> ## Documentation Index > Fetch the complete documentation index at: https://docs.pipeshub.com/llms.txt > Use this file to discover all available pages before exploring further. # Ollama > Configure PipesHub Workplace AI to use different Ollama models # Ollama Configuration Ollama Configuration Interface *The Ollama configuration screen in PipesHub where you'll configure your local Ollama instance connection* PipesHub allows you to integrate with Ollama to run open-source language models locally, providing privacy-focused AI capabilities in your workspace with full control over your data and infrastructure. ## Required Fields ### Model Name \* The Model Name field defines which Ollama model you want to use with PipesHub. **Popular Ollama models include:** * `gemma4:latest` - Google's Gemma 4 model * `hf.co/unsloth/gpt-oss-20b-GGUF:F16` - A Hugging Face-hosted GGUF model **How to choose a model:** * For multimodal capabilities (text + images), select models with vision support * Check Ollama's [model library](https://ollama.com/library) for the complete list of available models ## Optional Fields ### Endpoint URL The Endpoint URL specifies where your Ollama instance is running. Defaults to `http://host.docker.internal:11434` if left blank. **Common configurations:** * `http://host.docker.internal:11434` - For accessing Ollama from within a Docker container (default) * `https://your-server-domain` - For remote Ollama instances **Default port:** Ollama runs on port `11434` by default. **Note:** Ensure your Ollama instance is running and accessible at the specified endpoint before configuring PipesHub. ### API Key The API Key field is optional for Ollama configurations. Most local Ollama instances do not require authentication. **When to use:** * If you've configured authentication on your Ollama instance * When connecting to a secured remote Ollama server * For enterprise deployments with access control If your Ollama instance doesn't require authentication, you can leave this field empty. ## Advanced Options ### Multimodal Support Enable the Multimodal checkbox if your selected model supports both text and image inputs. **Multimodal-capable models include:** * `qwen3-vl:8b` **When enabled:** * PipesHub can send both text and image data to the model * Supports use cases like image analysis, visual question answering, and document understanding ### Reasoning Enable the Reasoning checkbox if your selected model has enhanced reasoning capabilities. **When enabled:** * The model will be used for tasks requiring complex logical reasoning * Better performance on multi-step problem solving * Enhanced analytical capabilities ## Configuration Steps As shown in the image above: 1. Click **Configure** on the Ollama provider card 2. Enter your Model Name (marked with \*) — e.g., `gemma4:latest` or `hf.co/unsloth/gpt-oss-20b-GGUF:F16` 3. (Optional) Specify your Endpoint URL — defaults to `http://host.docker.internal:11434` 4. (Optional) Enter an API Key if your Ollama instance requires authentication 5. (Optional) Check "Multimodal" if your model supports text + image processing 6. (Optional) Check "Reasoning" if your model has enhanced reasoning capabilities 7. Click **Add Model** to save and validate your credentials > Model Name is the only required field. The endpoint defaults to `http://host.docker.internal:11434` if left blank. No API key is required for a standard local Ollama installation. ## Prerequisites Before configuring Ollama in PipesHub, ensure you have: 1. **Ollama installed** on your machine or server * Download from [ollama.com](https://ollama.com/) * Follow installation instructions for your operating system 2. **Model downloaded** - Pull your desired model: ```bash theme={null} ollama pull qwen3-vl:8b # or any other model from the library ``` 3. **Ollama running** - Start the Ollama service: ```bash theme={null} ollama serve ``` 4. **Network accessibility** - Ensure PipesHub can reach your Ollama endpoint ## Usage Considerations ### Advantages of Ollama * **Privacy:** All processing happens locally or on your infrastructure * **Cost-effective:** No API usage fees or quotas * **Customizable:** Full control over model selection and configuration * **Offline capable:** Works without internet connectivity (after initial model download) * **No rate limits:** Process as many requests as your hardware can handle ### System Requirements * Sufficient RAM (varies by model - typically 8GB minimum, 16GB+ recommended) * Modern CPU or GPU for optimal performance * Adequate disk space for model storage (models range from 2GB to 40GB+) ### Performance Tips * Use GPU acceleration when available for faster inference * Choose smaller models for quicker responses if your use case allows * Consider quantized models (e.g., `llama2:7b-q4` vs `llama2:7b`) for lower memory usage ## Troubleshooting ### Connection Issues * **Verify Ollama is running:** Run `ollama list` in your terminal * **Check endpoint URL:** Ensure the URL matches where Ollama is accessible * **Firewall settings:** Verify that port 11434 is not blocked * **Docker networking:** If using Docker, ensure proper network configuration for `host.docker.internal` ### Model Issues * **Model not found:** Ensure you've pulled the model with `ollama pull ` * **Out of memory:** Try a smaller or quantized version of the model * **Slow performance:** Consider using a GPU ### Multimodal Issues * **Images not processing:** Verify your selected model supports vision capabilities * **Check model documentation:** Not all models support multimodal inputs ## Available Models Visit the [Ollama Model Library](https://ollama.com/library) to browse all available models, including: * Language models (LLMs) * Code-specialized models * Multimodal models (vision + language) * Embedding models * Quantized variants for efficiency For additional support, refer to the [Ollama documentation](https://github.com/ollama/ollama/tree/main/docs) or contact PipesHub support.