Overview
Cline has published a step-by-step guide for running its coding agent completely offline on a laptop. The setup pairs LM Studio as the local runtime with the Qwen3 Coder 30B model, while Cline handles orchestration inside VS Code. According to the post (published August 28, 2025), this configuration supports repository analysis, code generation, and terminal command execution without sending data to external services or incurring API usage.
The model choice is central. Qwen3 Coder 30B is cited for its 256k native context and tool-use capabilities. On Apple Silicon, an MLX build is recommended; on Windows and other platforms, GGUF builds are available. The guide emphasizes operating entirely on local hardware and configuring both LM Studio and Cline to match the model’s long context.
Requirements
- LM Studio for model hosting and inference: https://lmstudio.ai/?ref=cline.ghost.io
- Cline for VS Code
- Qwen3 Coder 30B model: https://lmstudio.ai/models/qwen/qwen3-coder-30b?ref=cline.ghost.io
The LM Studio interface will recommend MLX on Mac and GGUF on Windows. The guide notes both formats work; selection depends on hardware and platform.
LM Studio setup
The walkthrough instructs loading “Qwen3 Coder 30B A3B Instruct” in LM Studio and starting the local server. The default endpoint is http://127.0.0.1:1234/?ref=cline.ghost.io.
Configuration highlights:
- Context Length: 262,144 (matching the model’s 256k maximum)
- KV Cache Quantization: disabled
The post warns that enabling KV cache quantization persists context between tasks and can lead to unpredictable behavior; it should remain off for consistent operation.
Quantization should be chosen based on available memory and performance targets. The guide uses a 4-bit quantized model as a practical baseline and mentions 5-bit and 6-bit options when memory headroom allows and slightly higher quality is desired.
Cline configuration
Inside Cline’s settings, the instructions specify:
- Provider: LM Studio
- Model: qwen/qwen3-coder-30b
- Base URL: leave unset when using LM Studio’s default local endpoint
- Context window: 262,144 tokens, to match LM Studio
The guide strongly recommends enabling “Use compact prompt.” This prompt is roughly 10% the length of Cline’s full system prompt and is described as important for local inference efficiency. The tradeoff is reduced feature access: MCP tools, Focus Chain, and MTP are not available with the compact prompt enabled.
Performance notes
The post describes Qwen3 Coder 30B as performing well on modern laptops, with MLX providing speedups on Apple Silicon. A brief warmup period on first load is expected each session. Ingestion of very large contexts slows over time, which is characteristic of long-context inference. The guidance suggests breaking large repository work into phases or reducing the active context window if performance degrades.
For quantization, 4-bit is presented as the default balance between speed and quality on consumer hardware. If additional memory is available, 5-bit or 6-bit quantization may offer incremental quality improvements.
Offline, privacy, and cost characteristics
Running locally means code and intermediate data remain on the machine. The guide positions this as useful for privacy-sensitive projects and for environments without reliable connectivity, including air-gapped workflows. There are no API tokens or metered usage in this setup; once the model is downloaded, inference runs locally.
When local vs. cloud models
The post outlines scenarios where local models are a good fit:
- Offline sessions without dependable internet
- Privacy-constrained work where source code should not leave the device
- Cost-sensitive use where API billing would be significant
- Learning or experimentation without usage limits
It also notes cases where cloud models still hold advantages:
- Very large repositories that exceed local context limits
- Extended refactoring tasks that benefit from larger context windows
- Teams seeking uniform performance across varied hardware
Troubleshooting and links
Connectivity issues typically trace back to LM Studio not running or not having a model loaded; the Developer tab should show “Server: Running” with a selected model. Unresponsiveness may indicate misconfiguration: the guide calls out ensuring “Use compact prompt” is enabled in Cline and KV cache quantization is disabled in LM Studio. For long sessions where performance declines, reducing the context window or reloading the model are suggested mitigations.
Resources:
- LM Studio: https://lmstudio.ai/?ref=cline.ghost.io
- Qwen3 Coder 30B: https://lmstudio.ai/models/qwen/qwen3-coder-30b?ref=cline.ghost.io
- Default local endpoint: http://127.0.0.1:1234/?ref=cline.ghost.io
- Cline: https://cline.bot/?ref=cline.ghost.io
Availability
The guide was published on August 28, 2025. No pricing information is provided; the post focuses on a local, non-metered runtime after model download.
TL;DR
- Fully offline Cline stack using LM Studio and Qwen3 Coder 30B
- Model formats: MLX on Apple Silicon; GGUF on Windows/others
- LM Studio settings: 262,144-token context; KV cache quantization off
- Cline settings: LM Studio provider; qwen/qwen3-coder-30b; compact prompt on
- Quantization: 4-bit as baseline; 5/6-bit if memory allows
- Strengths: local privacy, no API billing, works without internet
- Cloud still preferable for very large repos, longest contexts, and uniform team performance
