---
# SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
title: "Use Local Inference with LM Studio"
sidebar-title: "Local Inference with LM Studio"
slug: "get-started/tutorials/local-inference-lmstudio"
description: "Attach a provider profile for a local LM Studio server and call its native compatible endpoints."
keywords: "Generative AI, Cybersecurity, Tutorial, LM Studio, Local Inference, Sandbox, Provider Profile"
---

This tutorial exposes an LM Studio server on the gateway host to selected
sandboxes. The provider attachment grants access; the workload calls LM
Studio's OpenAI- or Anthropic-compatible endpoint directly.

## Prerequisites

- Complete the [Quickstart](/get-started/quickstart).
- Install [LM Studio](https://lmstudio.ai/download) on the gateway host.

For a headless installation:

<Tabs>
<Tab title="Linux/macOS">

```shell
curl -fsSL https://lmstudio.ai/install.sh | bash
```

</Tab>
<Tab title="Windows">

```shell
irm https://lmstudio.ai/install.ps1 | iex
```

</Tab>
</Tabs>

Start the daemon and server:

```shell
lms daemon up
lms server start --bind 0.0.0.0
```

Load a model:

```shell
lms get qwen/qwen3.5-2b
lms load qwen/qwen3.5-2b
```

## Create the Provider Profile

Save this as `lmstudio.yaml`:

```yaml
id: lmstudio
display_name: LM Studio
description: Host-level LM Studio compatible APIs
category: inference
inference_capable: true
credentials: []
endpoints:
  - host: host.openshell.internal
    port: 1234
    protocol: rest
    access: read-write
    enforcement: enforce
binaries:
  - /usr/bin/curl
  - /usr/local/bin/curl
  - /usr/bin/python3
  - /usr/local/bin/python
  - /sandbox/.uv/python/**
  - /sandbox/.venv/**
```

```shell
openshell provider profile lint -f lmstudio.yaml
openshell provider profile import -f lmstudio.yaml
openshell provider create --name lmstudio --type lmstudio
```

The profile intentionally has no credential. LM Studio does not require one by
default, but the provider attachment still supplies its policy boundary.

## Attach and Call the Native Endpoint

OpenAI-compatible request:

```shell
openshell sandbox create \
  --name lmstudio-client \
  --provider lmstudio \
  -- \
  curl http://host.openshell.internal:1234/v1/chat/completions \
    --json '{
      "model":"qwen/qwen3.5-2b",
      "messages":[{"role":"user","content":"hello"}],
      "max_tokens":10
    }'
```

Anthropic-compatible request:

```shell
openshell sandbox exec lmstudio-client -- \
  curl http://host.openshell.internal:1234/v1/messages \
    --json '{
      "model":"qwen/qwen3.5-2b",
      "messages":[{"role":"user","content":"hello"}],
      "max_tokens":10
    }'
```

Configure SDKs with the same native base URL. Some SDKs require a non-empty
API key even when LM Studio ignores it; use a literal non-secret value such as
`unused`. Keep the actual model ID in the client request.

## Troubleshooting

- Enable **Serve on Local Network** in the LM Studio Developer tab, or use
  `lms server start --bind 0.0.0.0`.
- Use `host.openshell.internal`, not `127.0.0.1` or `localhost`, in the
  sandbox.
- Confirm the model is loaded with `lms ps`.
- Confirm the attachment with
  `openshell sandbox provider list lmstudio-client`.
- Inspect endpoint and binary policy with
  `openshell policy get lmstudio-client --full`.
- A remote gateway cannot reach an LM Studio process on your laptop without a
  tunnel or shared network path.

## Next Steps

- [Provider-backed Inference](/sandboxes/inference-routing)
- [Profiles](/providers/profiles)
- [LM Studio CLI](https://lmstudio.ai/docs/cli)