# Ollama is not using the GPU: five separate checks

Diagnose Ollama CPU-only execution by checking exact GPU support, drivers, process visibility, device selection and memory placement with logs.

Author: Retrivium team
Updated: 2026-11-01
Canonical URL: https://retrivium.ai/journal/ollama-does-not-use-gpu/
Language: en

When Ollama is not using the GPU, separate five checks: exact hardware support, driver compatibility, device visibility to the server, accelerator selection and whether the model fits the chosen placement. A successful answer proves that inference happened, not where it happened.

## Match the exact GPU and platform

Record the complete card name, operating system, driver and Ollama version. Use the official hardware-support documentation for that combination. Similar product names can refer to different architectures, and support on one operating system does not automatically establish support on another.

Keep a small control model available. It is useful for distinguishing an unsupported or invisible device from a target artifact whose memory requirements are too large. Do not immediately reinstall the entire environment when a more specific check can isolate the cause.

## Check the server process, not just the client

The command-line client may contact an already running background service. That service can have different environment variables from the terminal where you are testing. Identify which process actually loads the model and where its startup configuration comes from.

On NVIDIA systems, compare the device with `nvidia-smi`; use the appropriate system tools on other platforms. Hardware visible to the host is not necessarily visible to a container or service. Save the server log around device discovery and model loading.

## Inspect the loaded model

After loading, run `ollama ps` and preserve the reported placement. Observe it at a defined point: after loading and during a known request. A brief activity spike is not a reliable substitute for identifying the running model and its processor allocation.

Compare the control model and target artifact using the same explicit, moderate context. If the control uses the GPU but the target does not remain fully there, investigate memory and placement before declaring the card unsupported. Include the exact tags in the report.

## Check memory and device selection

Other applications, context and simultaneous requests reduce the available operating margin. Close only your own unnecessary workloads or agree changes with the machine's other users. On multi-GPU machines, preserve device identifiers and the selected backend so the next test addresses the same hardware.

Finally, correlate the log, model placement and system observation. Mixed CPU/GPU placement may be a valid supported result rather than a malfunction. Describe what workload is actually usable, including latency and memory, instead of reporting only whether a GPU indicator briefly moved.

## Turn the check into a practical next step

Use the actual model placement and server log to identify the failing layer. A successful answer alone does not prove GPU execution.

For the documented details, consult the [primary documentation](https://docs.ollama.com/gpu); [configuration reference](https://docs.ollama.com/troubleshooting). Continue with [ollama cpu gpu split](/journal/ollama-cpu-gpu-split/) and [ollama context length setting](/journal/ollama-context-length-setting/).