Local LLM Coding Setup: LMStudio + OpenCode

A guide to setting up a local AI coding assistant using LMStudio and OpenCode — a solid alternative to Claude Code when you run out of daily usage.

1. Install LMStudio

Download and install from https://lmstudio.ai/

2. Select a Model

Choose an appropriate model depending on your hardware. In this case, I chose Qwen3-Coder-Next-MLX-6bit because:

• It fits within my available RAM


• It's optimized for macOS with Apple Silicon (M4 chip)


• It can leverage the M4 GPU

You may need to wait a bit for the model to fully download.

3. Load and Configure the Model

Load the model you selected in Step 2 (e.g., Qwen3-Coder-Next-MLX-6bit) and configure the following:

⚠️ Do not leave the context length at the default 16000 — it's too small.

4. Install OpenCode

Install from https://opencode.ai/

5. Configure OpenCode to Use LMStudio

Open the config file at ~/.config/opencode/opencode.jsonc and paste the following:

{

  "$schema": "https://opencode.ai/config.json",

  "theme": "tokyonight",

  "disabled_providers": [],

  "provider": {

    "localllm": {

      "name": "Local LLM",

      "npm": "@ai-sdk/openai-compatible",

      "models": {

        "qwen3-coder-next-mlx": {

          "name": "Qwen3-Coder-Next"

        }

      },

      "options": {

        "baseURL": "http://127.0.0.1:1234/v1"

      }

    }

  }

}

⚠️ Make sure the key "qwen3-coder-next-mlx" matches the model name in LMStudio exactly, otherwise you'll get an error: "can not load model..."

6. Run OpenCode

Open a new terminal, navigate to your project directory, and run:

opencode

E.g: "help me to understand the code base", while opencode running, you can watch out LMstudio server log to see it really works

7. Results

Tested with a demo project and the results are not bad at all compared to Sonnet 4.5. More testing on larger projects is needed, but the output quality makes it a worthwhile alternative:

• 🔄 Use as a fallback when you run out of daily Claude Code usage


• 💡 Explore other use cases where a local LLM fits your workflow


• 💰 Zero API cost — everything runs locally