Cline with a local model: setup for Ollama, LM Studio or llama.cpp

Cline is an open-source (Apache-2.0) coding agent that runs as an extension in VS Code, Cursor, Windsurf and JetBrains IDEs, with a separate terminal CLI. It has built-in Ollama and LM Studio providers and a Compact Prompt option for local inference, which suits people who want an agent inside their editor.

Source code Official docs Compare all agents

At a glance

Type: vscode-extension, cli
Licence: Apache-2.0
Latest release: 4.1.25 (extension); 3.0.73 (CLI) (2026-10-17)
Status: Maintained
Local backends: ollama, lmstudio
Needs native tool calling: Not verified
MCP support: Yes
Recommended minimum context: 32,000 tokens
Start-up prompt: Not measured
GitHub stars: 67.6k (2026-10-19)

How to set it up with a local model

Pick your backend. Each command has its own Copy button. Where a step uses an example model, swap in the one you run; the table below lists the ones that fit your machine.

Install it in VS Code: Extensions view → search "Cline" → Install.
Start Ollama with a 32k context window (or set the context slider in the Ollama app's settings).
```
OLLAMA_CONTEXT_LENGTH=32768 ollama serve
```
In a second terminal, pull a tool-capable coding model (replace qwen3-coder:30b with any model you prefer).
```
ollama pull qwen3-coder:30b
```
In Cline Settings, set API Provider to Ollama and Base URL to http://localhost:11434.
Select your model from the dropdown, or type it (e.g. qwen3-coder:30b).
Set Context Window to at least 32K tokens in the same settings panel.
Enable Use Compact Prompt under Settings → Features.

Other ways to install

JetBrains: Settings → Plugins → Marketplace → search "Cline" → Install

CLI (npm, Node.js 20+)

npm install -g cline

Check that it works

Start a new task asking Cline to list the files in your workspace. If the connection fails, check that the server answers at its base URL and that a model is loaded; in the CLI, `cline config` shows the current configuration.

cline config

Which local models fit, by machine

Checked by this site's engine at the context Cline needs, with 32 GB of system RAM for MoE offload. A Mac counts three quarters of unified memory as usable.

Machine	Model	Quant	Context	Fit	Ollama
12 GB GPU	Qwen 3.5 9B	Q4_K_M	32,768	Comfortable (7.4 GB of 12 GB)	`qwen3.5:9b`
	Qwen 3.6 35B-A3B	Q4_K_M	32,768	With CPU offload (MoE) (22.7 GB of 12 GB)	`qwen3.6:35b-a3b`
16 GB GPU	Qwen 3.5 9B	Q4_K_M	32,768	Comfortable (7.4 GB of 16 GB)	`qwen3.5:9b`
	Qwen 3.6 35B-A3B	Q4_K_M	32,768	With CPU offload (MoE) (22.7 GB of 16 GB)	`qwen3.6:35b-a3b`
24 GB GPU	Qwen3.8 27B	Q4_K_M	32,768	Comfortable (19.9 GB of 24 GB)	`qwen3.8:27b`
	Qwen 3.6 27B	Q4_K_M	32,768	Comfortable (19.9 GB of 24 GB)	`qwen3.6:27b`
	Devstral Small 2 24B	Q4_K_M	32,768	Comfortable (20.7 GB of 24 GB)	`devstral-small-2:24b`
	Qwen 3.6 35B-A3B	Q4_K_M	65,536	Tight (23.3 GB of 24 GB)	`qwen3.6:35b-a3b`
32 GB GPU	Qwen3.8 27B	Q4_K_M	32,768	Comfortable (19.9 GB of 32 GB)	`qwen3.8:27b`
	Qwen 3.6 27B	Q4_K_M	32,768	Comfortable (19.9 GB of 32 GB)	`qwen3.6:27b`
	Devstral Small 2 24B	Q4_K_M	32,768	Comfortable (20.7 GB of 32 GB)	`devstral-small-2:24b`
	Qwen 3.6 35B-A3B	Q4_K_M	65,536	Comfortable (23.3 GB of 32 GB)	`qwen3.6:35b-a3b`
	Qwen3-Coder 30B-A3B (MoE)	Q4_K_M	65,536	Comfortable (25.7 GB of 32 GB)	`qwen3-coder:30b`
Mac, 64 GB	Qwen 3.6 35B-A3B	Q8_0	131,072	Comfortable (40.7 GB of 48 GB)	—

Your exact machine: filter the matrix or check any model in the analyzer.

Common problems with local models

Ollama's Cline guide asks for a context window of at least 32K tokens for coding.
Cline's local-model guide recommends Use Compact Prompt, focused tasks, and starting a new task when the context gets too large.
LM Studio must have a model loaded and its server running before Cline can list models.

Other coding agents

How they rank for local models: the best agent harness for local LLMs.

Sources (8), checked 2026-10-20