected.
- Read the error and logs for the failing stage. Determine whether the error occurs during weight loading, KV-cache allocation, graph capture or warmup, or a later operation. Then focus on the memory consumers and settings relevant to that stage.
- Change the relevant constraint. For a weight-fit problem, review model size, precision, and supported parallelism. For a cache-fit problem, review context length against the workload’s needs. For suspected fragmentation, examine allocator statistics and use framework- and deployment-specific guidance.
- Reduce simultaneous demand if needed. If both workloads do not fit together, run them separately or move work to CPU or another GPU where the software supports it. Consider a GPU with more VRAM only after measuring the remaining capacity gap.
What clearing PyTorch’s cache can and cannot do
torch.cuda.empty_cache() releases unused cached blocks held by PyTorch so other GPU applications can use them, as described in the PyTorch CUDA documentation. It does not free memory occupied by live tensors, and it does not create extra capacity for those tensors. It may help when unused cached blocks are the relevant issue; it will not solve a workload whose live allocations exceed available memory.
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When configuration changes are not enough
If measured demand still exceeds device capacity after adjusting the model, precision, context, or concurrency, more VRAM may be appropriate. A capacity label such as 24 GB is a filter for evaluating hardware, not a guarantee that every model or pair of workloads will fit. The required amount depends on weights, runtime allocations, context, and simultaneous use.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchMemory offload is also platform-specific. NVIDIA describes CPU/GPU memory sharing for 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. This is not a general claim that a typical desktop GPU can transparently borrow system RAM at equivalent speed. See NVIDIA’s discussion of inference and KV-cache offload for that platform-specific context.
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