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Identify when the CUDA out-of-memory error occurs
Before changing settings, record the exact command, llama.cpp version or build, GGUF model and quantization, GPU model, and currently available VRAM. Note whether the error occurs as weights load, during prompt prefill, during generation, or only when a server handles traffic. Check the startup log and use --list-devices to confirm which devices the binary detects; the option is documented in the llama.cpp server README.
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If the GPU is not being used as expected, check that the build includes the relevant backend, that --n-gpu-layers is not zero or set too low, and that CUDA_VISIBLE_DEVICES is not hiding a GPU. These are among the causes identified in the llama.cpp multi-GPU guide. Other programs may also be using VRAM, so closing avoidable GPU workloads can help isolate the cause, but it is not a guaranteed fix.
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Try a smaller context setting with --ctx-size or its short form -c. The KV cache stores information used while processing and generating tokens; the multi-GPU guide says its memory use is roughly proportional to n_ctx. A smaller context therefore reduces cache demand, at the cost of limiting how much text the model can handle in a conversation or prompt.
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This is the first mitigation listed for “CUDA OOM at startup or during prefill in --split-mode tensor” in the guide. The setting that fits depends on the model, quantization, available VRAM, and runtime configuration; there is no single context value that works for every GGUF model and GPU.
For llama-server, lower parallel request capacity
If the error occurs while serving multiple requests, reduce --parallel (also written -np) after trying a smaller context. The guide explains that the server allocates a KV-cache slot for each concurrent sequence, so fewer simultaneous sequences can reduce cache demand. This limits serving concurrency; it does not reduce the model weights’ memory requirement.
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Reduce the number of GPU-offloaded layers
If context and, where applicable, parallelism reductions are not enough, lower --n-gpu-layers (or -ngl). The server README describes it as the maximum number of model layers stored in VRAM. Layers not offloaded run on the CPU, which can allow a model to load when it does not fit fully in GPU memory, but inference may become substantially slower.
The server README also documents --n-gpu-layers auto and all, and an auto-fit option, --fit, that adjusts unset arguments to fit device memory. Auto-fit is enabled by default in the documented server options, but behavior and available values can change between releases. Check the documentation for the exact llama.cpp build you run rather than assuming current master options match an installed version.
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When using more than one GPU, check the split mode
llama.cpp documents several ways to distribute work across GPUs. Select a mode with --split-mode; the server README and multi-GPU guide describe these options:
| Mode | How work is distributed | Important qualification |
|---|---|---|
none |
Uses one GPU. | Does not distribute work across multiple GPUs. |
layer |
Spreads layers and KV cache across GPUs. | The guide documents this as the default split mode. |
row |
Divides weights by rows. | Suitability depends on the model and build. |
tensor |
Splits weights and KV cache across GPUs. | Experimental and subject to architecture and KV-cache restrictions. |
Use --tensor-split to set comma-separated relative proportions for the selected devices. For example, 3,1 expresses a three-to-one split; it does not guarantee a particular model or context will fit.
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Tensor split has additional constraints
According to the multi-GPU guide, tensor split requires flash attention and supports only non-quantized KV-cache types: f32, f16, or bf16. Quantized KV cache is not supported in this mode and results in an error. The guide also lists model architecture families for which tensor split is not implemented; check that list for your architecture before choosing the mode. If it is unsupported, use the documented layer split where appropriate.
Auto-fit is not supported in tensor split mode. If using that mode and memory is insufficient, manually reduce settings such as context size instead of expecting auto-fit to resolve the failure.
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Choose the remedy that fits the symptom
- Failure during load or prefill: confirm device visibility and build support, then try reducing context. If using tensor split, verify its constraints.
- Failure under concurrent server traffic: reduce
--parallelto lower the number of simultaneous KV-cache slots. - Weights still do not fit: reduce GPU-offloaded layers, accepting slower CPU execution for the layers that remain offloaded to the CPU.
- Multiple-GPU behavior is unexpectedly slow or unstable: check split-mode compatibility, build support, and interconnect behavior. The guide notes that missing NCCL lowers multi-GPU performance in tensor mode. CUDA peer-to-peer is opt-in and may be unstable with some motherboard and BIOS configurations; if problems begin after enabling it, unset
GGML_CUDA_P2P.
The documentation describes how these settings affect memory but does not give a universal VRAM requirement or a quantified savings percentage. Fit depends on the particular model and quantization, context length, concurrent sequences, devices, and llama.cpp build.
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