Picture by Creator
# Introduction
Working a top-performing AI mannequin domestically now not requires a high-end workstation or costly cloud setup. With light-weight instruments and smaller open-source fashions, now you can flip even an older laptop computer right into a sensible native AI setting for coding, experimentation, and agent-style workflows.
On this tutorial, you’ll learn to run Qwen3.5 domestically utilizing Ollama and join it to OpenCode to create a easy native agentic setup. The purpose is to maintain all the things simple, accessible, and beginner-friendly, so you will get a working native AI assistant with out coping with an advanced stack.
# Putting in Ollama
Step one is to put in Ollama, which makes it simple to run massive language fashions domestically in your machine.
In case you are utilizing Home windows, you may both obtain Ollama immediately from the official Obtain Ollama on Home windows web page and set up it like another utility, or run the next command in PowerShell:
irm https://ollama.com/set up.ps1 | iex
The Ollama obtain web page additionally contains set up directions for Linux and macOS, so you may comply with the steps there in case you are utilizing a special working system.
As soon as the set up is full, you can be prepared to start out Ollama and pull your first native mannequin.
# Beginning Ollama
Generally, Ollama begins robotically after set up, particularly whenever you launch it for the primary time. Meaning it’s possible you’ll not have to do the rest earlier than operating a mannequin domestically.
If the Ollama server shouldn’t be already operating, you can begin it manually with the next command:
# Working Qwen3.5 Domestically
As soon as Ollama is operating, the subsequent step is to obtain and launch Qwen3.5 in your machine.
If you happen to go to the Qwen3.5 mannequin web page in Ollama, you will note a number of mannequin sizes, starting from bigger variants to smaller, extra light-weight choices.
For this tutorial, we are going to use the 4B model as a result of it affords an excellent stability between efficiency and {hardware} necessities. It’s a sensible selection for older laptops and usually requires round 3.5 GB of random entry reminiscence (RAM).
To obtain and run the mannequin out of your terminal, use the next command:
The primary time you run this command, Ollama will obtain the mannequin information to your machine. Relying in your web pace, this will take a couple of minutes.
After the obtain finishes, Ollama might take a second to load the mannequin and put together all the things wanted to run it domestically. As soon as prepared, you will note an interactive terminal chat interface the place you may start prompting the mannequin immediately.
At this level, you may already use Qwen3.5 within the terminal for easy native conversations, fast checks, and light-weight coding assist earlier than connecting it to OpenCode for a extra agentic workflow.
# Putting in OpenCode
After organising Ollama and Qwen3.5, the subsequent step is to put in OpenCode, an area coding agent that may work with fashions operating by yourself machine.
You possibly can go to the OpenCode web site to discover the out there set up choices and be taught extra about the way it works. For this tutorial, we are going to use the fast set up technique as a result of it’s the easiest option to get began.
Run the next command in your terminal:
curl -fsSL https://opencode.ai/set up | bash
This installer handles the setup course of for you and installs the required dependencies, together with Node.js when wanted, so that you don’t have to configure all the things manually.
# Launching OpenCode with Qwen3.5
Now that each Ollama and OpenCode are put in, you may join OpenCode to your native Qwen3.5 mannequin and begin utilizing it as a light-weight coding agent.
If you happen to take a look at the Qwen3.5 web page in Ollama, you’ll discover that Ollama now helps easy integrations with exterior AI instruments and coding brokers. This makes it a lot simpler to make use of native fashions in a extra sensible workflow as an alternative of solely chatting with them within the terminal.
To launch OpenCode with the Qwen3.5 4B mannequin, run the next command:
ollama launch opencode –model qwen3.5:4b
This command tells Ollama to start out OpenCode utilizing your domestically out there Qwen3.5 mannequin. After it runs, you can be taken into the OpenCode interface with Qwen3.5 4B already linked and able to use.
# Constructing a Easy Python Venture with Qwen3.5
As soon as OpenCode is operating with Qwen3.5, you can begin giving it easy prompts to construct software program immediately out of your terminal.
For this tutorial, we requested it to create a small Python sport venture from scratch utilizing the next immediate:
Create a brand new Python venture and construct a contemporary Guess the Phrase sport with clear code, easy gameplay, rating monitoring, and an easy-to-use terminal interface.
After a couple of minutes, OpenCode generated the venture construction, wrote the code, and dealt with the setup wanted to get the sport operating.
We additionally requested it to put in any required dependencies and check the venture, which made the workflow really feel a lot nearer to working with a light-weight native coding agent than a easy chatbot.
The ultimate end result was a totally working Python sport that ran easily within the terminal. The gameplay was easy, the code construction was clear, and the rating monitoring labored as anticipated.
For instance, whenever you enter an accurate character, the sport instantly reveals the matching letter within the hidden phrase, exhibiting that the logic works correctly proper out of the field.
# Closing Ideas
I used to be genuinely impressed by how simple it’s to get an area agentic setup operating on an older laptop computer with Ollama, Qwen3.5, and OpenCode. For a light-weight, low-cost setup, it really works surprisingly nicely and makes native AI really feel way more sensible than many individuals count on.
That stated, it isn’t all easy crusing.
As a result of this setup depends on a smaller and quantized mannequin, the outcomes will not be all the time robust sufficient for extra advanced coding duties. In my expertise, it will possibly deal with easy initiatives, primary scripting, analysis assist, and general-purpose duties fairly nicely, but it surely begins to battle when the software program engineering work turns into extra demanding or multi-step.
One difficulty I bumped into repeatedly was that the mannequin would generally cease midway by means of a process. When that occurred, I needed to manually sort proceed to get it to maintain going and end the job. That’s manageable for experimentation, but it surely does make the workflow much less dependable whenever you need constant output for bigger coding duties.
Abid Ali Awan (@1abidaliawan) is a licensed information scientist skilled who loves constructing machine studying fashions. Presently, he’s specializing in content material creation and writing technical blogs on machine studying and information science applied sciences. Abid holds a Grasp’s diploma in know-how administration and a bachelor’s diploma in telecommunication engineering. His imaginative and prescient is to construct an AI product utilizing a graph neural community for college kids scuffling with psychological sickness.
