Set up Ollama
Serve a local model with Ollama, raise its context window, add it to Borago Board as a self-hosted service and run a first task on it.
Ollama is the easiest self-hosted server for Borago Board. It needs no API key and a run costs nothing. It is also the only server that tells the app its real context window, and the only one the app can start and download models for. The full walk-through is Run tasks on a local model with Ollama.
You need
- The desktop app and a project, see Creating a project.
- A computer with about 24 GB of graphics memory, or 32 GB of unified memory on an Apple silicon Mac. Using a local AI model explains why.
Steps
1. Install Ollama and pull a model
Install Ollama from ollama.com. Then download a model that calls tools well.
ollama pull qwen3-coder:30bWhile Ollama runs, it serves the OpenAI-compatible endpoint at http://localhost:11434/v1.
2. Raise the context window
Ollama serves a small window by default. On Linux, run the first command, add the two lines and restart Ollama.
sudo systemctl edit ollama.service[Service]
Environment="OLLAMA_CONTEXT_LENGTH=32768"The Ollama tutorial gives the macOS and Windows equivalents. The app cannot set the window itself.
3. Add the service
Open Settings from the gear and choose Self-hosted services. Press Add a self-hosted service. Name it, keep Server address as http://localhost:11434/v1, leave API key (optional) empty and press Add.
4. Verify
Press Edit on the new row. Models on this server fills in. Press Verify. The card shows whether the model makes tool calls and the context window Ollama really serves. If Ollama is installed and stopped, the editor offers Start it. Leave Context window (tokens) empty, because Ollama reports its own.
5. Choose it for the runs
In the project, press Edit project, open AI Roles, and in Task runner choose your service from Self-hosted AI services (your hardware). Choose the model and press Save changes.
What happens. Self-hosted runs have a shell and a file editor in the project container, a code search and task breakdown. They have no web search, no page fetch and no investigator. A run stops at Time limit per task (min), 120 minutes by default.
First task to try
Create a file named hello.txt in the project's root folder containing the
single line "Hello from a local model". Then list the root folder.Related: Run tasks on a local model with Ollama, Using a local AI model, Set up vLLM, Choosing an AI provider