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cron/issue-10590
LocalAI/docs/content/getting-started/containers.md
localai-org-maint-bot 68a0460681 fix(oci): install backends on filesystems without symlinks (#11166) 
* fix(backends): fall back to copying links when the filesystem rejects symlinks (#10890)

Backend installation extracts the OCI image tar via containerd's
archive.Apply, which calls os.Symlink directly. On filesystems that do
not support symlinks (notably CIFS/SMB mounts, commonly used to back the
/backends volume) the syscall fails with "operation not supported" and
the whole install aborts, leaving an empty backend directory. The CUDA
llama.cpp image trips this on the libcublas.so -> libcublas.so.12.x
symlink.

When archive.Apply fails with a link-unsupported error, reset the
staging directory and re-extract with a pure-Go walker that still
attempts real symlinks/hardlinks first and degrades to copying the link
target's contents in place when the filesystem rejects them.
mutate.Extract already flattened the layers, so the tar carries no
whiteouts to interpret. Link copies are deferred to a second pass so
forward references resolve.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]

* fix(oci): check deferred Close in copyFilePreservingMode (errcheck)

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]

* fix(oci): reject path-traversal tar entries in the link-copy fallback

safeJoin sanitized "../.." entries by clamping them under root instead of
rejecting them, so a malicious entry was silently redirected rather than
refused. Join without the leading-slash trick and reject any entry whose
cleaned path resolves outside root; absolute link targets are still mapped
under root (image-root relative) rather than escaping.

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]

* fix(oci): silence gosec on the validated link-copy file ops

Use hdr.FileInfo().Mode() instead of converting the int64 tar mode to
os.FileMode (removes two G115 overflow findings), and annotate the tar
extraction file operations with justified #nosec comments: every path is
validated by safeJoin against the extraction root before use (G304/G305).

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Assisted-by: Claude:opus-4.8 [Claude Code]

* fix(oci): build extraction image from downloaded layers

Avoid appending downloaded layers to the original remote-backed image, which duplicates the layer stack and reopens the source during extraction. Building from an empty image preserves the flattened whiteout semantics while keeping extraction local.

Assisted-by: Codex:gpt-5

* fix(oci): materialize chained links in dependency order

Retry deferred link copies until their targets exist so soname chains work on filesystems without symlink support. Document that copied links can increase backend storage usage on CIFS and SMB mounts.

Assisted-by: Codex:gpt-5

---------

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: localai-org-maint-bot <306269227+localai-org-maint-bot@users.noreply.github.com>
2026-07-30 16:23:42 +02:00

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Containers Install and use LocalAI with container engines (Docker, Podman) 8 /installation/containers/
/basics/container/

LocalAI supports Docker, Podman, and other OCI-compatible container engines. This guide covers the common aspects of running LocalAI in containers.

Prerequisites

Before you begin, ensure you have a container engine installed:

Quick Start

The fastest way to get started is with the CPU image:

docker run -p 8080:8080 --name local-ai -ti localai/localai:latest
# Or with Podman:
podman run -p 8080:8080 --name local-ai -ti localai/localai:latest

This will:

  • Start LocalAI (you'll need to install models separately)
  • Make the API available at http://localhost:8080

Image Types

LocalAI provides several image types to suit different needs. These images work with both Docker and Podman.

Standard Images

Standard images don't include pre-configured models. Use these if you want to configure models manually.

CPU Image

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 localai/localai:latest

GPU Images

NVIDIA CUDA 13:

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 --device nvidia.com/gpu=all localai/localai:latest-gpu-nvidia-cuda-13

NVIDIA CUDA 12:

docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 --device nvidia.com/gpu=all localai/localai:latest-gpu-nvidia-cuda-12

AMD GPU (ROCm):

docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 --device rocm.com/gpu=all localai/localai:latest-gpu-hipblas

Intel GPU:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-intel
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 --device gpu.intel.com/all localai/localai:latest-gpu-intel

Vulkan:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan

NVIDIA Jetson (L4T ARM64):

CUDA 12 (for Nvidia AGX Orin and similar platforms):

docker run -ti --name local-ai -p 8080:8080 --runtime nvidia --gpus all localai/localai:latest-nvidia-l4t-arm64

CUDA 13 (for Nvidia DGX Spark):

docker run -ti --name local-ai -p 8080:8080 --runtime nvidia --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13

Using Compose

For a more manageable setup, especially with persistent volumes, use Docker Compose or Podman Compose:

Using CDI (Container Device Interface) - Recommended for NVIDIA Container Toolkit 1.14+

The CDI approach is recommended for newer versions of the NVIDIA Container Toolkit (1.14 and later). It provides better compatibility and is the future-proof method:

version: "3.9"
services:
  api:
    image: localai/localai:latest-gpu-nvidia-cuda-12
    # For CUDA 13, use: localai/localai:latest-gpu-nvidia-cuda-13
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"]
      # start_period, not timeout, is the knob for a slow first boot: startup
      # preload can download tens of GB before the API binds, and failures
      # inside the start period leave the container `starting` rather than
      # marking it unhealthy. timeout is a per-probe deadline.
      start_period: 60m
      interval: 1m
      timeout: 10s
      retries: 3
    ports:
      - 8080:8080
    environment:
      - DEBUG=false
    volumes:
      - ./models:/models:cached
    # CDI driver configuration (recommended for NVIDIA Container Toolkit 1.14+)
    # This uses the nvidia.com/gpu resource API
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia.com/gpu
              count: all
              capabilities: [gpu]

Save this as compose.yaml and run:

docker compose up -d
# Or with Podman:
podman-compose up -d

Using Legacy NVIDIA Driver - For Older NVIDIA Container Toolkit

If you are using an older version of the NVIDIA Container Toolkit (before 1.14), or need backward compatibility, use the legacy approach:

version: "3.9"
services:
  api:
    image: localai/localai:latest-gpu-nvidia-cuda-12
    # For CUDA 13, use: localai/localai:latest-gpu-nvidia-cuda-13
    healthcheck:
      test: ["CMD", "curl", "-f", "http://localhost:8080/readyz"]
      # start_period, not timeout, is the knob for a slow first boot: startup
      # preload can download tens of GB before the API binds, and failures
      # inside the start period leave the container `starting` rather than
      # marking it unhealthy. timeout is a per-probe deadline.
      start_period: 60m
      interval: 1m
      timeout: 10s
      retries: 3
    ports:
      - 8080:8080
    environment:
      - DEBUG=false
    volumes:
      - ./models:/models:cached
    # Legacy NVIDIA driver configuration (for older NVIDIA Container Toolkit)
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: 1
              capabilities: [gpu]

Persistent Storage

The container exposes the following volumes:

Volume Description CLI Flag Environment Variable
/models Model files used for inferencing --models-path $LOCALAI_MODELS_PATH
/backends Custom backends for inferencing --backends-path $LOCALAI_BACKENDS_PATH
/configuration Dynamic config files (api_keys.json, external_backends.json, runtime_settings.json) --localai-config-dir $LOCALAI_CONFIG_DIR
/data Persistent data (collections, agent state, tasks, jobs) --data-path $LOCALAI_DATA_PATH

{{% notice warning %}} Container files that are not stored in a volume are lost when the container is recreated during an image upgrade. Mount all four paths if you want to preserve installed models, backends, settings, and application data. {{% /notice %}}

The host paths can be anywhere on persistent storage, but the container paths must be exactly /models, /backends, /configuration, and /data. In UnRAID and other container-template UIs, create one path mapping for each row in the table above.

Backend OCI images contain symbolic links. When /backends is stored on a filesystem that cannot create links, such as some CIFS/SMB mounts, LocalAI materializes each link as a regular file so installation can complete. This can use more disk space than a local filesystem. Prefer a Docker or Podman named volume for /backends when possible.

To use bind mounts:

docker run -ti --name local-ai -p 8080:8080 \
  -v $PWD/models:/models \
  -v $PWD/backends:/backends \
  -v $PWD/configuration:/configuration \
  -v $PWD/data:/data \
  localai/localai:latest
# Or with Podman:
podman run -ti --name local-ai -p 8080:8080 \
  -v $PWD/models:/models \
  -v $PWD/backends:/backends \
  -v $PWD/configuration:/configuration \
  -v $PWD/data:/data \
  localai/localai:latest

Or use named volumes:

docker volume create localai-models
docker volume create localai-backends
docker volume create localai-configuration
docker volume create localai-data
docker run -ti --name local-ai -p 8080:8080 \
  -v localai-models:/models \
  -v localai-backends:/backends \
  -v localai-configuration:/configuration \
  -v localai-data:/data \
  localai/localai:latest
# Or with Podman:
podman volume create localai-models
podman volume create localai-backends
podman volume create localai-configuration
podman volume create localai-data
podman run -ti --name local-ai -p 8080:8080 \
  -v localai-models:/models \
  -v localai-backends:/backends \
  -v localai-configuration:/configuration \
  -v localai-data:/data \
  localai/localai:latest

Next Steps

After installation:

  1. Access the WebUI at http://localhost:8080
  2. Check available models: curl http://localhost:8080/v1/models
  3. Install additional models
  4. Try out examples

Troubleshooting

Container won't start

  • Check container engine is running: docker ps or podman ps
  • Check port 8080 is available: netstat -an | grep 8080 (Linux/Mac)
  • View logs: docker logs local-ai or podman logs local-ai

GPU not detected

  • Ensure Docker has GPU access: docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi
  • For Podman, pass the GPU with the --device flags shown in the GPU sections above (for example --device nvidia.com/gpu=all)
  • For NVIDIA: Install NVIDIA Container Toolkit
  • For AMD: Ensure devices are accessible: ls -la /dev/kfd /dev/dri

NVIDIA Container fails to start with "Auto-detected mode as 'legacy'" error

If you encounter this error:

Error response from daemon: failed to create task for container: failed to create shim task: OCI runtime create failed: runc create failed: unable to start container process: error during container init: error running prestart hook #0: exit status 1, stdout: , stderr: Auto-detected mode as 'legacy'
nvidia-container-cli: requirement error: invalid expression

This indicates a Docker/NVIDIA Container Toolkit configuration issue. The container runtime's prestart hook fails before LocalAI starts. This is not a LocalAI code bug.

Solutions:

  1. Use CDI mode (recommended): Update your docker-compose.yaml to use the CDI driver configuration:

    deploy:
  resources:
    reservations:
      devices:
        - driver: nvidia.com/gpu
          count: all
          capabilities: [gpu]

  2. Upgrade NVIDIA Container Toolkit: Ensure you have version 1.14 or later, which has better CDI support.

  3. Check NVIDIA Container Toolkit configuration: Run nvidia-container-cli --query-gpu to verify your installation is working correctly outside of containers.

  4. Verify Docker GPU access: Test with docker run --rm --gpus all nvidia/cuda:12.0.0-base-ubuntu22.04 nvidia-smi

Models not downloading

  • Check internet connection
  • Verify disk space: df -h
  • Check container logs for errors: docker logs local-ai or podman logs local-ai

Full image reference

The quick-start examples above use the Docker Hub image names. Every image is published to both Docker Hub and Quay. The tables below map the Docker Hub tag to its Quay equivalent for each variant. Replace {{< version >}} with a released version to pin a specific build.

{{< tabs >}} {{% tab title="Vanilla / CPU Images" %}}

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master localai/localai:master
Latest tag quay.io/go-skynet/local-ai:latest localai/localai:latest
Versioned image quay.io/go-skynet/local-ai:{{< version >}} localai/localai:{{< version >}}

{{% /tab %}}

{{% tab title="GPU Images CUDA 12" %}}

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-gpu-nvidia-cuda-12 localai/localai:master-gpu-nvidia-cuda-12
Latest tag quay.io/go-skynet/local-ai:latest-gpu-nvidia-cuda-12 localai/localai:latest-gpu-nvidia-cuda-12
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-gpu-nvidia-cuda-12 localai/localai:{{< version >}}-gpu-nvidia-cuda-12

{{% /tab %}}

{{% tab title="GPU Images CUDA 13" %}}

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-gpu-nvidia-cuda-13 localai/localai:master-gpu-nvidia-cuda-13
Latest tag quay.io/go-skynet/local-ai:latest-gpu-nvidia-cuda-13 localai/localai:latest-gpu-nvidia-cuda-13
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-gpu-nvidia-cuda-13 localai/localai:{{< version >}}-gpu-nvidia-cuda-13

{{% /tab %}}

{{% tab title="Intel GPU" %}}

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-gpu-intel localai/localai:master-gpu-intel
Latest tag quay.io/go-skynet/local-ai:latest-gpu-intel localai/localai:latest-gpu-intel
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-gpu-intel localai/localai:{{< version >}}-gpu-intel

{{% /tab %}}

{{% tab title="AMD GPU" %}}

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-gpu-hipblas localai/localai:master-gpu-hipblas
Latest tag quay.io/go-skynet/local-ai:latest-gpu-hipblas localai/localai:latest-gpu-hipblas
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-gpu-hipblas localai/localai:{{< version >}}-gpu-hipblas

{{% /tab %}}

{{% tab title="Vulkan Images" %}}

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-gpu-vulkan localai/localai:master-gpu-vulkan
Latest tag quay.io/go-skynet/local-ai:latest-gpu-vulkan localai/localai:latest-gpu-vulkan
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-gpu-vulkan localai/localai:{{< version >}}-gpu-vulkan

{{% /tab %}}

{{% tab title="Nvidia Linux for tegra (CUDA 12)" %}}

These images are compatible with Nvidia ARM64 devices with CUDA 12, such as the Jetson Nano, Jetson Xavier NX, and Jetson AGX Orin. For more information, see the [Nvidia L4T guide]({{%relref "reference/nvidia-l4t" %}}).

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-nvidia-l4t-arm64 localai/localai:master-nvidia-l4t-arm64
Latest tag quay.io/go-skynet/local-ai:latest-nvidia-l4t-arm64 localai/localai:latest-nvidia-l4t-arm64
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-nvidia-l4t-arm64 localai/localai:{{< version >}}-nvidia-l4t-arm64

{{% /tab %}}

{{% tab title="Nvidia Linux for tegra (CUDA 13)" %}}

These images are compatible with Nvidia ARM64 devices with CUDA 13, such as the Nvidia DGX Spark. For more information, see the [Nvidia L4T guide]({{%relref "reference/nvidia-l4t" %}}).

Description Quay Docker Hub
Latest images from the branch (development) quay.io/go-skynet/local-ai:master-nvidia-l4t-arm64-cuda-13 localai/localai:master-nvidia-l4t-arm64-cuda-13
Latest tag quay.io/go-skynet/local-ai:latest-nvidia-l4t-arm64-cuda-13 localai/localai:latest-nvidia-l4t-arm64-cuda-13
Versioned image quay.io/go-skynet/local-ai:{{< version >}}-nvidia-l4t-arm64-cuda-13 localai/localai:{{< version >}}-nvidia-l4t-arm64-cuda-13

{{% /tab %}}

{{< /tabs >}}

See Also

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