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

April 26, 2026

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# Introduction

We dwell in an thrilling period the place you may run a strong synthetic intelligence coding assistant immediately by yourself pc, utterly offline, with out paying a month-to-month subscription price. This text will present you how one can construct a free, native synthetic intelligence coding setup by combining three highly effective instruments: OpenCode, Ollama, and Qwen3-Coder.

By the tip of this tutorial, you should have an entire understanding of how one can run Qwen3-Coder domestically with Ollama and combine it into your workflow utilizing OpenCode. Consider it as constructing your individual non-public, offline synthetic intelligence pair programmer.

Allow us to break down each bit of our native setup. Understanding the function of every software will enable you to make sense of your entire system:

OpenCode: That is your interface. It’s an open-source synthetic intelligence coding assistant that lives in your terminal, built-in growth atmosphere (IDE), or as a desktop app. Consider it because the “front-end” you discuss to. It understands your challenge construction, can learn and write recordsdata, run instructions, and work together with Git, all via a easy text-based interface. The perfect half? You’ll be able to obtain OpenCode totally free.
Ollama: That is your mannequin supervisor. It’s a software that allows you to obtain, run, and handle giant language fashions (LLMs) domestically with only a single command. You’ll be able to consider it as a light-weight engine that powers the substitute intelligence mind. You’ll be able to set up Ollama from its official web site.
Qwen3-Coder: That is your synthetic intelligence mind. It’s a highly effective coding mannequin from Alibaba Cloud, particularly designed for code era, completion, and restore. The Qwen3-Coder mannequin boasts an unbelievable 256,000 token context window, which implies it will possibly perceive and work with very giant code recordsdata or complete small initiatives without delay.

While you mix these three, you get a totally practical, native synthetic intelligence code assistant that provides full privateness, zero latency, and limitless use.

# Selecting A Native Synthetic Intelligence Coding Assistant

You would possibly marvel why you must undergo the trouble of an area setup when cloud-based synthetic intelligence assistants like GitHub Copilot can be found. Right here is why an area setup is usually a superior selection:

Complete Privateness and Safety: Your code by no means leaves your pc. For firms working with delicate or proprietary code, it is a game-changer. You aren’t sending your mental property to a third-party server.
Zero Value, Limitless Utilization: After you have arrange the instruments, you should utilize them as a lot as you need. There aren’t any API charges, no utilization limits, and no surprises on a month-to-month invoice.
No Web Required: You’ll be able to code on a aircraft, in a distant cabin, or wherever with a laptop computer. Your synthetic intelligence assistant works absolutely offline.
Full Management: You select the mannequin that runs in your machine. You’ll be able to change between fashions, fine-tune them, and even create your individual customized fashions. You aren’t locked into any vendor’s ecosystem.

For a lot of builders, the privateness and value advantages alone are purpose sufficient to modify to an area synthetic intelligence code assistant just like the one we’re constructing in the present day.

# Assembly The Conditions

Earlier than we begin putting in issues, allow us to guarantee your pc is prepared. The necessities are modest, however assembly them will guarantee a easy expertise:

A Trendy Pc: Most laptops and desktops from the final 5-6 years will work high quality. You want no less than 8GB of random-access reminiscence (RAM), however 16GB is extremely beneficial for a easy expertise with the 7B mannequin we’ll use.
Ample Storage House: Synthetic intelligence fashions are giant. The qwen2.5-coder:7b mannequin we’ll use is about 4-5 GB in measurement. Guarantee you’ve gotten no less than 10-15 GB of free house to be snug.
Working System: Ollama and OpenCode work on Home windows, macOS (each Intel and Apple Silicon), and Linux.
Fundamental Consolation with the Terminal: You’ll need to run instructions in your terminal or command immediate. Don’t worry if you’re not an knowledgeable — we’ll clarify each command step-by-step.

# Following The Step-By-Step Setup Information

Now, we’ll proceed to set every thing up.

// Putting in Ollama

Ollama is our mannequin supervisor. Putting in it’s easy.

This could print the model variety of Ollama, confirming it was put in appropriately.

// Putting in OpenCode

OpenCode is our synthetic intelligence coding assistant interface. There are a number of methods to put in it. We’ll cowl the best methodology utilizing npm, a typical software for JavaScript builders.

First, guarantee you’ve gotten Node.js put in in your system. Node.js consists of npm, which we’d like.
Open your terminal and run the next command. For those who want to not use npm, you should utilize a one-command installer for Linux/macOS:
```
curl -fsSL https://opencode.ai/set up | bash
```
Or, if you’re on macOS and use Homebrew, you may run:
```
brew set up sst/faucet/opencode
```
These strategies can even set up OpenCode for you.
After set up, confirm it really works by operating:

// Pulling The Qwen3-Coder Mannequin

Now for the thrilling half: you will want to obtain the substitute intelligence mannequin that can energy your assistant. We’ll use the qwen2.5-coder:7b mannequin. It’s a 7-billion parameter mannequin, providing a incredible steadiness of coding capacity, pace, and {hardware} necessities. It’s a excellent place to begin for many builders.

First, we have to begin the Ollama service. In your terminal, run:

This begins the Ollama server within the background. Hold this terminal window open or run it as a background service. On many programs, Ollama begins robotically after set up.
Open a brand new terminal window for the following command. Now, pull the mannequin:
```
ollama pull qwen2.5-coder:7b
```
This command will obtain the mannequin from Ollama’s library. The obtain measurement is about 4.2 GB, so it might take a couple of minutes relying in your web pace. You will note a progress bar exhibiting the obtain standing.
As soon as the obtain is full, you may take a look at the mannequin by operating a fast interactive session:
```
ollama run qwen2.5-coder:7b
```
Sort a easy coding query, equivalent to:

Write a Python operate that prints ‘Whats up, World!’.

It’s best to see the mannequin generate a solution. Sort /bye to exit the session. This confirms that your mannequin is working completely. Notice: If in case you have a strong pc with plenty of RAM (32GB or extra) and a superb graphics processing unit (GPU), you may attempt the bigger 14B or 32B variations of the Qwen2.5-Coder mannequin for even higher coding help. Simply change 7b with 14b or 32b within the ollama pull command.

# Configuring OpenCode To Use Ollama And Qwen3-Coder

Now we have now the mannequin prepared, however OpenCode doesn’t learn about it but. We have to inform OpenCode to make use of our native Ollama mannequin. Right here is probably the most dependable strategy to configure this:

First, we have to improve the context window for our mannequin. The Qwen3-Coder mannequin can deal with as much as 256,000 tokens of context, however Ollama has a default setting of solely 4096 tokens. This can severely restrict what the mannequin can do. To repair this, we create a brand new mannequin with a bigger context window.
In your terminal, run:
```
ollama run qwen2.5-coder:7b
```
This begins an interactive session with the mannequin.
Contained in the session, set the context window to 16384 tokens (16k is an efficient place to begin):
```
>>> /set parameter num_ctx 16384
```
It’s best to see a affirmation message.
Now, save this modified mannequin underneath a brand new identify:
```
>>> /save qwen2.5-coder:7b-16k
```
This creates a brand new mannequin entry known as qwen2.5-coder:7b-16k in your Ollama library.
Sort /bye to exit the interactive session.
Now we have to inform OpenCode to make use of this mannequin. We’ll create a configuration file. OpenCode seems to be for a config.json file in ~/.config/opencode/ (on Linux/macOS) or %APPDATApercentopencodeconfig.json (on Home windows).
Utilizing a textual content editor (like VS Code, Notepad++, and even nano within the terminal), create or edit the config.json file and add the next content material:
```
{
  "$schema": "https://opencode.ai/config.json",
  "supplier": {
    "ollama": {
      "npm": "@ai-sdk/openai-compatible",
      "choices": {
        "baseURL": "http://localhost:11434/v1"
      },
      "fashions": {
        "qwen2.5-coder:7b-16k": {
          "instruments": true
        }
      }
    }
  }
}
```
This configuration does a couple of essential issues. It tells OpenCode to make use of Ollama’s OpenAI-compatible API endpoint (which runs at http://localhost:11434/v1). It additionally particularly registers our qwen2.5-coder:7b-16k mannequin and, very importantly, permits software utilization. Instruments are what enable the substitute intelligence to learn and write recordsdata, run instructions, and work together along with your challenge. The "instruments": true setting is important for making OpenCode a really helpful assistant.

# Utilizing OpenCode With Your Native Synthetic Intelligence

Your native synthetic intelligence assistant is now prepared for motion. Allow us to see how one can use it successfully. Navigate to a challenge listing the place you wish to experiment. For instance, you may create a brand new folder known as my-ai-project:

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

Now, launch OpenCode:

You’ll be greeted by OpenCode’s interactive terminal interface. To ask it to do one thing, merely kind your request and press Enter. For instance:

Generate a brand new file: Attempt to create a easy hypertext markup language (HTML) web page with a heading and a paragraph. OpenCode will suppose for a second after which present you the code it desires to write down. It’s going to ask in your affirmation earlier than really creating the file in your disk. It is a security function.
Learn and analyze code: After you have some recordsdata in your challenge, you may ask questions like “Clarify what the primary operate does” or “Discover any potential bugs within the code”.
Run instructions: You’ll be able to ask it to run terminal instructions: “Set up the specific package deal utilizing npm”.
Use Git: It might probably assist with model management. “Present me the git standing” or “Commit the present adjustments with a message ‘Preliminary commit'”.

OpenCode operates with a level of autonomy. It’s going to suggest actions, present you the adjustments it desires to make, and wait in your approval. This provides you full management over your codebase.

# Understanding The OpenCode And Ollama Integration

The mixture of OpenCode and Ollama is exceptionally highly effective as a result of they complement one another so effectively. OpenCode gives the intelligence and the software system, whereas Ollama handles the heavy lifting of operating the mannequin effectively in your native {hardware}.

This Ollama with OpenCode tutorial can be incomplete with out highlighting this synergy. OpenCode’s builders have put important effort into making certain that the OpenCode and Ollama integration works seamlessly. The configuration we arrange above is the results of that work. It permits OpenCode to deal with Ollama as simply one other synthetic intelligence supplier, providing you with entry to all of OpenCode’s options whereas conserving every thing native.

# Exploring Sensible Use Instances And Examples

Allow us to discover some real-world eventualities the place your new native synthetic intelligence assistant can prevent hours of labor.

Understanding a International Codebase: Think about you’ve gotten simply joined a brand new challenge or have to contribute to an open-source library you’ve gotten by no means seen earlier than. Understanding a big, unfamiliar codebase will be daunting. With OpenCode, you may merely ask. Navigate to the challenge’s root listing and run opencode. Then kind:

Clarify the aim of the primary entry level of this utility.

OpenCode will scan the related recordsdata and supply a transparent clarification of what the code does and the way it suits into the bigger utility.
Producing Boilerplate Code: Boilerplate code is the repetitive, normal code you must write for each new function — it’s a excellent job for a synthetic intelligence. As a substitute of writing it your self, you may ask OpenCode to do it. For instance, if you’re constructing a representational state switch (REST) API with Node.js and Specific, you may kind:

Create a REST API endpoint for consumer registration. It ought to settle for a username and password, hash the password utilizing bcrypt, and save the consumer to a MongoDB database.

OpenCode will then generate all the required recordsdata: the route handler, the controller logic, the database mannequin, and even the set up instructions for the required packages.
Debugging and Fixing Errors: Now we have all spent hours looking at a cryptic error message. OpenCode might help you debug quicker. While you encounter an error, you may ask OpenCode to assist. For example, should you see a TypeError: Can not learn property 'map' of undefined in your JavaScript console, you may ask:

Repair the TypeError: Can not learn property ‘map’ of undefined within the userList operate.

OpenCode will analyze the code, establish that you’re attempting to make use of .map() on a variable that’s undefined at that second, and counsel a repair, equivalent to including a verify for the variable’s existence earlier than calling .map().
Writing Unit Exams: Testing is essential, however writing assessments will be tedious. You’ll be able to ask OpenCode to generate unit assessments for you. For a Python operate that calculates the factorial of a quantity, you may kind:

Write complete unit assessments for the factorial operate. Embody edge circumstances.

OpenCode will generate a take a look at file with take a look at circumstances for optimistic numbers, zero, destructive numbers, and huge inputs, saving you a big period of time.

# Troubleshooting Widespread Points

Even with a simple setup, you would possibly encounter some hiccups. Here’s a information to fixing the most typical issues.

// Fixing The opencode Command Not Discovered Error

Downside: After putting in OpenCode, typing opencode in your terminal provides a “command not discovered” error.
Resolution: This often means the listing the place npm installs world packages will not be in your system’s PATH. On many programs, npm installs world binaries to ~/.npm-global/bin or /usr/native/bin. It is advisable add the proper listing to your PATH. A fast workaround is to reinstall OpenCode utilizing the one-command installer (curl -fsSL https://opencode.ai/set up | bash), which regularly handles PATH configuration robotically.

// Fixing The Ollama Connection Refused Error

Downside: While you run opencode, you see an error about being unable to connect with Ollama or ECONNREFUSED.
Resolution: This virtually all the time means the Ollama server will not be operating. Be sure you have a terminal window open with ollama serve operating. Alternatively, on many programs, you may run ollama serve as a background course of. Additionally, be sure that no different utility is utilizing port 11434, which is Ollama’s default port. You’ll be able to take a look at the connection by operating curl http://localhost:11434/api/tags in a brand new terminal — if it returns a JSON checklist of your fashions, Ollama is operating appropriately.

// Addressing Sluggish Fashions Or Excessive RAM Utilization

Downside: The mannequin runs slowly, or your pc turns into sluggish when utilizing it.
Resolution: The 7B mannequin we’re utilizing requires about 8GB of RAM. If in case you have much less, or in case your central processing unit (CPU) is older, you may attempt a smaller mannequin. Ollama provides smaller variations of the Qwen2.5-Coder mannequin, such because the 3B or 1.5B variations. These are considerably quicker and use much less reminiscence, although they’re additionally much less succesful. To make use of one, merely run ollama pull qwen2.5-coder:3b after which configure OpenCode to make use of that mannequin as a substitute. For CPU-only programs, you too can attempt setting the atmosphere variable OLLAMA_LOAD_IN_GPU=false earlier than beginning Ollama, which forces it to make use of the CPU solely, which is slower however will be extra steady on some programs.

// Fixing Synthetic Intelligence Incapability To Create Or Edit Recordsdata

Downside: OpenCode can analyze your code and chat with you, however once you ask it to create a brand new file or edit current code, it fails or says it can not.
Resolution: That is the most typical configuration difficulty. It occurs as a result of software utilization will not be enabled in your mannequin. Double-check your OpenCode configuration file (config.json). Make sure the "instruments": true line is current underneath your particular mannequin, as proven in our configuration instance. Additionally, be sure you are utilizing the mannequin we saved with the elevated context window (qwen2.5-coder:7b-16k). The default mannequin obtain doesn’t have the required context size for OpenCode to handle its instruments correctly.

# Following Efficiency Ideas For A Clean Expertise

To get the very best efficiency out of your native synthetic intelligence coding assistant, hold the following pointers in thoughts:

Use a GPU if Attainable: If in case you have a devoted GPU from NVIDIA or an Apple Silicon Mac (M1, M2, M3), Ollama will robotically use it. This dramatically quickens the mannequin’s responses. For NVIDIA GPUs, guarantee you’ve gotten the most recent drivers put in. For Apple Silicon, no further configuration is required.
Shut Pointless Purposes: LLMs are resource-intensive. Earlier than a heavy coding session, shut net browsers with dozens of tabs, video editors, or different memory-hungry functions to unencumber RAM for the substitute intelligence mannequin.
Contemplate Mannequin Measurement for Your {Hardware}: For 8-16GB RAM programs, use qwen2.5-coder:3b or qwen2.5-coder:7b (with num_ctx set to 8192 for higher pace). For 16-32GB RAM setups, use qwen2.5-coder:7b (with num_ctx set to 16384, as in our information). For 32GB+ RAM setups with a superb GPU, you may attempt the superb qwen2.5-coder:14b and even the 32b model for state-of-the-art coding help.
Hold Your Fashions Up to date: The Ollama library and the Qwen fashions are actively improved. Often run ollama pull qwen2.5-coder:7b to make sure you have the most recent model of the mannequin.

# Wrapping Up

You might have now constructed a strong, non-public, and utterly free synthetic intelligence coding assistant that runs by yourself pc. By combining OpenCode, Ollama, and Qwen3-Coder, you’ve gotten taken a big step towards a extra environment friendly and safe growth workflow.

This native synthetic intelligence code assistant places you in management. Your code stays in your machine. There aren’t any utilization limits, no API keys to handle, and no month-to-month charges. You might have a succesful synthetic intelligence pair programmer that works offline and respects your privateness.

The journey doesn’t finish right here. You’ll be able to discover different fashions within the Ollama library, such because the bigger Qwen2.5-Coder 32B or the general-purpose Llama 3 fashions. You may also tweak the context window or different parameters to fit your particular initiatives.

I encourage you to start out utilizing OpenCode in your day by day work. Ask it to write down your subsequent operate, enable you to debug a difficult error, or clarify a fancy piece of legacy code. The extra you utilize it, the extra you’ll uncover its capabilities.

Shittu Olumide is a software program engineer and technical author enthusiastic about leveraging cutting-edge applied sciences to craft compelling narratives, with a eager eye for element and a knack for simplifying advanced ideas. You may also discover Shittu on Twitter.