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# Introduction
Working a top-performing AI mannequin domestically 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 learn to run Qwen3.5 domestically utilizing Ollama and join it to OpenCode to create a easy native agentic setup. The aim is to maintain the whole lot simple, accessible, and beginner-friendly, so you may get a working native AI assistant with out coping with a sophisticated stack.
# Putting in Ollama
Step one is to put in Ollama, which makes it straightforward to run giant language fashions domestically 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 observe the steps there in case you are utilizing a unique 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
Normally, Ollama begins mechanically after set up, particularly once you launch it for the primary time. Which means chances are you’ll not have to do the rest earlier than working a mannequin domestically.
If the Ollama server shouldn’t be already working, you can begin it manually with the next command:
# Working Qwen3.5 Regionally
As soon as Ollama is working, 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 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 gives an excellent stability between efficiency and {hardware} necessities. It’s a sensible selection for older laptops and sometimes 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 may occasionally take a couple of minutes.
After the obtain finishes, Ollama might take a second to load the mannequin and put together the whole lot wanted to run it domestically. As soon as prepared, you will note 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 assessments, 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 working by yourself machine.
You’ll be able to 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 strategy 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 do not need to configure the whole lot 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 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 obtainable Qwen3.5 mannequin. After it runs, you can be taken into the OpenCode interface with Qwen3.5 4B already related and able to use.
# Constructing a Easy Python Venture with Qwen3.5
As soon as OpenCode is working 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 working.
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 outcome 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, once you enter an accurate character, the sport instantly reveals the matching letter within the hidden phrase, displaying that the logic works correctly proper out of the field.
# Closing Ideas
I used to be genuinely impressed by how straightforward it’s to get an area agentic setup working on an older laptop computer with Ollama, Qwen3.5, and OpenCode. For a light-weight, low-cost setup, it really works surprisingly effectively and makes native AI really feel way more sensible than many individuals anticipate.
That stated, it isn’t all clean 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 complicated coding duties. In my expertise, it could deal with easy tasks, fundamental scripting, analysis assist, and general-purpose duties fairly effectively, however it begins to wrestle 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 way of a job. 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 an authorized information scientist skilled who loves constructing machine studying fashions. At the moment, 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 students scuffling with psychological sickness.
