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# Introduction
Working a top-performing AI mannequin regionally 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 surroundings for coding, experimentation, and agent-style workflows.
On this tutorial, you’ll discover ways to run Qwen3.5 regionally utilizing Ollama and join it to OpenCode to create a easy native agentic setup. The aim is to maintain every thing easy, 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 giant language fashions regionally in your machine.
If you’re utilizing Home windows, you’ll be able to both obtain Ollama immediately from the official Obtain Ollama on Home windows web page and set up it like every other 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’ll be able to comply with the steps there if you’re utilizing a distinct working system.
As soon as the set up is full, you can be prepared to begin Ollama and pull your first native mannequin.
# Beginning Ollama
Most often, Ollama begins robotically after set up, particularly once you launch it for the primary time. Meaning it’s possible you’ll not must do anything earlier than operating a mannequin regionally.
If the Ollama server isn’t already operating, you can begin it manually with the next command:
# Working Qwen3.5 Regionally
As soon as Ollama is operating, the subsequent step is to obtain and launch Qwen3.5 in your machine.
Should you go to the Qwen3.5 mannequin web page in Ollama, you will notice 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 presents an excellent steadiness between efficiency and {hardware} necessities. It’s a sensible alternative 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 velocity, this will likely take a couple of minutes.
After the obtain finishes, Ollama might take a second to load the mannequin and put together every thing wanted to run it regionally. As soon as prepared, you will notice an interactive terminal chat interface the place you’ll be able to start prompting the mannequin immediately.
At this level, you’ll be able to 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, a neighborhood coding agent that may work with fashions operating by yourself machine.
You possibly can go to the OpenCode web site to discover the obtainable set up choices and study extra about the way it works. For this tutorial, we are going to use the fast set up methodology as a result of it’s the easiest approach 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 would not have to configure every thing manually.
# Launching OpenCode with Qwen3.5
Now that each Ollama and OpenCode are put in, you’ll be able to join OpenCode to your native Qwen3.5 mannequin and begin utilizing it as a light-weight coding agent.
Should you 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 a substitute 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 begin OpenCode utilizing your regionally obtainable 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 recreation mission from scratch utilizing the next immediate:
Create a brand new Python mission and construct a contemporary Guess the Phrase recreation with clear code, easy gameplay, rating monitoring, and an easy-to-use terminal interface.
After a couple of minutes, OpenCode generated the mission 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 take a look at the mission, which made the workflow really feel a lot nearer to working with a light-weight native coding agent than a easy chatbot.
The ultimate outcome was a totally working Python recreation that ran easily within the terminal. The gameplay was easy, the code construction was clear, and the rating monitoring labored as anticipated.
For instance, once 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.
# Last Ideas
I used to be genuinely impressed by how simple it’s to get a neighborhood 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 rather more sensible than many individuals count on.
That mentioned, it isn’t all easy crusing.
As a result of this setup depends on a smaller and quantized mannequin, the outcomes should not at all times sturdy sufficient for extra advanced coding duties. In my expertise, it may possibly deal with easy tasks, fundamental scripting, analysis assist, and general-purpose duties fairly nicely, however it begins to wrestle when the software program engineering work turns into extra demanding or multi-step.
One concern I bumped into repeatedly was that the mannequin would typically cease midway by a activity. When that occurred, I needed to manually sort proceed to get it to maintain going and end the job. That’s manageable for experimentation, however it does make the workflow much less dependable once 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 expertise 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 combating psychological sickness.
