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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11A local model runs out of memory when its weights and the rest of its runtime allocations exceed available GPU memory (VRAM) or system RAM. The fix depends on when the failure happens: while loading weights, allocating the context cache, warming up the runtime, or handling multiple requests. Identify that stage first; reducing context helps some failures, but cannot fix every memory shortage.
What uses memory in a local model?
Model weights are the baseline, not the entire memory budget. In addition to weights, a runtime may need memory for the key-value (KV) cache that holds context, temporary activations, drivers and runtime overhead, communication buffers, adapters, and simultaneous requests. GPU memory can be consumed by other applications, too.
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NVIDIA estimates weight memory from parameter count, bytes per parameter, and how weights are distributed across GPUs. Its example gives an 8-billion-parameter model in BF16 as 16 GB of weights on one GPU (8 billion × 2 bytes). NVIDIA says that example can fit on a 24 GB GPU with room for KV cache and overhead; it is an estimate, not a guarantee that every model or runtime configuration will fit. NVIDIA’s memory troubleshooting guide treats weights, KV cache, activations, and overhead as separate parts of the budget.
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Find the stage where the failure occurs
Read the runtime’s startup output and error around the failed allocation. “Out of memory” is a symptom, not a diagnosis: a weight-load failure, context-cache allocation, allocator fragmentation, and graph warm-up can need different remedies. NVIDIA recommends using startup diagnostics to identify the allocation phase rather than treating all CUDA errors alike. NVIDIA’s troubleshooting guide describes these distinct cases.
| When it fails | Likely cause | First response |
|---|---|---|
| Before loading finishes | Weights at the chosen precision, or the selected hardware/profile configuration, exceed capacity. | Check model, precision, profile, and GPU placement; consider a smaller model, lower-memory supported format, or supported multi-GPU execution. |
| After weights load, during KV-cache allocation | The configured context needs more cache than remains after weights and other allocations. | Reduce context to the workload’s actual input-plus-output needs. |
| During cache allocation, with substantial reserved but unallocated PyTorch memory | Allocator fragmentation may prevent a large contiguous allocation despite apparently sufficient aggregate free memory. | Check the runtime’s allocator guidance; NVIDIA documents an allocator setting for this PyTorch situation. |
| During graph capture or warm-up | There may not be enough headroom for initialization or graph allocations. | Use the runtime-specific diagnostics to test a lower cache budget or disabled CUDA graphs, if supported. |
| Only with parallel requests or several loaded models | Combined context, concurrency, and resident models exceed capacity. | Stop idle models, reduce request parallelism, or lower context. |
| Only for one model or runtime | Unsupported hardware/profile or a backend-specific issue may be involved, rather than a simple capacity shortage. | Check the model and profile’s runtime support and inspect logs before changing hardware. |
Fix the problem in order
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Record the configuration and error
Note the model, weight precision or quantization, context length, number of parallel requests and loaded models, GPU and free VRAM, runtime version, and the relevant log lines. In Ollama, run
ollama psto see loaded model size, processor placement, and context. NVIDIA NIM prints memory diagnostics at INFO or DEBUG log levels. Ollama’s FAQ and NVIDIA’s guide describe these diagnostics.Rank #2
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Reduce context to what the task needs
Context includes the prompt and generated tokens, so leave enough room for both, but do not allocate a much larger window than the task requires. In Ollama, adjust context in the application settings or with
OLLAMA_CONTEXT_LENGTH; in an interactiveollama runsession, use/set parameter num_ctx. In llama.cpp, set--ctx-sizeor-c. NVIDIA’s DGX Spark playbook gives lowering the value, for example to 4096, as a startup-OOM remedy; that is a configuration example, not a universal recommendation. Ollama context documentation · NVIDIA DGX Spark llama.cpp playbookPerformanceWindows Errors? Fix Them Before They SpreadDriversOutdated Drivers Are Slowing You DownPerformancePC Slower Than It Used to Be?Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy. -
Reduce concurrent allocations
Stop an idle Ollama model with
ollama stop <model>, reduce parallel request count, or avoid keeping multiple models loaded when they do not need to serve at once. Ollama models can remain loaded for a default period; its FAQ documents parallel memory scaling with context and concurrency. Ollama FAQRank #3
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Use smaller or lower-memory weights if loading fails
Choose a smaller parameter-count model or a lower-precision/quantized format supported by the model and runtime. Lower-memory weights may alter output quality, speed, or hardware support, so compare the actual model-format options rather than assuming all quantizations behave alike. NVIDIA notes that hardware support for a precision can affect performance. NVIDIA memory troubleshooting guide
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Reduce KV-cache use where supported
Ollama documents Flash Attention as a way to significantly reduce memory use as context grows, and documents quantized K/V cache options when Flash Attention is enabled. Its FAQ estimates that
q8_0uses about half the memory off16with very small precision loss;q4_0uses about one quarter, with small-to-medium precision loss that may be more noticeable at higher context. These are Ollama’s estimates, not guaranteed results for every model or task. Ollama FAQRank #4
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Investigate allocator and warm-up failures separately
For the described PyTorch fragmentation case, NVIDIA documents
PYTORCH_ALLOC_CONF=expandable_segments:Trueas a possible mitigation. This changes allocator behavior; it does not add physical memory, and compatibility should be checked where CUDA allocations are shared. For NIM/vLLM graph-capture or warm-up shortages, NVIDIA describes reducing the KV-cache budget or disabling CUDA graphs as diagnostic options. Such graph changes can reduce throughput, and the example settings in that guide are not universal values. NVIDIA troubleshooting guideSpecial offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy. -
Consider offload or hardware only after checking placement and budget
CPU offload may make a model runnable when GPU memory is insufficient, but can reduce performance. Use
ollama psto inspect placement; Ollama’s context guide advises avoiding CPU offload for performance where possible. If the model weights and required runtime allocations still cannot fit, more VRAM or supported multi-GPU execution may be necessary. Confirm the exact model, precision, context, and other memory users before choosing hardware. Ollama context documentation · NVIDIA troubleshooting guide
How to compare configurations or upgrades
Compare the whole workload, not just parameter count or a GPU’s advertised VRAM. A useful comparison includes:
- Memory required by the chosen model at its actual weight precision or quantization.
- Usable context for the prompt and output, plus any cache-format choices.
- Quality and speed trade-offs from lower precision, quantized cache, or CPU offload.
- Supported GPU count, model placement, and runtime/backend support.
- Memory for activations, runtime overhead, adapters, other applications, and concurrent requests.
NVIDIA’s weight estimate can help establish a baseline, but its guide identifies KV cache, activations, and overhead as additional allocations. A 24 GB VRAM GPU is relevant only if the complete configuration fits; advertised VRAM alone does not establish that it will. NVIDIA memory troubleshooting guide
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