Local model runtime
How to use
LM Studio.
Running supported open models locally, comparing model configurations, chatting with private local data, and providing local application APIs.
LM Studio is a desktop and headless runtime for downloading, evaluating, and serving local language models. Running supported open models locally, comparing model configurations, chatting with private local data, and providing local application APIs. This guide covers the whole path in one place: official access, a first session that produces something reviewable, the checks that make output trustworthy, and the permissions worth limiting before you connect real work.
AI should make the work easier to inspect. If the workflow removes the source, the owner, or the review step, redesign the workflow.
Access & setup
Find, install, and sign in to LM Studio
Get into the official LM Studio experience with the right account and a setup you understand.
- 01
Start at https://lmstudio.ai/ and confirm the domain before entering account or payment information.
- 02
Availability: Desktop builds are available for supported macOS, Windows, and Linux systems; a headless runtime is also documented.
- 03
A supported processor and operating system, sufficient RAM and storage for selected models, and preferably compatible GPU memory for larger workloads.
- 04
Sign in with the account you intend to keep using, then review plan, data, notification, and permission settings.
- 05
Run one low-risk test task before connecting sensitive files, repositories, or workspace data.
- Use official download pages.
- Review permissions during setup.
- Keep installers and applications updated.
First session
Your first useful LM Studio session
Learn the interaction loop using a small task with a clear outcome.
- 01
Check the official system requirements and record available RAM, VRAM, storage, and the task the model must perform.
- 02
Download a licensed model and quantization sized realistically for the machine.
- 03
Load it with conservative settings and run a fixed set of representative and adversarial prompts.
- 04
Record the exact model, configuration, speed, memory use, and quality before integrating it elsewhere.
- State the outcome before the background.
- Provide the real source material.
- Review the result before expanding the task.
Quality control
Check the quality of LM Studio output
Establish that a change is safe to run in production and reversible if it is not.
- 01
Restate the intended end state and the blast radius before applying anything.
- 02
Review the generated configuration line by line against the provider's current documentation.
- 03
Apply to a non-production environment first and confirm the observed result matches the intended one.
- 04
Confirm the rollback path works by actually exercising it, not by assuming it exists.
- 05
Check cost, scaling limits, and network exposure before the change reaches production traffic.
- Test the rollback, do not assume it.
- Check what a configuration exposes to the public internet.
- Watch cost and rate limits as closely as correctness.
Privacy & permissions
Use LM Studio safely
Keep credentials, network exposure, and cost under deliberate control.
- 01
Use scoped, short-lived credentials, and never paste production secrets into a prompt or config file.
- 02
Confirm what each change exposes publicly before applying it, especially storage, databases, and admin endpoints.
- 03
Separate environments so a mistake in development cannot reach production data.
- 04
Set billing alerts and hard quotas before enabling autoscaling or usage-based services.
- 05
Review audit logs and revoke access for integrations that are no longer in use.
- Local inference does not make model output trustworthy, and the API server has no authentication by default. Review model licenses and cards, keep the server on localhost unless necessary, require a token before network exposure, and tightly control CORS and MCP access.
- Follow your organisation's approved-use policy.
- Never treat fluent output as authorization to act.
Core workflows
Step-by-step ways to use LM Studio for the work it does best.
Each workflow is a separate guide with its own steps and review checkpoints.
Match a downloadable model to the machine and evaluate it locally with a repeatable test set.
↗ 02 WorkflowServe an LM Studio model through a secured local APIExpose a local model to an application while controlling authentication, network reach, and tool permissions.
↗Official references
Check the current product documentation.
Features, plan limits, availability, and data controls change. These official pages are the starting points used for this guide.