linuxserver/lm-studio pulls statistics

The LinuxServer.io team brings you another container release featuring:

• regular and timely application updates • easy user mappings (PGID, PUID) • custom base image with s6 overlay • weekly base OS updates with common layers across the entire LinuxServer.io ecosystem to minimise space usage, down time and bandwidth • regular security updates

Find us at:

• Blog - all the things you can do with our containers including How-To guides, opinions and much more! • Discord - realtime support / chat with the community and the team. • Discussions - post on our community forum. • GitHub - view the source for all of our repositories. • Open Collective - please consider helping us by either donating or contributing to our budget

linuxserver/lm-studio

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

Supported Architectures

We utilise the docker manifest for multi-platform awareness. More information is available from docker here and our announcement here.

Simply pulling lscr.io/linuxserver/lm-studio:latest should retrieve the correct image for your arch, but you can also pull specific arch images via tags.

The architectures supported by this image are:

| Architecture | Available | Tag |

| x86-64 | ✅ | amd64-\ | | arm64 | ❌ | |

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:

Nvidia GPUs:

As of driver 595.80 with NVIDIA Container Toolkit installed:

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.

Strict reverse proxies

This image uses a self-signed certificate by default. This naturally means the scheme is https. If you are using a reverse proxy which validates certificates, you need to disable this check for the container.

This container is based on Docker Baseimage Selkies, which streams a desktop or single application to your web browser. Only the essentials are covered below, the full platform documentation lives at docs.linuxserver.io/selkies.

Start Minimal

Most support issues we see stem from large run configurations assembled from years of forum posts. These containers are designed to work with almost nothing. Start with the minimal command below, confirm it works, then add your options (volumes, env vars, GPU flags) one at a time:

Open https://localhost:3001 (note the https) and accept the self signed certificate warning. The trailing bash gives you a shell inside the container, ctrl+d exits and cleans everything up. If a bigger setup misbehaves, return to this command to isolate the problem, see the Quickstart and Troubleshooting guides.

GPU Acceleration

A GPU is optional, the CPU pipeline is fast enough for smooth sessions on modest hardware. When a GPU is mounted into the container it is automatically detected and configured for zero copy rendering and encoding, set AUTO_GPU=false to disable this behavior.

Intel / AMD (open source drivers), one flag is all it takes:

Nvidia (proprietary drivers, not available on Alpine based images):

Requires host driver 580 or newer installed from the .run file downloaded directly from Nvidia. On drivers older than 595.80 you may also need kernel parameters and a dummy plug, see GPU Acceleration before opening an issue.

Compose examples, multi GPU selection (DRINODE/DRI_NODE), Unraid notes, and a step by step debugging loop are in the GPU Acceleration guide.

Security

[!WARNING] This container provides privileged access to the host system. Do not expose it to the Internet unless you have secured it properly.

HTTPS is required. Modern browser features used for video and audio (WebCodecs) only work in a secure context. Port 3001 serves HTTPS directly, the HTTP port exists only to sit behind a reverse proxy.…

Description from the Docker Hub listing.