A GPU out-of-memory error can mean the model weights do not fit, the key-value (KV) cache needs more space than is available, memory is fragmented, or the runtime ran out of temporary headroom during warm-up or graph capture. Check the log to find where allocation failed before changing settings: reducing context helps a cache-capacity problem, but will not fix every kind of OOM.
Find when the out-of-memory error occurs
Start with the startup or inference log and identify the last completed stage. The allocation stage narrows down the likely cause and prevents changing settings that cannot address it. NVIDIA’s GPU memory troubleshooting guide distinguishes weight loading, KV-cache allocation, and graph-capture or warm-up failures.
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- While loading weights: Check model size, precision, and how the model is distributed across GPUs.
- After the weights load: Look for KV-cache allocation failure or signs of fragmentation.
- During warm-up or graph capture: Investigate temporary allocations and whether the runtime has enough headroom beyond its cache allocation.
Changing context length is most relevant when the log points to KV-cache capacity. Use the remedy that matches the failure stage.
Estimate whether the model weights fit
Weights are only one part of GPU memory use. KV cache, activations, communication buffers, CUDA graphs, adapters, and model-specific state can also take memory. NVIDIA gives this estimate for weight memory per GPU:
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weight_memory_per_gpu = total_parameters × bytes_per_parameter / tensor_parallelism
In NVIDIA’s examples, BF16 and FP16 use 2 bytes per parameter, FP8 uses 1 byte, and INT4 and NVFP4 use 0.5 bytes. NVIDIA estimates that Llama 3.1 8B in BF16 needs 16 GB for weights on one GPU; that figure excludes the additional memory required for KV cache and runtime overhead. It is an estimate, not a guarantee that a model and workload will fit in a particular runtime.
Match the fix to the failure
If KV-cache allocation fails, reduce context length
A long configured context can require a large KV cache. If the log identifies cache allocation as the problem, lower the runtime’s maximum model length to suit the actual task. The configured context covers both input and output tokens, so leave enough room for the prompts and responses you need.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallDo not lower a memory-utilization budget automatically when the cache itself cannot fit: in NVIDIA’s documented NIM case, that can shrink the cache budget and make a cache-capacity failure worse. Check what the log says before adjusting memory-budget settings.
If memory appears fragmented, check allocator evidence
An allocation can fail even when the device reports free memory if there is no sufficiently large contiguous block available. For this specific fragmentation case, NVIDIA documents PYTORCH_ALLOC_CONF=expandable_segments:True as a PyTorch allocator mitigation. It does not add physical GPU memory, and compatibility can depend on the deployment.
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PyTorch memory snapshots can show allocation history and stack traces. Compare PyTorch’s allocator accounting with device usage as well: memory used outside PyTorch can help explain a discrepancy. Avoid treating allocator tuning as a general fix for a model that simply needs more capacity.
If warm-up or graph capture fails, inspect the preceding allocations
Graph capture or warm-up may need memory beyond what the KV cache uses, and there is no single headroom figure that applies to every model and configuration. In the NIM case NVIDIA documents, if the failure follows KV-cache allocation, reducing --gpu-memory-utilization can leave more headroom by reducing cache allocation. This advice is specific to that backend; do not copy NIM flags into another runtime without checking its own configuration and logs.
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Make sure the runtime is using the GPU you expect
A runtime detecting a GPU does not necessarily prove that inference is running on it. Check GPU discovery and actual execution before concluding that the device lacks enough memory.
- For Ollama, use its troubleshooting documentation to inspect debug logs, GPU discovery, and runtime-specific device selection. In container setups, check GPU access, drivers, and relevant device permissions.
- For llama.cpp on AMD, the ROCm guide notes that listing a device confirms the ROCm libraries were found, but does not prove the GPU is doing the computation. Verify actual GPU use with a short model benchmark.
When to consider different hardware
More GPU memory may help when logs and measurements establish a genuine capacity bottleneck after reasonable model, precision, and context adjustments. It will not fix driver, device-discovery, or GPU-access problems. Check runtime compatibility and actual device use before buying hardware; a GPU recommendation also depends on your operating system, power supply, case, workload, and budget.
When comparing remedies, consider which failure stage each addresses, how much memory it may free, effects on usable context or output quality, inference speed, compatibility, and reversibility. The sources do not establish a universally best precision, context length, allocator setting, or GPU purchase.
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