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A GPU-layer setting labeled “Max” is a request, not confirmation that every layer is in VRAM. Check the model loader’s startup report for the number actually offloaded: a result of 54/65 means that runtime reported 54 layers placed on the GPU for that load. It does not, by itself, reveal why the other layers were not. The loader, its settings, available VRAM, and load-time conditions are needed to diagnose the cause.
What does “Max” mean when only 54 of 65 layers load?
In llama.cpp, the GPU-layer option specifies the maximum number of layers to store in VRAM. The documented options include a number, auto, and all; none makes the setting alone proof of actual placement. The load report is the evidence to check. See the llama.cpp server README.
If the report says 54 of 65, that is a partial GPU offload: 54 layers were reported as offloaded, while the remaining layers are not thereby shown to be in VRAM. It does not identify whether memory capacity, automatic fitting, split configuration, or another runtime-specific detail explains the result. Do not treat “the model runs on GPU” as an all-or-nothing state; a model can use both GPU and CPU.
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How can I check whether my model is running on GPU or CPU?
- Identify the runtime and version. Record which loader produced the message. A command-line option in llama.cpp, a Python wrapper, and an application’s “Max” control may have different defaults or reporting conventions.
- Read the startup or model-load report. Note the requested layer setting and the actual loaded or offloaded count. The count, not the label “Max,” is the useful confirmation of placement for that load.
- Capture the conditions at load time. Record the GPU model, available VRAM, model file and quantization, context size, batch settings, and other GPU workloads. Without these details, a partial count cannot establish a particular cause.
- Inspect the runtime’s relevant configuration. For llama.cpp, check the GPU-layer option, automatic fitting, and—if using multiple GPUs—the split settings. For llama-cpp-python, inspect
n_gpu_layers, split configuration, context and batch settings, and verbose load output. - Change one setting at a time and reload. Compare the new report with the original while keeping other conditions as consistent as possible. This helps show whether that change affected placement; it does not establish a general cause for other setups.
Which GPU-offload controls should I check?
Control names and behavior depend on the runtime. The settings below describe the documented controls in llama.cpp and llama-cpp-python; they should not be assumed to apply to a different application’s “Max” option.
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| Runtime | Requested layer control | Other relevant controls | What confirms placement |
|---|---|---|---|
| llama.cpp | -ngl, --gpu-layers, or --n-gpu-layers; documented values include a number, auto, and all. |
--fit can automatically adjust unset arguments to fit device memory; --fit-target sets a per-device memory margin; --fit-ctx sets a minimum context size used by fitting. With multiple GPUs, split mode and tensor split configure distribution. |
The startup/load report’s actual offloaded count. The options are documented in the server README. |
| llama-cpp-python | n_gpu_layers specifies the number of layers to put on GPU; the rest are on CPU. A value of -1 requests all layers. |
Its server configuration also exposes split mode, main GPU, tensor split, context, batch, and related settings. | Inspect the configuration and actual model-load output. See the API reference and server documentation. |
Why can the actual count be lower than the requested count?
The available documentation establishes that the layer option is a maximum or requested count, and that some llama.cpp fitting controls can adjust unset arguments to fit device memory. It does not establish why a particular machine reported 54/65. That number alone cannot distinguish among memory conditions, fitting behavior, multi-GPU distribution, or differences in another application’s loader.
To narrow it down, use the load-time details together: available VRAM, model and quantization, context and batch settings, concurrent GPU use, runtime version, and split or fitting configuration. Make controlled changes and compare load reports rather than inferring a cause from the “Max” label.
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What details are needed to diagnose a specific 54/65 result?
- The loader or application name and version, plus the exact GPU-layer setting.
- The full startup/load output showing both the request and reported placement.
- GPU model and free VRAM at the time of loading.
- Model file and quantization, context size, and batch settings.
- Any other GPU workloads, automatic memory fitting, or multi-GPU split settings.
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