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What it’s good for

The easiest local setup. LM Studio has a built-in model browser, one-click downloads, and runs an OpenAI-compatible server out of the box. Great if you want local models without managing GGUF files and command-line flags.

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Requirements

  • LM Studio installed
  • A model downloaded through LM Studio’s model browser
  • The local server started in LM Studio (Developer tab)

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Configure in Spaceduck

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Chat

1

Start the LM Studio server

  1. Open LM Studio
  2. Load a model (e.g., Qwen3, Llama 3.1, DeepSeek)
  3. Go to the Developer tab
  4. Click Start Server — it runs at http://localhost:1234 by default
2

Configure Spaceduck

In Settings > Chat:
  • Provider: LM Studio
  • Base URL: http://localhost:1234/v1
  • Model: the model identifier shown in LM Studio (e.g., qwen/qwen3-4b-thinking-2507)
Or via CLI:
LM Studio doesn’t require an API key by default. Spaceduck sends a dummy key (lm-studio) for servers that require the Authorization header to be present.
3

Verify

You should see your loaded model listed.

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Embeddings

LM Studio can serve embeddings from the same server, or you can run a second instance on a different port.
1

Load an embedding model

In LM Studio, load an embedding model alongside your chat model, or start a second server instance on a different port.
2

Configure Spaceduck

In Settings > Memory:
  • Toggle Semantic recall on
  • Provider: LM Studio
  • Server URL: http://localhost:1234/v1 (same server, or a different port if running separately)
  • Model: your embedding model identifier
  • Dimensions: match the model (e.g., 768 for nomic, 1024 for large models)
Or via CLI:
3

Test

Click the Test button in Settings > Memory to verify the embedding connection.

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Test and troubleshoot

LM Studio’s model browser makes it easy to try different models. Download a few, test them with Spaceduck, and keep the one that works best for your use case.