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A local AI model runs out of memory when its weights and runtime allocations together exceed available system RAM or GPU memory. The cause depends on when the failure happens: while loading weights, allocating context-related memory, warming up the model, or handling concurrent requests. Identify that stage first; then adjust the setting or workload responsible instead of assuming you need a larger GPU.
Why model memory use exceeds the weight size
Model weights are only the baseline. A running model may also need memory for the key-value (KV) cache, activations, runtime and driver overhead, communication buffers, adapters, and other loaded requests or models. The actual requirement depends on the model, its precision, context, runtime, and workload.
NVIDIA estimates weight memory using parameter count, bytes per parameter, and tensor parallelism. For example, its NIM documentation estimates that an 8-billion-parameter model using BF16 weights needs 16 GB for weights on one GPU. NVIDIA says that example can fit on a single 24 GB GPU with room for KV cache and overhead, but that is not a guarantee for every runtime or configuration. NVIDIA’s GPU memory troubleshooting guide separates the weight estimate from other allocations that affect whether a workload fits.
Context length adds to the budget
Context length is the number of tokens the model can access in memory. Increasing it increases memory use, in part because the KV cache must accommodate the configured context. If the weights load successfully but the runtime fails while allocating the cache, a smaller context may solve the problem. Ollama’s context-length guide explains the setting and its memory effect.
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Concurrency can multiply the requirement
Parallel requests and multiple loaded models compete for the same available memory. Ollama documents that the memory requirement for parallel requests scales with OLLAMA_NUM_PARALLEL × OLLAMA_CONTEXT_LENGTH. A setup that works for one short request may therefore fail with more simultaneous requests or a larger context. Ollama’s FAQ describes this relationship.
Diagnose the failure by when it occurs
Read the startup or runtime logs and note the last operation before the error. A CUDA out-of-memory message alone does not identify which allocation failed. NVIDIA recommends checking the diagnostics for the failing phase; its NIM startup logs include memory information at INFO or DEBUG levels. NVIDIA’s troubleshooting documentation covers these failure patterns.
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| When it fails | Likely cause | First response |
|---|---|---|
| Before the model finishes loading | Weights at the selected precision do not fit, or the selected profile or parallelism configuration is unsupported or incorrect. | Check hardware and profile support; try a smaller model or lower-memory weight format, or supported multi-GPU execution. |
| After weights load, during KV-cache allocation | The configured context requires more cache memory than remains after weights and other allocations. | Reduce context to the workload’s actual need. This will not help if weights, adapters, or multimodal allocations already use all available memory. |
| During a PyTorch allocation despite reserved but unallocated memory | Fragmentation may prevent a large contiguous allocation even if aggregate free memory appears adequate. | For the PyTorch situation described by NVIDIA, consider PYTORCH_ALLOC_CONF=expandable_segments:True. It changes allocator behavior, not physical capacity; check compatibility, particularly where CUDA allocations are shared. |
| During graph capture or warm-up | There may not be enough headroom for the warm-up or graph allocation. | In the NIM/vLLM context documented by NVIDIA, reducing the KV-cache budget or disabling CUDA graphs can help diagnose the issue. The example settings are not universal, and disabling graphs can reduce throughput. |
| Only with multiple requests or loaded models | Concurrency or other loaded models consume the remaining memory. | Stop idle models, reduce parallel requests, or lower context. |
| Only with one runtime or model | The backend or selected profile may be unsupported or defective, rather than limited by raw hardware capacity. | Verify runtime and model support and inspect logs before treating memory tuning as the solution. |
The fragmentation and graph-capture cases are described in NVIDIA’s NIM troubleshooting guide; its configuration examples apply to that documented environment, not automatically to every local runtime.
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- Record the configuration and failure stage. Note the model, precision or quantization, context length, number of parallel requests and loaded models, GPU and available VRAM, runtime and version, and relevant log lines. In Ollama, run
ollama psto see loaded model size, processor placement, and context. NVIDIA NIM prints startup memory diagnostics at INFO or DEBUG log levels. - Lower context to what the task needs. In Ollama, set context in the app settings or through
OLLAMA_CONTEXT_LENGTH; during anollama runsession, use/set parameter num_ctx. In llama.cpp, set--ctx-sizeor-c. NVIDIA’s DGX Spark playbook gives 4096 as an example of a lower startup context setting, not a universal recommendation. Ollama’s context-length documentation and NVIDIA’s llama.cpp playbook describe these options. - Reduce concurrent memory use. Stop an idle Ollama model with
ollama stop <model>, lower parallel request count, or avoid loading models simultaneously when they do not need to be resident together. Ollama models can remain loaded for a default period, so an apparently idle model may still be using memory. Its FAQ explains model residency and concurrency settings. - Use smaller or lower-memory weights if loading fails. Try a smaller parameter-count model or a supported quantized model or lower-precision profile. These reduce weight memory, but can affect output quality, speed, and hardware support. NVIDIA’s memory guide provides precision-specific estimates and notes that hardware support can affect performance.
- Reduce KV-cache memory where supported. Ollama says Flash Attention can significantly reduce memory use as context grows; with Flash Attention enabled, its FAQ documents quantized K/V cache options. Ollama estimates that
q8_0uses about half the memory off16with a very small precision loss, whileq4_0uses about one quarter with a small-to-medium loss that may be more noticeable at higher context. These are documented estimates, not guaranteed outcomes for every model or task. See the Ollama FAQ. - Consider CPU offload or more hardware only after checking placement and fit. Use
ollama psto inspect where the model is running. CPU offload can let a model run when GPU memory is insufficient, but may reduce performance; Ollama advises avoiding it where possible for performance. If the chosen model and required allocations still cannot fit, more VRAM or supported multi-GPU execution may be necessary.
How to compare model and hardware options
Compare configurations using the memory requirement at the selected precision, usable context, expected output quality and speed, and supported hardware and backend. For a hardware upgrade, account for available VRAM, the model and precision, supported GPU count and execution backend, and all other workload allocations. Advertised VRAM or parameter count alone does not establish that a setup will fit; NVIDIA’s estimates treat weights separately from KV cache, activations, and overhead.
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Before buying hardware, first confirm the exact model, precision, context, and other memory consumers in your workload. NVIDIA’s 8-billion-parameter BF16 example supports the point that a 24 GB GPU can accommodate that particular weight estimate with additional room; it is not a recommendation for a specific GPU or a promise that every 8B configuration will fit.
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