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LocalAI/docs/content/getting-started/containers.md
mudler's LocalAI [bot] 0eb8a1188d fix(worker): give the worker a real health endpoint and a mode-aware HEALTHCHECK (#10999) 
fix(worker): give the worker a real health endpoint (#10987)

The image bakes in a single HEALTHCHECK that curls
http://localhost:8080/readyz, but the same image also runs `local-ai
worker`, which serves HTTP on the gRPC base port minus one and never
binds 8080. Every worker container was therefore permanently
`unhealthy` (43 consecutive failures observed on a production node),
which is worse than having no healthcheck: a genuinely broken worker and
a perfectly good one both report `unhealthy`, so the signal carries no
information and orchestration that keys on it misbehaves.

The worker already served /readyz on that port via the file-transfer
server, but as a constant 200 — it only proved the listener was bound,
which is precisely the failure mode at issue. Readiness now tracks the
live NATS connection: all of a worker's actual work (backend lifecycle
events, inference dispatch, file staging) arrives over NATS, so a worker
whose link is dead is up and useless. Registration is already implied,
since the server only starts after registration succeeds.

This reports something the controller cannot already see. The node
registry's status/last_heartbeat is fed by an HTTP heartbeat to the
frontend, a different network path from NATS — a worker can keep
heartbeating while its NATS connection is dead and still look healthy in
the registry. /healthz stays a constant 200: liveness must not follow
readiness, or a NATS blip becomes a cluster-wide restart storm.

The HEALTHCHECK is now a script that derives its endpoint from the mode
the container is actually running plus the env vars that configure the
bind address, so a frontend moved off 8080 with LOCALAI_ADDRESS (broken
the same way) and a worker on a non-default base port are both probed
correctly. Modes with no HTTP surface (agent-worker, one-shot commands)
report healthy rather than false-unhealthy. HEALTHCHECK_ENDPOINT remains
as an explicit override, so the workaround shipped in
docker-compose.distributed.yaml keeps working; both overrides in that
file are now unnecessary and have been removed.

Also fixes the latent --start-period gap. Since #10949 a frontend's
startup preload materializes HuggingFace artifacts before the HTTP
server binds (31 GB observed on a live cluster), so a healthy replica
can legitimately fail probes for a long time. --start-period is Docker's
knob for exactly this: failures inside it leave the container `starting`
instead of burning retries, and it ends early on the first success, so a
generous 60m costs a fast-starting container nothing. --timeout drops
from 10m to 10s — it is a per-probe deadline, and a localhost curl that
has not answered in 10s is itself the fault being detected.


Assisted-by: Claude Code:claude-opus-4-8[1m] [Read] [Edit] [Bash]

Signed-off-by: Ettore Di Giacinto <mudler@localai.io>
Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
2026-07-20 23:07:27 +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

To persist models and data, mount volumes:

docker run -ti --name local-ai -p 8080:8080 \
  -v $PWD/models:/models \
  -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/data:/data \
  localai/localai:latest

Or use named volumes:

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