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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchYes—you can run a coding assistant locally in VS Code without recurring model API charges. Use LM Studio to download and serve a coding model, OpenCode as the agent that reads and edits your repository, and VS Code as the editor. You can work from OpenCode in a terminal or install the LM Studio Code extension for an OpenCode-powered panel inside VS Code. The software can be free, but the computer, storage, electricity and model download are not.
How the local setup fits together
This workflow has three separate jobs. Keeping them distinct makes installation and troubleshooting much easier.
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LM Studio: the local model runtime
LM Studio stores the model files on your computer, loads a selected model into memory and exposes a local REST API, including OpenAI-compatible API behavior. It is the component that performs inference. You choose the model and start its local server in LM Studio; clients then connect to the address and model identifier shown by your installed build.
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OpenCode: the coding agent
OpenCode is an open-source AI coding agent. It runs in a project directory, inspects files, proposes or applies edits and can use shell commands when you allow them. Its agent loop is different from a simple chat window: it keeps project context, performs multi-step work and can initialize repository guidance with /init.
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VS Code: the editing surface
VS Code remains the place where you inspect diffs, edit files and run your normal development tools. You can launch OpenCode in VS Code’s integrated terminal, or use the LM Studio Code extension, whose Marketplace listing describes an OpenCode-powered panel that can read and edit files, run shell commands and ask for permission while using LM Studio models locally.
What “free” and “local” actually mean
LM Studio states that it is free for home and work use. OpenCode is open source. When the model files and API server stay on your computer, source code can avoid being sent to a hosted model provider, and you can avoid recurring API charges. That does not make the setup costless: you still need suitable hardware, disk space for model files, electricity and an internet connection for the initial software and model downloads.
Local also describes a configuration, not a guarantee about every integration. A client, extension or provider setting may be able to use a cloud fallback, telemetry or an online account. Before handling sensitive code, inspect the selected provider, disable fallback options you do not want and test with networking disabled. Confirm that the model is already downloaded and that the client can reach the local server without an external service.
Hardware and operating requirements
Memory is the first practical constraint, but it is not the only one. Model parameter count, quantization level, context-window length, CPU or GPU acceleration, memory bandwidth, thermals and storage all affect speed and whether an agent can complete a long task.
| Guideline | What it means for this workflow |
|---|---|
| 10GB or more of RAM | Dell Technologies says systems with 10GB+ RAM should run quantized 3B–7B models comfortably. Treat this as an experimentation baseline, not a promise of response speed or coding quality. |
| 16GB RAM laptop | A useful task-enabling shopping category for a general local-assistant setup. Compare CPU or GPU, memory bandwidth, thermals, storage, operating-system support and upgradeability before buying. |
| Larger models or long contexts | Expect substantially higher memory demand. Tool-heavy agent sessions and large repositories can exceed a small laptop’s comfortable limits even when a short chat works. |
You need a supported desktop operating system for LM Studio and VS Code, enough free storage for the model files and permission to run a local server. The exact model formats, acceleration backends and operating-system support depend on the current LM Studio build and the model you select. Start with a smaller quantized coding model if you are testing the workflow on modest hardware.
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Step 1: Install LM Studio and download a coding model
- Install LM Studio from its official distribution channel for your operating system.
- Open the model catalog and search for a model intended for code generation or software development. Check the model’s parameter size and quantization before downloading; those choices determine memory use and often affect speed.
- Download one model to local storage. Keep the model identifier visible because OpenCode will need the exact identifier when it connects.
- Load the downloaded model in LM Studio. If loading fails, select a smaller quantization or close other memory-intensive applications before trying again.
Do not assume that a model described as “coding” will perform equally well on every language, repository size or tool-use task. Begin with a small, representative project and evaluate whether it follows instructions, preserves formatting and produces tests that make sense for your codebase.
Step 2: Start LM Studio’s local API server
- In LM Studio, open the area for serving or running a local model and start the local API server.
- Copy the base URL, port, API style and model identifier displayed by your current LM Studio build.
- Leave the model loaded while OpenCode or the VS Code extension is using it. Stopping the server, unloading the model or changing the port will interrupt clients.
LM Studio’s documentation covers local REST and OpenAI-compatible APIs, SDKs, offline operation and the lms command-line interface. Use the values shown in your installation instead of copying an old tutorial’s endpoint or menu name: labels and endpoint paths can change between releases.
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From a terminal, install OpenCode with its official installer:
curl -fsSL https://opencode.ai/install | bashYou can also use a package manager supported by the current OpenCode documentation. After installation, open a terminal in the repository you want the agent to work on and run:
opencodeLaunching it from the repository root matters. The agent can then see the intended project structure, instructions and version-control context rather than treating a parent directory as the workspace.
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Step 4: Connect OpenCode to LM Studio
OpenCode supports local models and multiple provider backends. In OpenCode’s provider configuration, add or select the LM Studio local provider, then enter the base URL and model identifier that LM Studio displays. If the configuration asks for an API-compatible mode, choose the local REST or OpenAI-compatible option exposed by your LM Studio server.
Do not hard-code an endpoint copied from an outdated guide. LM Studio builds can change hostnames, ports, route names and UI labels. The reliable sequence is to start the server, copy its current connection details and then enter those values in OpenCode. Save the configuration and send a short test request before attempting a repository-wide change.
A successful test should return a model response without a cloud account or a remote provider key. If OpenCode reports that no model is available, compare the configured model identifier character-for-character with the identifier shown by LM Studio; a display name and an internal identifier are not always the same.
Step 5: Initialize project guidance with /init
Inside the running OpenCode session, enter:
/initOpenCode analyzes the project and creates an AGENTS.md file. This file gives the agent durable instructions about the repository, such as build commands, test expectations, style rules and directories it should avoid changing.
Review the generated file as ordinary source control content. Correct inaccurate assumptions, remove instructions that expose secrets and add commands that a new contributor would genuinely need. Commit AGENTS.md when its guidance is part of the project’s long-term workflow; leave it uncommitted if it contains temporary, personal instructions.
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Step 6: Use the assistant from VS Code
Terminal-first workflow
- Open the repository folder in VS Code.
- Open the integrated terminal and run
opencodefrom the repository root. - Ask for one bounded task, such as explaining a failing test or adding a test for a named function.
- Inspect the proposed diff in VS Code before accepting edits.
- Run the project’s formatter, type checker and tests yourself, then give OpenCode the exact failure output if another iteration is needed.
This approach keeps the agent’s command execution visible in the terminal while VS Code provides navigation, search, source control and debugging.
In-editor panel workflow
- Open the VS Code Marketplace and install the LM Studio Code extension.
- Start the LM Studio model and local server first.
- Open the extension’s panel and select the local LM Studio model or connection details requested by the current extension version.
- When the panel asks to read files, edit files or run a shell command, review the requested scope and grant only the permission needed for that step.
- Use VS Code’s source-control view to inspect every change before committing.
The extension provides convenience, not automatic trust. A panel that can run shell commands can also invoke destructive commands if you approve them. Keep the permission prompts enabled, work on a branch or disposable checkout and avoid granting a broad command request when a narrower command will do.
Choosing between terminal and in-editor control
| Decision point | OpenCode in a terminal | LM Studio Code extension in VS Code |
|---|---|---|
| Control surface | Explicit terminal session in the repository root | Integrated agent panel alongside the editor |
| File work | Agent reads and edits through its normal workflow; you inspect results in VS Code | Panel can read and edit files, with permission prompts |
| Shell commands | Visible in the terminal and subject to the agent’s approval flow | Panel can request permission before running commands |
| Best fit | Users who want a transparent, scriptable agent loop | Users who want conversation, files and editor navigation in one window |
| Privacy mode | Can remain local when configured only for the LM Studio provider | Can use local models, but verify extension and provider settings before assuming every path is offline |
Both interfaces use the same fundamental constraint: the quality of the result depends on the local model and the context it can fit, not just on the editor surface.
How to work effectively with a local coding model
Start with bounded tasks
Ask for a single change with a clear file or symbol, acceptance criteria and a command for validation. “Find and fix the null handling in parseConfig, add a regression test and run the parser tests” gives the agent a more useful target than “improve this project.”
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When a test or build fails, provide the exact command and output. Avoid paraphrasing a stack trace; missing line numbers or error codes can send a smaller local model down the wrong path.
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Control context growth
Long conversations consume the model’s context window. Start a new session for an unrelated task, summarize a completed investigation in AGENTS.md or a project note and ask the agent to inspect only the files relevant to the current change. Large context windows also increase memory use.
Keep a human review gate
Review diffs, dependency changes, generated files and shell commands. Run tests independently and check security-sensitive code—authentication, authorization, cryptography, database migrations and file deletion—rather than accepting a plausible-looking answer.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Offline operation and privacy boundaries
After the software and model are downloaded, LM Studio can serve the model locally without sending prompts to a hosted inference service. OpenCode can then connect to that local endpoint. To make that claim meaningful, verify all three layers: the model is stored locally, OpenCode’s selected provider points to the local server and the VS Code extension is not configured with a cloud fallback.
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Initial installation and model downloads require network access. Updates, extension installation, documentation lookup and any tool that deliberately calls an external service also require network access. A machine disconnected from the internet may still be able to code with an already downloaded model, but only after you have tested the exact configuration in advance.
Cost, capacity and trade-offs
- Software cost: LM Studio is free for home and work use; OpenCode is open source.
- Usage cost: Local inference can remove recurring per-request API charges.
- Hardware cost: RAM, CPU or GPU capability, storage and cooling determine the practical model size and speed.
- Operating cost: Electricity and the time spent downloading, updating and maintaining models still count.
- Quality trade-off: A larger or better coding model may produce stronger answers but needs more memory and can respond more slowly on CPU-only hardware.
- Privacy trade-off: Keeping inference local can reduce source-code exposure, while a cloud fallback may improve capability but changes the privacy boundary.
Troubleshooting common failures
OpenCode cannot connect
- Confirm that LM Studio’s server is running and the model is loaded.
- Copy the current host, port and route from LM Studio again; do not rely on a stale tutorial.
- Check that a local firewall is not blocking the chosen port.
- Verify that OpenCode is using the LM Studio provider rather than a different default provider.
The model will not load
- Close other memory-heavy applications.
- Choose a smaller parameter count or a more memory-efficient quantization.
- Reduce the context length if LM Studio exposes that control.
- Remember that a 10GB+ baseline applies to quantized 3B–7B experimentation, not to every model or session.
Responses are too slow
- Use a smaller model or shorter context.
- Check whether the model is running on the intended CPU or GPU backend.
- Stop unused applications and watch for thermal throttling during long sessions.
- Limit tool calls by asking for a narrow task and a specific validation command.
The agent changes the wrong files
- Run OpenCode from the repository root.
- Inspect and correct
AGENTS.md. - State the allowed files, forbidden directories and acceptance tests in the prompt.
- Review the diff before granting further permissions or committing.
Offline mode fails unexpectedly
- Check for a configured cloud provider or fallback.
- Confirm that the model was fully downloaded before disconnecting.
- Test the local endpoint directly through the client configuration while online, then repeat with networking disabled.
- Expect extension updates, documentation requests and other external tools to stop working offline.
A practical first-session checklist
- LM Studio is installed and a quantized coding model is downloaded.
- The model is loaded and the local API server is running.
- The host, port, API mode and model identifier are copied from the current LM Studio interface.
- OpenCode is installed and launched from the repository root.
- OpenCode’s provider points to LM Studio’s local endpoint.
/inithas been reviewed and the resultingAGENTS.mdis either committed intentionally or kept local.- A harmless test request succeeds before any broad edit.
- Permission prompts remain enabled for file edits and shell commands.
- VS Code’s diff, formatter, type checker and test commands are available for independent verification.
What this architecture is best for
This arrangement is strongest when you value source-code locality, predictable recurring cost and control over the agent’s permissions. It is a sensible way to explain an unfamiliar codebase, draft tests, perform repetitive refactors and iterate on errors while keeping the editor you already use.
It is less suitable when you need the very strongest model, huge repository-wide context or consistently fast responses on hardware with limited memory. In those cases, a local model may still be useful for routine work while a separately approved hosted service handles exceptional tasks. Keep that fallback explicit so you know exactly which prompts and files leave the machine.
The Bottom Line
Install LM Studio, load a quantized coding model, start its local API, point OpenCode at the displayed endpoint, and run the agent from your repository or through the LM Studio Code extension in VS Code. A 10GB-plus system is a reasonable baseline for experimenting with quantized 3B–7B models, while 16GB RAM gives a more practical starting point. Local inference can avoid recurring API fees and reduce code exposure, but performance, privacy and offline behavior depend on your model, hardware and provider settings.
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