Seeing What’s Possible with OpenCode + Ollama + Qwen3-Coder

# Introduction

 
We live in an exciting era where you can run a powerful artificial intelligence coding assistant directly on your own computer, completely offline, without paying a monthly subscription fee. This article will show you how to build a free, local artificial intelligence coding setup by combining three powerful tools: OpenCode, Ollama, and Qwen3-Coder.

By the end of this tutorial, you will have a complete understanding of how to run Qwen3-Coder locally with Ollama and integrate it into your workflow using OpenCode. Think of it as building your own private, offline artificial intelligence pair programmer.

Let us break down each piece of our local setup. Understanding the role of each tool will help you make sense of the entire system:

1. OpenCode: This is your interface. It is an open-source artificial intelligence coding assistant that lives in your terminal, integrated development environment (IDE), or as a desktop app. Think of it as the “front-end” you talk to. It understands your project structure, can read and write files, run commands, and interact with Git, all through a simple text-based interface. The best part? You can download OpenCode for free.
2. Ollama: This is your model manager. It is a tool that lets you download, run, and manage large language models (LLMs) locally with just a single command. You can think of it as a lightweight engine that powers the artificial intelligence brain. You can install Ollama from its official website.
3. Qwen3-Coder: This is your artificial intelligence brain. It is a powerful coding model from Alibaba Cloud, specifically designed for code generation, completion, and repair. The Qwen3-Coder model boasts an incredible 256,000 token context window, which means it can understand and work with very large code files or entire small projects at once.

When you combine these three, you get a fully functional, local artificial intelligence code assistant that offers complete privacy, zero latency, and unlimited use.

# Choosing A Local Artificial Intelligence Coding Assistant

 
You might wonder why you should go through the effort of a local setup when cloud-based artificial intelligence assistants like GitHub Copilot are available. Here is why a local setup is often a superior choice:

• Total Privacy and Security: Your code never leaves your computer. For companies working with sensitive or proprietary code, this is a game-changer. You are not sending your intellectual property to a third-party server.
• Zero Cost, Unlimited Usage: Once you have set up the tools, you can use them as much as you want. There are no API fees, no usage limits, and no surprises on a monthly bill.
• No Internet Required: You can code on a plane, in a remote cabin, or anywhere with a laptop. Your artificial intelligence assistant works fully offline.
• Full Control: You choose the model that runs on your machine. You can switch between models, fine-tune them, or even create your own custom models. You are not locked into any vendor’s ecosystem.

For many developers, the privacy and cost benefits alone are reason enough to switch to a local artificial intelligence code assistant like the one we are building today.

# Meeting The Prerequisites

 
Before we start installing things, let us ensure your computer is ready. The requirements are modest, but meeting them will ensure a smooth experience:

• A Modern Computer: Most laptops and desktops from the last 5-6 years will work fine. You need at least 8GB of random-access memory (RAM), but 16GB is highly recommended for a smooth experience with the 7B model we will use.
• Sufficient Storage Space: Artificial intelligence models are large. The qwen2.5-coder:7b model we will use is about 4-5 GB in size. Ensure you have at least 10-15 GB of free space to be comfortable.
• Operating System: Ollama and OpenCode work on Windows, macOS (both Intel and Apple Silicon), and Linux.
• Basic Comfort with the Terminal: You will need to run commands in your terminal or command prompt. Do not worry if you are not an expert — we will explain every command step by step.

# Following The Step-By-Step Setup Guide

 
Now, we will proceed to set everything up.

// Installing Ollama

Ollama is our model manager. Installing it is straightforward.

This should print the version number of Ollama, confirming it was installed correctly.

// Installing OpenCode

OpenCode is our artificial intelligence coding assistant interface. There are several ways to install it. We will cover the simplest method using npm, a standard tool for JavaScript developers.

• First, ensure you have Node.js installed on your system. Node.js includes npm, which we need.
• Open your terminal and run the following command. If you prefer not to use npm, you can use a one-command installer for Linux/macOS:

  curl -fsSL https://opencode.ai/install | bash

   

  Or, if you are on macOS and use Homebrew, you can run:

  brew install sst/tap/opencode

   

  These methods will also install OpenCode for you.

• After installation, verify it works by running:

   

// Pulling The Qwen3-Coder Model

Now for the exciting part: you will need to download the artificial intelligence model that will power your assistant. We will use the qwen2.5-coder:7b model. It is a 7-billion parameter model, offering a fantastic balance of coding ability, speed, and hardware requirements. It is a perfect starting point for most developers.

• First, we need to start the Ollama service. In your terminal, run:

   

  This starts the Ollama server in the background. Keep this terminal window open or run it as a background service. On many systems, Ollama starts automatically after installation.

• Open a new terminal window for the next command. Now, pull the model:

  ollama pull qwen2.5-coder:7b

   

  This command will download the model from Ollama’s library. The download size is about 4.2 GB, so it may take a few minutes depending on your internet speed. You will see a progress bar showing the download status.

• Once the download is complete, you can test the model by running a quick interactive session:

  ollama run qwen2.5-coder:7b

   

  Type a simple coding question, such as:

  Write a Python function that prints ‘Hello, World!’.

   

  You should see the model generate an answer. Type /bye to exit the session. This confirms that your model is working perfectly. Note: If you have a powerful computer with lots of RAM (32GB or more) and a good graphics processing unit (GPU), you can try the larger 14B or 32B versions of the Qwen2.5-Coder model for even better coding assistance. Just replace 7b with 14b or 32b in the ollama pull command.

# Configuring OpenCode To Use Ollama And Qwen3-Coder

 
Now we have the model ready, but OpenCode does not know about it yet. We need to tell OpenCode to use our local Ollama model. Here is the most reliable way to configure this:

• First, we need to increase the context window for our model. The Qwen3-Coder model can handle up to 256,000 tokens of context, but Ollama has a default setting of only 4096 tokens. This will severely limit what the model can do. To fix this, we create a new model with a larger context window.
• In your terminal, run:

  ollama run qwen2.5-coder:7b

   

  This starts an interactive session with the model.

• Inside the session, set the context window to 16384 tokens (16k is a good starting point):

  >>> /set parameter num_ctx 16384

   

  You should see a confirmation message.

• Now, save this modified model under a new name:

  >>> /save qwen2.5-coder:7b-16k

   

  This creates a new model entry called qwen2.5-coder:7b-16k in your Ollama library.

• Type /bye to exit the interactive session.
• Now we need to tell OpenCode to use this model. We will create a configuration file. OpenCode looks for a config.json file in ~/.config/opencode/ (on Linux/macOS) or %APPDATA%\opencode\config.json (on Windows).
• Using a text editor (like VS Code, Notepad++, or even nano in the terminal), create or edit the config.json file and add the following content:

  {
    "$schema": "https://opencode.ai/config.json",
    "provider": {
      "ollama": {
        "npm": "@ai-sdk/openai-compatible",
        "options": {
          "baseURL": "http://localhost:11434/v1"
        },
        "models": {
          "qwen2.5-coder:7b-16k": {
            "tools": true
          }
        }
      }
    }
  }

   

  This configuration does a few important things. It tells OpenCode to use Ollama’s OpenAI-compatible API endpoint (which runs at http://localhost:11434/v1). It also specifically registers our qwen2.5-coder:7b-16k model and, very importantly, enables tool usage. Tools are what allow the artificial intelligence to read and write files, run commands, and interact with your project. The "tools": true setting is essential for making OpenCode a truly useful assistant.

# Using OpenCode With Your Local Artificial Intelligence

 
Your local artificial intelligence assistant is now ready for action. Let us see how to use it effectively. Navigate to a project directory where you want to experiment. For example, you can create a new folder called my-ai-project:

mkdir my-ai-project
cd my-ai-project

Now, launch OpenCode:

You will be greeted by OpenCode’s interactive terminal interface. To ask it to do something, simply type your request and press Enter. For example:

• Generate a new file: Try to create a simple hypertext markup language (HTML) page with a heading and a paragraph. OpenCode will think for a moment and then show you the code it wants to write. It will ask for your confirmation before actually creating the file on your disk. This is a safety feature.
• Read and analyze code: Once you have some files in your project, you can ask questions like “Explain what the main function does” or “Find any potential bugs in the code”.
• Run commands: You can ask it to run terminal commands: “Install the express package using npm”.
• Use Git: It can help with version control. “Show me the git status” or “Commit the current changes with a message ‘Initial commit'”.

OpenCode operates with a degree of autonomy. It will propose actions, show you the changes it wants to make, and wait for your approval. This gives you full control over your codebase.

# Understanding The OpenCode And Ollama Integration

 
The combination of OpenCode and Ollama is exceptionally powerful because they complement each other so well. OpenCode provides the intelligence and the tool system, while Ollama handles the heavy lifting of running the model efficiently on your local hardware.

This Ollama with OpenCode tutorial would be incomplete without highlighting this synergy. OpenCode’s developers have put significant effort into ensuring that the OpenCode and Ollama integration works seamlessly. The configuration we set up above is the result of that work. It allows OpenCode to treat Ollama as just another artificial intelligence provider, giving you access to all of OpenCode’s features while keeping everything local.

# Exploring Practical Use Cases And Examples

 
Let us explore some real-world scenarios where your new local artificial intelligence assistant can save you hours of work.

1. Understanding a Foreign Codebase: Imagine you have just joined a new project or need to contribute to an open-source library you have never seen before. Understanding a large, unfamiliar codebase can be daunting. With OpenCode, you can simply ask. Navigate to the project’s root directory and run opencode. Then type:
   

   Explain the purpose of the main entry point of this application.

    

   OpenCode will scan the relevant files and provide a clear explanation of what the code does and how it fits into the larger application.

2. Generating Boilerplate Code: Boilerplate code is the repetitive, standard code you need to write for every new feature — it is a perfect job for an artificial intelligence. Instead of writing it yourself, you can ask OpenCode to do it. For example, if you are building a representational state transfer (REST) API with Node.js and Express, you could type:
   

   Create a REST API endpoint for user registration. It should accept a username and password, hash the password using bcrypt, and save the user to a MongoDB database.

    

   OpenCode will then generate all the necessary files: the route handler, the controller logic, the database model, and even the installation commands for the required packages.

3. Debugging and Fixing Errors: We have all spent hours staring at a cryptic error message. OpenCode can help you debug faster. When you encounter an error, you can ask OpenCode to help. For instance, if you see a TypeError: Cannot read property 'map' of undefined in your JavaScript console, you can ask:
   

   Fix the TypeError: Cannot read property ‘map’ of undefined in the userList function.

    

   OpenCode will analyze the code, identify that you are trying to use .map() on a variable that is undefined at that moment, and suggest a fix, such as adding a check for the variable’s existence before calling .map().

4. Writing Unit Tests: Testing is crucial, but writing tests can be tedious. You can ask OpenCode to generate unit tests for you. For a Python function that calculates the factorial of a number, you could type:
   

   Write comprehensive unit tests for the factorial function. Include edge cases.

    

   OpenCode will generate a test file with test cases for positive numbers, zero, negative numbers, and large inputs, saving you a significant amount of time.

# Troubleshooting Common Issues

 
Even with a straightforward setup, you might encounter some hiccups. Here is a guide to solving the most common problems.

// Fixing The opencode Command Not Found Error

• Problem: After installing OpenCode, typing opencode in your terminal gives a “command not found” error.
• Solution: This usually means the directory where npm installs global packages is not in your system’s PATH. On many systems, npm installs global binaries to ~/.npm-global/bin or /usr/local/bin. You need to add the correct directory to your PATH. A quick workaround is to reinstall OpenCode using the one-command installer (curl -fsSL https://opencode.ai/install | bash), which often handles PATH configuration automatically.

// Fixing The Ollama Connection Refused Error

• Problem: When you run opencode, you see an error about being unable to connect to Ollama or ECONNREFUSED.
• Solution: This almost always means the Ollama server is not running. Make sure you have a terminal window open with ollama serve running. Alternatively, on many systems, you can run ollama serve as a background process. Also, ensure that no other application is using port 11434, which is Ollama’s default port. You can test the connection by running curl http://localhost:11434/api/tags in a new terminal — if it returns a JSON list of your models, Ollama is running correctly.

// Addressing Slow Models Or High RAM Usage

• Problem: The model runs slowly, or your computer becomes sluggish when using it.
• Solution: The 7B model we are using requires about 8GB of RAM. If you have less, or if your central processing unit (CPU) is older, you can try a smaller model. Ollama offers smaller versions of the Qwen2.5-Coder model, such as the 3B or 1.5B versions. These are significantly faster and use less memory, though they are also less capable. To use one, simply run ollama pull qwen2.5-coder:3b and then configure OpenCode to use that model instead. For CPU-only systems, you can also try setting the environment variable OLLAMA_LOAD_IN_GPU=false before starting Ollama, which forces it to use the CPU only, which is slower but can be more stable on some systems.

// Fixing Artificial Intelligence Inability To Create Or Edit Files

• Problem: OpenCode can analyze your code and chat with you, but when you ask it to create a new file or edit existing code, it fails or says it cannot.
• Solution: This is the most common configuration issue. It happens because tool usage is not enabled for your model. Double-check your OpenCode configuration file (config.json). Ensure the "tools": true line is present under your specific model, as shown in our configuration example. Also, make sure you are using the model we saved with the increased context window (qwen2.5-coder:7b-16k). The default model download does not have the necessary context length for OpenCode to manage its tools properly.

# Following Performance Tips For A Smooth Experience

 
To get the best performance out of your local artificial intelligence coding assistant, keep these tips in mind:

• Use a GPU if Possible: If you have a dedicated GPU from NVIDIA or an Apple Silicon Mac (M1, M2, M3), Ollama will automatically use it. This dramatically speeds up the model’s responses. For NVIDIA GPUs, ensure you have the latest drivers installed. For Apple Silicon, no extra configuration is needed.
• Close Unnecessary Applications: LLMs are resource-intensive. Before a heavy coding session, close web browsers with dozens of tabs, video editors, or other memory-hungry applications to free up RAM for the artificial intelligence model.
• Consider Model Size for Your Hardware: For 8-16GB RAM systems, use qwen2.5-coder:3b or qwen2.5-coder:7b (with num_ctx set to 8192 for better speed). For 16-32GB RAM setups, use qwen2.5-coder:7b (with num_ctx set to 16384, as in our guide). For 32GB+ RAM setups with a good GPU, you can try the excellent qwen2.5-coder:14b or even the 32b version for state-of-the-art coding assistance.
• Keep Your Models Updated: The Ollama library and the Qwen models are actively improved. Occasionally run ollama pull qwen2.5-coder:7b to ensure you have the latest version of the model.

# Wrapping Up

 
You have now built a powerful, private, and completely free artificial intelligence coding assistant that runs on your own computer. By combining OpenCode, Ollama, and Qwen3-Coder, you have taken a significant step toward a more efficient and secure development workflow.

This local artificial intelligence code assistant puts you in control. Your code stays on your machine. There are no usage limits, no API keys to manage, and no monthly fees. You have a capable artificial intelligence pair programmer that works offline and respects your privacy.

The journey does not end here. You can explore other models in the Ollama library, such as the larger Qwen2.5-Coder 32B or the general-purpose Llama 3 models. You can also tweak the context window or other parameters to suit your specific projects.

I encourage you to start using OpenCode in your daily work. Ask it to write your next function, help you debug a tricky error, or explain a complex piece of legacy code. The more you use it, the more you will discover its capabilities.
 
 

Shittu Olumide is a software engineer and technical writer passionate about leveraging cutting-edge technologies to craft compelling narratives, with a keen eye for detail and a knack for simplifying complex concepts. You can also find Shittu on Twitter.