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Yes—you can run a useful AI coding assistant on your own computer without recurring API charges. The practical arrangement is LM Studio as the local model runtime, OpenCode as the coding agent, and VS Code as the editor. LM Studio stores and serves the model, OpenCode reads the repository and performs agent tasks, and VS Code remains the place where you inspect and edit code. For an integrated experience, the LM Studio Code extension adds an OpenCode-powered panel inside VS Code.
This setup can keep source code on your machine while the model and API are local, but “free” applies to the software, not to the computer, storage, electricity, or model downloads. Performance depends heavily on model size, quantization, context length, and whether inference uses your CPU, integrated graphics, or a discrete GPU.
How the local VS Code coding stack fits together
These tools solve different parts of the problem, so installing only one of them does not create a complete coding assistant.
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| Component | Job | What it needs to know |
|---|---|---|
| LM Studio | Downloads model files, loads a model, and exposes a local API compatible with common OpenAI-style clients. | Your hardware, model choice, context setting, and server settings. |
| OpenCode | Acts as the open-source coding agent. It can inspect a repository, propose or apply edits, run commands when permitted, and maintain project guidance. | The project directory, provider configuration, model endpoint, and your permissions. |
| VS Code | Provides the editor, terminal, source control, extensions, and visual review of changes. | The same local project folder used by OpenCode. |
| LM Studio Code extension | Embeds an OpenCode-powered agent panel in VS Code so you can work without switching to a separate terminal window. | LM Studio models and the extension’s current connection settings. |
OpenCode describes itself as an open-source AI coding agent and advertises support for more than 75 LLM providers (the provider count shown by its official page in 2026). That does not mean every provider is local: choose the LM Studio provider path when your goal is local inference, and do not enable a cloud fallback unless you deliberately accept that data may leave the computer.
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Hardware and software requirements
Start with a machine that can hold the model in memory while leaving room for the operating system, VS Code, your repository, and the agent’s context. Dell Technologies says systems with 10GB or more of RAM should run quantized 3B–7B models comfortably. Dell does not present that figure as a speed guarantee, and the guide has no clear publication date; treat it as a baseline for experimentation rather than a buying specification.
| Requirement | Practical guidance |
|---|---|
| RAM | 10GB or more is a cited baseline for quantized 3B–7B models. A 16GB RAM laptop is a more useful starting category for running VS Code, LM Studio, and OpenCode together. |
| CPU/GPU | Faster CPU cores reduce generation latency on CPU inference. A supported discrete GPU can improve throughput, but the exact benefit depends on model format, drivers, and available VRAM. |
| Storage | Reserve space for the model file, additional quantized variants, caches, your repository, and operating-system updates. The exact model download size is not fixed because it varies by model and quantization. |
| Operating system | Install versions of LM Studio, VS Code, and OpenCode supported by your operating system. The exact compatibility matrix changes, so use the current installers and documentation for your platform. |
| Network access | Internet access is needed to download LM Studio, OpenCode, the model, and the VS Code extension. After those pieces are installed, inference can remain local if the selected integration does not call a remote service. |
When shopping, do not compare RAM alone. Check CPU generation, memory bandwidth, thermal limits, GPU and VRAM, storage capacity, operating-system support, and whether memory can be upgraded. A machine that technically loads a model may still feel slow during long-context or tool-heavy sessions.
Step 1: Install LM Studio and download a coding model
- Install LM Studio from its official distribution. Use the installer for your operating system and launch the application.
- Open the model catalog. Search for a model intended for code generation and instruction following. Prefer a quantized build that fits your available memory rather than choosing the largest model listed.
- Download the model. Keep the model on a local drive with enough free space for the file and future updates. If you plan to test several models, account for each file separately.
- Load the model. In LM Studio, select the downloaded model and start it. The current LM Studio build shows the controls for context length, GPU offload, and other runtime choices; labels and locations can change between releases.
For a first run, use a moderate context length and the model’s recommended defaults. Increasing context consumes more memory and can reduce speed. A smaller coding model that responds promptly is often more useful for navigation, edits, and quick explanations than a larger model that repeatedly runs out of memory.
Step 2: Start LM Studio’s local API server
- Open LM Studio’s developer or server view.
- Choose the model you loaded and start the local server.
- Record the base URL, port, and model identifier displayed by the current build.
- Leave the server running while OpenCode or the VS Code extension uses it.
Do not hard-code an endpoint from an old tutorial. LM Studio’s UI labels and endpoint details can change, so copy the connection information shown in your installation. OpenCode needs a local provider entry that points to this server and identifies the model. If a client asks for an API key even though the server is local, use the value required by that client’s current configuration rather than assuming that every local setup uses authentication.
Step 3: Install and launch OpenCode
Open a terminal and install OpenCode with its official installer:
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curl -fsSL https://opencode.ai/install | bashUse a supported package manager instead if that is the normal installation method for your operating system. After installation, change to the repository you want the agent to work on and launch it:
cd path/to/your-project
opencodeRun the command from the repository root. That gives the agent a consistent view of source files, configuration, tests, and version-control metadata. If your shell cannot find opencode after installation, restart the terminal or add the install directory to your user PATH, then verify the command again.
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OpenCode supports local models and multiple provider backends. In its provider configuration, create or select the LM Studio local provider and enter the base URL, port, and model identifier copied from LM Studio. The exact file location, property names, and UI labels are version-sensitive, so follow the provider configuration screen or the current OpenCode documentation bundled with your release.
- Keep LM Studio’s server running.
- Open OpenCode’s provider or model settings.
- Select the local or OpenAI-compatible provider option that matches LM Studio.
- Enter the local server address and the exact model name exposed by LM Studio.
- Save the configuration and send a small test request, such as asking the agent to list the top-level files.
A successful test should return a response without a request appearing in a cloud provider dashboard. If OpenCode reports that the model is unavailable, compare the identifier character-for-character with LM Studio’s server display. If it reports a connection failure, confirm that the server is started, the port is unchanged, and local firewall rules allow the application to communicate with the loopback address.
Step 5: Initialize project guidance with /init
Inside the OpenCode session, run:
/initOpenCode analyzes the repository and creates an AGENTS.md file containing project guidance. Review the generated file before relying on it. Correct inaccurate commands, build instructions, directory conventions, and testing rules. Commit AGENTS.md when it represents durable guidance that should be shared with collaborators; keep it uncommitted when it contains only personal notes or machine-specific instructions.
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Use a small, representative task for the first real request: ask the agent to explain one module, identify the tests for a function, or propose a narrowly scoped change. Confirm that it can read the expected files before granting permission for edits or shell commands.
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For an in-editor workflow, install the LM Studio Code extension from the VS Code Marketplace. Its listing says the extension can read and edit files, run shell commands, and ask for permission while using LM Studio models locally.
- Open VS Code and choose Extensions from the Activity Bar.
- Search for LM Studio Code and install the extension published under that name.
- Open the project folder that you used with OpenCode.
- Open the extension’s agent panel and select the LM Studio-backed model or connection.
- Approve file and terminal permissions deliberately, starting with read-only or narrowly scoped requests.
The extension is an additional interface, not a second model runtime. LM Studio still needs to have the model loaded and its local server available. If the panel cannot connect, test the same model from the OpenCode terminal first; that separates a provider problem from an extension problem.
How to use the assistant safely and effectively
Begin with inspection, not a broad rewrite
Ask for a file tree, a description of the relevant module, or a test plan before requesting changes. This gives you a chance to catch a wrong working directory, missing dependencies, or an incorrect interpretation of the codebase.
Give the agent an acceptance test
State the behavior that must change, the files that are in scope, and the command that should pass afterward. For example, specify the endpoint behavior, the expected error response, and the test command. A local model has no implicit knowledge of your team’s conventions beyond the repository and the instructions you provide.
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Review every diff
Use VS Code’s source-control view or your normal version-control commands to inspect changes. Check imports, error handling, migrations, generated files, and secrets. Run tests and linters yourself even when the agent reports success.
Control shell commands
OpenCode and the VS Code extension may request permission to run shell commands. Allow harmless inspection and test commands when you understand their effects. Refuse commands that delete files, modify production resources, upload data, install untrusted software, or expose credentials unless you have independently verified them.
Local privacy, offline operation, and cost boundaries
LM Studio states that it is free for home and work use. OpenCode is open source. With the model files, server, and provider connection all kept on the computer, source code can avoid being sent to a cloud AI provider. That is a deployment property, not an automatic guarantee for every extension or configuration.
- Download the model and extension before disconnecting from the internet.
- Check that the provider points to a local address rather than a hosted endpoint.
- Disable optional cloud fallback if the integration offers it.
- Remember that telemetry, extension updates, package managers, and shell commands can still make network requests independently of model inference.
- Keep proprietary repositories, credentials, and regulated data out of prompts unless your organization’s policy explicitly permits them.
There is no recurring model API charge in a local-only arrangement, but the computer, electricity, storage, and download bandwidth still have costs. Large models also increase heat, fan noise, and battery drain on laptops.
Choosing between terminal, VS Code, and cloud workflows
| Workflow | Best for | Trade-offs |
|---|---|---|
| OpenCode in a terminal | Users who want explicit project-root control, visible command output, and a workflow that works without an editor extension. | Requires switching between the terminal and editor; permissions and diffs are still your responsibility. |
| LM Studio Code extension in VS Code | Users who want chat, file edits, and approvals beside the code they are reviewing. | Adds an extension layer whose labels and connection behavior can change; the LM Studio server must still be available. |
| Cloud coding assistant | Users who prioritize access to larger hosted models and do not need source code to remain local. | Usually introduces recurring usage costs and sends prompts or code to a remote provider under that provider’s terms. |
| Local model with cloud fallback | Users who want local operation for routine tasks but a remote option for difficult requests. | Privacy is no longer local-only; configure clear boundaries and know when fallback is triggered. |
The local stack is strongest when privacy, predictable recurring cost, and offline capability matter more than maximum model quality. Cloud models generally have an advantage on very large repositories or difficult reasoning tasks, while local performance is constrained by your machine.
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Performance tuning without guesswork
Match model size to available memory
Quantization reduces memory use, allowing a smaller machine to load a model. It can also affect output quality. If the model fails to load, reduce model size or quantization memory requirements before changing unrelated settings.
Keep context realistic
A long context window lets the agent see more files, but it consumes memory even when the prompt is short. Start with the model’s recommended context and increase it only when a task genuinely needs more repository history.
Separate latency from quality
Measure usefulness by completed tasks, not just tokens per second. A fast model that misses project conventions can require more correction than a slower model that produces a clean first draft. For routine navigation and small edits, responsiveness usually matters more than maximum parameter count.
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Watch thermals and battery behavior
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Troubleshooting checklist
OpenCode cannot connect
- Confirm LM Studio has loaded the model and started its server.
- Copy the current base URL, port, and model identifier again.
- Check that OpenCode is using the LM Studio provider rather than a similarly named cloud provider.
- Temporarily test with a short prompt and a smaller context setting.
The model loads but responses are extremely slow
- Use a smaller or more aggressively quantized coding model.
- Lower the context length.
- Check whether GPU offload is available and compatible with your drivers.
- Close memory-heavy applications, including unused browser tabs and additional development tools.
The VS Code panel is unavailable
- Verify that the LM Studio Code extension is installed and enabled in the current workspace.
- Test the connection from OpenCode in the terminal.
- Restart the extension or VS Code after changing the LM Studio server.
- Recheck the extension’s current model and provider selection rather than assuming it inherited the terminal configuration.
Edits are unsafe or unrelated
- Stop the session and inspect the diff.
- Restore the repository to a known state if necessary.
- Run
/initagain only after correcting project instructions; do not blindly accept a generatedAGENTS.md. - Break the request into smaller tasks with explicit files, tests, and acceptance criteria.
A repeatable daily workflow
- Start LM Studio and load the model that fits the current task.
- Start the local API server and verify its displayed connection details.
- Open the repository in VS Code and, if needed, launch OpenCode from that same root.
- Ask the agent to inspect the relevant code and propose a plan.
- Approve only the files and commands required for the task.
- Review the diff in VS Code, run tests and linters, and fix failures with narrowly scoped follow-up prompts.
- Commit code and durable project guidance separately when that makes review clearer.
This process preserves the convenience of an AI pair programmer while keeping the model, source code, and final decisions under your control.
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