docs.json

.changeset/slimy-icons-sin.md

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+---
+"claude-dev": minor
+---
+
+Migrate running models locally section to new docs

docs/docs.json

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 					"getting-started/what-is-cline"
 				]
 			},
+			{
+				"group": "Running Models Locally",
+				"pages": [
+					"running-models-locally/read-me-first",
+					"running-models-locally/lm-studio",
+					"running-models-locally/ollama"
+				]
+			},
 			{
 				"group": "Exploring Cline's Tools",
 				"pages": [

docs/running-models-locally/lm-studio.mdx

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+---
+title: "LM Studio"
+description: "A quick guide to setting up LM Studio for local AI model execution with Cline."
+---
+
+## 🤖 Setting Up LM Studio with Cline
+
+Run AI models locally using LM Studio with Cline.
+
+### 📋 Prerequisites
+
+-   Windows, macOS, or Linux computer with AVX2 support
+-   Cline installed in VS Code
+
+### 🚀 Setup Steps
+
+#### 1. Install LM Studio
+
+-   Visit [lmstudio.ai](https://lmstudio.ai)
+-   Download and install for your operating system
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="LM Studio download page" />
+</Frame>
+
+#### 2. Launch LM Studio
+
+-   Open the installed application
+-   You'll see four tabs on the left: **Chat**, **Developer** (where you will start the server), **My Models** (where your downloaded models are stored), **Discover** (add new models)
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="LM Studio interface overview" />
+</Frame>
+
+#### 3. Download a Model
+
+-   Browse the "Discover" page
+-   Select and download your preferred model
+-   Wait for download to complete
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Downloading a model in LM Studio" />
+</Frame>
+
+#### 4. Start the Server
+
+-   Navigate to the "Developer" tab
+-   Toggle the server switch to "Running"
+-   Note: The server will run at `http://localhost:1234`
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Starting the LM Studio server" />
+</Frame>
+
+#### 5. Configure Cline
+
+1. Open VS Code
+2. Click Cline settings icon
+3. Select "LM Studio" as API provider
+4. Select your model from the available options
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Configuring Cline with LM Studio" />
+</Frame>
+
+### ⚠️ Important Notes
+
+-   Start LM Studio before using with Cline
+-   Keep LM Studio running in background
+-   First model download may take several minutes depending on size
+-   Models are stored locally after download
+
+### 🔧 Troubleshooting
+
+1. If Cline can't connect to LM Studio:
+2. Verify LM Studio server is running (check Developer tab)
+3. Ensure a model is loaded
+4. Check your system meets hardware requirements

docs/running-models-locally/ollama.mdx

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+---
+title: "Ollama"
+description: "A quick guide to setting up Ollama for local AI model execution with Cline."
+---
+
+### 📋 Prerequisites
+
+-   Windows, macOS, or Linux computer
+-   Cline installed in VS Code
+
+### 🚀 Setup Steps
+
+#### 1. Install Ollama
+
+-   Visit [ollama.com](https://ollama.com)
+-   Download and install for your operating system
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Ollama download page" />
+</Frame>
+
+#### 2. Choose and Download a Model
+
+-   Browse models at [ollama.com/search](https://ollama.com/search)
+-   Select model and copy command:
+
+    ```bash
+    ollama run [model-name]
+    ```
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Selecting a model in Ollama" />
+</Frame>
+
+-   Open your Terminal and run the command:
+
+    -   Example:
+
+        ```bash
+        ollama run llama2
+        ```
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Running Ollama in terminal" />
+</Frame>
+
+**✨ Your model is now ready to use within Cline!**
+
+#### 3. Configure Cline
+
+1. Open VS Code
+2. Click Cline settings icon
+3. Select "Ollama" as API provider
+4. Enter configuration:
+    - Base URL: `http://localhost:11434/` (default value, can be left as is)
+    - Select the model from your available options
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Configuring Cline with Ollama" />
+</Frame>
+
+### ⚠️ Important Notes
+
+-   Start Ollama before using with Cline
+-   Keep Ollama running in background
+-   First model download may take several minutes
+
+### 🔧 Troubleshooting
+
+If Cline can't connect to Ollama:
+
+1. Verify Ollama is running
+2. Check base URL is correct
+3. Ensure model is downloaded
+
+Need more info? Read the [Ollama Docs](https://github.com/ollama/ollama/blob/main/docs/api.md).

docs/running-models-locally/read-me-first.mdx

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+---
+title: "Read Me First"
+---
+
+## Running Local Models with Cline: What You Need to Know 🤖
+
+Cline is a powerful AI coding assistant that uses tool-calling to help you write, analyze, and modify code. While running models locally can save on API costs, there's an important trade-off: local models are significantly less reliable at using these essential tools.
+
+## Why Local Models Are Different 🔬
+
+When you run a "local version" of a model, you're actually running a drastically simplified copy of the original. This process, called distillation, is like trying to compress a professional chef's knowledge into a basic cookbook – you keep the simple recipes but lose the complex techniques and intuition.
+
+Local models are created by training a smaller model to imitate a larger one, but they typically only retain 1-26% of the original model's capacity. This massive reduction means:
+
+-   Less ability to understand complex contexts
+-   Reduced capability for multi-step reasoning
+-   Limited tool-use abilities
+-   Simplified decision-making process
+
+Think of it like running your development environment on a calculator instead of a computer – it might handle basic tasks, but complex operations become unreliable or impossible.
+
+<Frame>
+	<img src="/assets/robot_panel_dark.png" alt="Local model comparison diagram" />
+</Frame>
+
+### What Actually Happens
+
+When you run a local model with Cline:
+
+#### Performance Impact 📉
+
+-   Responses are 5-10x slower than cloud services
+-   System resources (CPU, GPU, RAM) get heavily utilized
+-   Your computer may become less responsive for other tasks
+
+#### Tool Reliability Issues 🛠️
+
+-   Code analysis becomes less accurate
+-   File operations may be unreliable
+-   Browser automation capabilities are reduced
+-   Terminal commands might fail more often
+-   Complex multi-step tasks often break down
+
+### Hardware Requirements 💻
+
+You'll need at minimum:
+
+-   Modern GPU with 8GB+ VRAM (RTX 3070 or better)
+-   32GB+ system RAM
+-   Fast SSD storage
+-   Good cooling solution
+
+Even with this hardware, you'll be running smaller, less capable versions of models:
+
+| Model Size | What You Get                                            |
+| ---------- | ------------------------------------------------------- |
+| 7B models  | Basic coding, limited tool use                          |
+| 14B models | Better coding, unstable tool use                        |
+| 32B models | Good coding, inconsistent tool use                      |
+| 70B models | Best local performance, but requires expensive hardware |
+
+Put simply, the cloud (API) versions of these models are the full-bore version of the model. The full version of DeepSeek-R1 is 671B. These distilled models are essentially "watered-down" versions of the cloud model.
+
+### Practical Recommendations 💡
+
+#### Consider This Approach
+
+1. Use cloud models for:
+    - Complex development tasks
+    - When tool reliability is crucial
+    - Multi-step operations
+    - Critical code changes
+2. Use local models for:
+    - Simple code completion
+    - Basic documentation
+    - When privacy is paramount
+    - Learning and experimentation
+
+#### If You Must Go Local
+
+-   Start with smaller models
+-   Keep tasks simple and focused
+-   Save work frequently
+-   Be prepared to switch to cloud models for complex operations
+-   Monitor system resources
+
+### Common Issues 🚨
+
+-   **"Tool execution failed":** Local models often struggle with complex tool chains. Simplify your prompt.
+-   **"No connection could be made because the target machine actively refused it":** This usually means that the Ollama or LM Studio server isn't running, or is running on a different port/address than Cline is configured to use. Double-check the Base URL address in your API Provider settings.
+-   **"Cline is having trouble...":** Increase your model's context length to its maximum size.
+-   **Slow or incomplete responses:** Local models can be slower than cloud-based models, especially on less powerful hardware. If performance is an issue, try using a smaller model. Expect significantly longer processing times.
+-   **System stability:** Watch for high GPU/CPU usage and temperature
+-   **Context limitations:** Local models often have smaller context windows than cloud models. Break tasks down into smaller pieces.
+
+### Looking Ahead 🔮
+
+Local model capabilities are improving, but they're not yet a complete replacement for cloud services, especially for Cline's tool-based functionality. Consider your specific needs and hardware capabilities carefully before committing to a local-only approach.
+
+### Need Help? 🤝
+
+-   Join our [Discord](https://discord.gg/cline) community and [r/cline](https://www.reddit.com/r/CLine/)
+-   Check the latest compatibility guides
+-   Share your experiences with other developers
+
+Remember: When in doubt, prioritize reliability over cost savings for important development work.