How to Run Ollama on CPU Only with Docker Compose

Run Ollama without a GPU by using the official ollama/ollama image, a persistent /root/.ollama volume, and port 11434. CPU-only operation is achieved by omission: do not configure GPU reservations, NVIDIA runtime flags, AMD device mappings, or the rocm image tag.

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Save the Compose file below as docker-compose.yml, start it with docker compose up -d, and launch a model with docker compose exec ollama ollama run llama3.2.

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What CPU-only Ollama in Docker Compose means

Ollama can serve models entirely on the host CPU. Docker does not need a special CPU runtime: the container uses ordinary container execution unless you explicitly grant it accelerator access.

For a CPU-only deployment, the important configuration decisions are:

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• Use the standard ollama/ollama image rather than an accelerator-specific image tag.
• Expose Ollama’s service port, 11434.
• Mount a named volume at /root/.ollama so downloaded models survive container replacement.
• Do not add GPU reservations, --gpus options, NVIDIA runtime settings, /dev/kfd, /dev/dri, or the rocm tag.

NVIDIA access requires an explicit --gpus=all-style grant, while AMD acceleration uses the ROCm image and device mappings. Leaving those features out is what keeps this Compose service on the CPU path.

Prerequisites before creating the service

You need Docker Engine or Docker Desktop with the Compose v2 command available as docker compose. The Docker daemon must be running, and the host needs enough disk space for the models you intend to download. Model size, context length, and CPU instruction support determine practical resource needs; there is no single CPU or memory requirement that applies to every model.

Choose where the service should be reachable before writing the file:

• For a service used only by programs on the same machine, bind the port to 127.0.0.1.
• For access from another machine, bind to an address reachable on your network and apply your own firewall and access controls. The basic example below does not add authentication.

On Docker Desktop, platform behavior differs from native Linux. In particular, the official FAQ notes that GPU acceleration is unavailable for Docker Desktop on macOS; a CPU-only Compose service is therefore the appropriate mode there.

Minimal CPU-only docker-compose.yml

Create a directory for the deployment and put this file in it:

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama:/root/.ollama
    restart: unless-stopped

volumes:
  ollama:

This defines one service named ollama. The container listens on port 11434, and Docker publishes that port on the host. The named volume is mounted at Ollama’s model directory. restart: unless-stopped allows Docker to restart the service after a routine failure or host reboot, unless you deliberately stopped it.

Localhost-only port binding

If only local applications should call the API, use an explicit loopback binding:

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ports:
  - "127.0.0.1:11434:11434"

Replace the ports section in the minimal file with this version. Clients on other machines will not be able to reach the published port through the host’s network interfaces.

Why there are no GPU keys

A CPU-only file should not contain a deploy.resources.reservations.devices entry, an NVIDIA runtime declaration, a gpus property, or device mappings for accelerator hardware. It should also use ollama/ollama, not a rocm-tagged image. Adding any of those settings changes the deployment requirements and can make Docker search for hardware that is not present.

Start the container and download a model

1. Open a terminal in the directory containing docker-compose.yml.

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2. Start the service in the background:

   docker compose up -d

3. Confirm that Compose created the container:

   docker compose ps

4. Run a model from inside the container. The first run downloads the model into the named volume:

   docker compose exec ollama ollama run llama3.2

The exec command invokes the Ollama CLI in the already-running container. Replace llama3.2 with another model name supported by your Ollama installation. A download can take substantial time and disk space; subsequent runs use the copy stored in /root/.ollama.

Calling the HTTP API from the host

Once the service is running, the Ollama endpoint is http://localhost:11434 when you use the standard port mapping. A simple health and model-list request is:

curl http://localhost:11434/api/tags

With the localhost-only mapping, this URL remains the same for programs on the Docker host. Remote clients need a deliberately configured non-loopback binding and must use the host’s reachable address instead.

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How to confirm that Ollama is using only the CPU

CPU mode is selected by the absence of accelerator configuration. Inspect the Compose file and verify all of the following:

• The image is ollama/ollama:latest (or the untagged standard image), not a ROCm-specific tag.
• No GPU reservation or --gpus equivalent is present.
• No NVIDIA runtime setting is present.
• No /dev/kfd or /dev/dri device is passed into the container.

To inspect what is actually running, use:

docker compose ps
docker compose logs -f ollama

Container output is written to standard output and standard error, so docker compose logs ollama is the first diagnostic command when startup or model loading behaves unexpectedly. If logs indicate that an accelerator was selected despite a CPU-only file, remove any inherited device mappings or accelerator image tags and recreate the container.

Forcing a CPU library when autodetection needs help

Ollama normally autodetects an appropriate execution library. The troubleshooting documentation also describes an explicit CPU override using OLLAMA_LLM_LIBRARY=cpu_avx2. Add it only when that runner is available in the image and the host CPU supports the required instruction set:

services:
  ollama:
    image: ollama/ollama:latest
    container_name: ollama
    ports:
      - "127.0.0.1:11434:11434"
    volumes:
      - ollama:/root/.ollama
    environment:
      - OLLAMA_LLM_LIBRARY=cpu_avx2
    restart: unless-stopped

volumes:
  ollama:

This variable is an override for autodetection, not a universal compatibility switch. If the image does not include the runner or the processor lacks the required AVX2 support, remove the override and let Ollama select automatically.

Persistence: keep models when containers change

The named volume declaration maps Docker volume ollama to /root/.ollama inside the container. Downloaded models are stored there, so a container recreation does not require downloading every model again as long as the same volume and mount path remain in the Compose file.

For example, this command recreates the container while retaining the volume:

docker compose down
docker compose up -d --force-recreate

Do not remove the named volume when you intend to keep the models. A volume deletion is separate from removing the container and discards the downloaded data. If models appear to download again after a recreation, compare the current volume name and the container mount path with the original file.

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Container lifecycle behavior

docker compose stop stops the service without removing the container or volume. docker compose down removes the Compose-managed container and network but leaves the named volume unless you explicitly request volume removal. Starting again with docker compose up -d reconnects the existing model volume.

The unless-stopped restart policy handles ordinary daemon restarts and host reboots. It does not replace monitoring, and it does not make a failed model download resume if the underlying storage or network problem remains.

Verification checklist after deployment

1. Check service state:

   docker compose ps

   The ollama service should be listed as running.

2. Watch startup output:

   docker compose logs -f ollama

   Use Ctrl+C to leave the log stream; leaving the stream does not stop the container.

3. Check the API endpoint:

   curl http://localhost:11434/api/tags

4. Start a model in the container:

   docker compose exec ollama ollama run llama3.2

5. After downloading, repeat the API request to confirm that the model list is available without starting a second container.

Troubleshooting common failures

Docker Desktop and operating-system scope

The Compose syntax is portable, but hardware integration is not identical on every Docker platform. Native Linux hosts can expose supported accelerators when explicitly configured; those mappings are intentionally absent from this CPU-only setup. Docker Desktop abstracts the Linux VM used for containers, and its accelerator capabilities vary by operating system.

On macOS, the official FAQ states that GPU acceleration is not available for Docker Desktop. Running the standard image without GPU settings therefore matches the platform’s supported path. On any platform, do not copy Linux GPU device mappings into a CPU-only file merely because an example for another deployment includes them.

CPU-only Compose versus a native Ollama installation

Both approaches run the same Ollama service and expose the same port. The difference is operational rather than a guaranteed performance advantage. CPU throughput depends on the model and host hardware; authoritative documentation does not provide a universal benchmark that can predict your result.

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Choose Compose when you value a declarative file, isolated dependencies, and a clearly managed model volume. Choose a native installation when adding Docker is unnecessary for your environment and you prefer the host’s normal service tooling.

Safe maintenance workflow

1. Keep the Compose file as the source of truth for the image, port, volume, restart policy, and any deliberate environment override.

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2. Before changing the file, record the current service and volume names with docker compose ps and your Compose configuration.

3. Apply configuration changes by recreating the service:

   docker compose down
   docker compose up -d --force-recreate

4. Immediately run docker compose logs ollama and curl http://localhost:11434/api/tags to verify startup and API reachability.

5. Run a known model with docker compose exec ollama ollama run llama3.2 if you need to confirm that model storage and inference still work.

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Keep the volume declaration unchanged during routine image or container changes. Treat volume removal as a destructive operation because it removes the locally downloaded models.

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