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Lm-studio Lm-studio

Remote DesktopAI

LM Studio can run local AI models like gpt-oss, Llama, Gemma, Qwen, and DeepSeek privately on your computer.

Image details

Pulls: 3k
Architectures: amd64
Image size: 8.2 GB
User: linuxserver
Created: Jun 26, 2026
Updated: 4 hours ago
Status: active

Configuration

Type
Container
Platform
linux
Image
linuxserver/lm-studio:latest
Ports
3000:3000/tcp3001:3001/tcp
Volumes
/config : /srv/lsio/lm-studio/config
Env vars
PUID=1000PGID=1000TZ=Etc/UTC
Restart
unless-stopped

Template by technorabilia

Notes

Portainer App Templates by Technorabilia, based on data provided by LinuxServer.io.

Ensure to create the following volume directories on the host file system, or modify the paths in the volume mapping section under the advanced options below, as needed.

mkdir -p /srv/lsio/lm-studio/config

Installation

  1. Ensure both Docker and Portainer are installed, and up-to-date
  2. Log into your Portainer web UI
  3. Under Settings → App Templates, paste the below URL
  4. Head to Home → App Templates, and the list of apps will show up
  5. Select the app you wish to deploy, fill in any config options, and hit Deploy

Template Import URL

https://raw.githubusercontent.com/Lissy93/portainer-templates/main/templates.json
Show Me demo

docker run -d --name Lm-studio \
  -p 3000:3000/tcp \
  -p 3001:3001/tcp \
  -e 'PUID=1000' \
  -e 'PGID=1000' \
  -e 'TZ=Etc/UTC' \
  -v /srv/lsio/lm-studio/config:/config \
  --restart=unless-stopped \
  linuxserver/lm-studio:latest

Save this file as compose.yaml and run docker compose up -d
Use this only as a guide.

services:
  lm-studio:
    image: linuxserver/lm-studio:latest
    ports:
      - 3000:3000/tcp
      - 3001:3001/tcp
    volumes:
      - /srv/lsio/lm-studio/config:/config
    environment:
      PUID: '1000'
      PGID: '1000'
      TZ: Etc/UTC
    restart: unless-stopped

Save this file as lm-studio-stack.yml, then from a manager node, run docker stack deploy -c lm-studio-stack.yml lm-studio

version: '3.8'
services:
  lm-studio:
    image: linuxserver/lm-studio:latest
    ports:
      - 3000:3000/tcp
      - 3001:3001/tcp
    volumes:
      - /srv/lsio/lm-studio/config:/config
    environment:
      PUID: '1000'
      PGID: '1000'
      TZ: Etc/UTC
    deploy:
      restart_policy:
        condition: any

Save each file below into a folder, then run kubectl apply -f .
Written for k3s, but works on any cluster: bind mounts use hostPath, named volumes get a persistent volume claim, and ports are exposed via a LoadBalancer service.

lm-studio-deployment.yaml

apiVersion: apps/v1
kind: Deployment
metadata:
  name: lm-studio
  labels:
    app: lm-studio
spec:
  replicas: 1
  selector:
    matchLabels:
      app: lm-studio
  template:
    metadata:
      labels:
        app: lm-studio
    spec:
      containers:
        - name: lm-studio
          image: linuxserver/lm-studio:latest
          env:
            - name: PUID
              value: '1000'
            - name: PGID
              value: '1000'
            - name: TZ
              value: Etc/UTC
          ports:
            - containerPort: 3000
              protocol: TCP
            - containerPort: 3001
              protocol: TCP
          volumeMounts:
            - name: lm-studio-data-0
              mountPath: /config
      volumes:
        - name: lm-studio-data-0
          hostPath:
            path: /srv/lsio/lm-studio/config

lm-studio-service.yaml

apiVersion: v1
kind: Service
metadata:
  name: lm-studio
spec:
  type: LoadBalancer
  selector:
    app: lm-studio
  ports:
    - name: 3000-tcp
      port: 3000
      targetPort: 3000
      protocol: TCP
    - name: 3001-tcp
      port: 3001
      targetPort: 3001
      protocol: TCP

Save this file as ~/.config/containers/systemd/lm-studio.container

[Unit]
Description=Lm-studio

[Container]
Image=linuxserver/lm-studio:latest
ContainerName=lm-studio
PublishPort=3000:3000/tcp
PublishPort=3001:3001/tcp
Environment=PUID=1000
Environment=PGID=1000
Environment=TZ=Etc/UTC
Volume=/srv/lsio/lm-studio/config:/config

[Service]
Restart=always

[Install]
WantedBy=default.target

Then reload systemd and start the service:

systemctl --user daemon-reload
systemctl --user start lm-studio

For a system-wide service, use /etc/containers/systemd/ and drop --user.

For more installation options, see the Documentation in our GitHub repo

linuxserver/lm-studio

This readme has been truncated from the full version found HERE

LM Studio can run local AI models like gpt-oss, Llama, Gemma, Qwen, and DeepSeek privately on your computer.

Application Setup

LM Studio can be accessed at:
  • https://yourhost:3001/

GPU Support

This image is Arch based and supports the following GPUs:
  • Cuda v13- This requires a 2000 series or higher Nvidia video card running the latest binary drivers, v12 is not supported.
  • Vulkan- This requires a semi modern AMD GPU.

ROCm is not supported at this time due to it's size and Vulkan option for AMD.

Minimum run commands (start here then expand to fit your needs)

AMD GPUs:
docker run --rm -it \
  --shm-size=1gb \
  --device /dev/dri \
  -e AUTO_GPU=true \
  -p 3001:3001 \
  linuxserver/lm-studio bash

Nvidia GPUs:
As of driver 595.80 with NVIDIA Container Toolkit installed:
# Host level modprobe modeset
nvidia-modprobe --modeset

docker run --rm -it \
  --shm-size=1gb \
  --device /dev/nvidia-modeset \
  --runtime nvidia \
  --gpus all \
  -e AUTO_GPU=true \
  -p 3001:3001 \
  linuxserver/lm-studio bash

AI tools included in this image

This image comes preloaded with a set of AI development and automation tools centered around LM Studio, enabling local model execution, and integration inside a KDE desktop environment.
At the core is LM Studio along with its developer ecosystem via lmstudio SDK, providing programmatic access to local models and workflows.
For AI-assisted coding and agentic workflows, the image includes:
  • Aider (via aider-install) for git-aware code editing with LLM assistance (manual configuration required)
  • Cline for agent style development inside development environments
  • Code-OSS for a desktop IDE with AI plugin support
  • OpenClaw for running autonomous AI agents that execute tasks through workflows and messaging based interaction
  • OpenCode AI for code generation and refactoring

These tools are primarily CLI and developer focused, and are designed to be configured by the user from the terminal on first launch to connect to a local LM Studio backend.
Included is a desktop launcher for LLMster to provide the backend for these tools in the desktop environment in a headless manner. This can also be launched with lms server start from the command line or from the desktop application via the taskbar icon.
Important notes:
  • LM Studio (the desktop application) and LLMster cannot be run at the same time, as they both manage overlapping runtime resources for local model access and will conflict if launched concurrently.
  • All included AI tools require initial configuration to point to the active LM Studio backend before use.

Usage

docker run -d \
  --name=lm-studio \
  -e PUID=1000 \
  -e PGID=1000 \
  -e TZ=Etc/UTC \
  -p 3000:3000 \
  -p 3001:3001 \
  -v /path/to/config:/config \
  --shm-size="1gb" `#optional` \
  --restart unless-stopped \
  lscr.io/linuxserver/lm-studio:latest

Serve Lm-studio on your own domain behind Caddy, Nginx or Traefik. Fill in your domain and copy the result. It's a starting point, some apps need their own base URL or extra headers set too.

Proxying lm-studio.example.com to http://Lm-studio:3000

Add this to your Caddyfile

lm-studio.example.com {
	reverse_proxy http://Lm-studio:3000
}

Check the logs first

Nine times out of ten the logs tell you exactly what went wrong.

  • In Portainer, go to Containers, click the container, then Logs. Or run docker logs Lm-studio
  • Exit codes help too: 137 means killed, usually out of memory. 126 or 127 means the command inside the image is broken.

Port already in use

If deployment fails with "Bind for 0.0.0.0:3000 failed: port is already allocated", something else on your server is using that port.

  • Find what's using it: sudo ss -tlnp | grep :3000
  • Stop the other service, or pick a different host port. In 3000:3000 only the left number is yours to change, the right one belongs to the app.

Running but the page won't load

The container is up but nothing appears in your browser.

  • Use your server's real IP: http://your-server-ip:3000. The 0.0.0.0 link Portainer shows isn't a real address.
  • Give it a minute after first deploy, Lm-studio can take a while to initialise.
  • Make sure your firewall allows the port, e.g. sudo ufw allow 3000

Permission denied on volumes

If the logs show "permission denied", the app can't write to its data folder on the host.

  • Fix the ownership: sudo chown -R 1000:1000 /srv/lsio/lm-studio/config
  • Or set the PUID and PGID variables (defaults 1000:1000) to match your own user, found with id $USER

Image won't pull

Test the pull directly on the host: docker pull linuxserver/lm-studio:latest

  • "manifest unknown" means the tag no longer exists. This template uses latest, so try pinning a specific version instead.
  • "toomanyrequests" is the Docker Hub rate limit. Log in with docker login to raise it.
  • "no space left on device" means a full disk. Reclaim space with docker system prune

"exec format error"

This means the image was built for a different CPU architecture than your server.

  • This image supports: amd64
  • Check yours with uname -m: x86_64 is amd64, aarch64 is arm64. Raspberry Pi and other ARM boards are the usual culprits.

Container keeps restarting

The unless-stopped restart policy relaunches the app after every crash, so the real error can scroll past.

  • Check the logs right after a restart, the last few lines before it died are the useful ones.
  • Get the exit code with docker inspect Lm-studio --format '{{.State.ExitCode}}'
  • Still stuck? Redeploy once with the restart policy set to no so the failure stays visible.

Raise an issue

Found something which isn't working as it should? Here's how to report it.

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