Written by Jakub Rusinowski · Last updated 2026-07-12 · Hardware figures computed by our VRAM engine
Cline runs fully local: set its API provider to Ollama, pick your model, and raise the context window to 16K+ — the default context is the single most common reason local Cline fails. Model reality check: agent loops are the hardest workload local models face; Qwen 3.6 27B on a 24 GB card is where autonomy gets dependable, Devstral-2 22B on 16 GB is the floor, and below that keep tasks small and single-file.
Cline is the most demanding thing you can ask a local model to do. A single task means: understand a plan, read multiple files, produce exact tool calls, react to command output, and stay coherent across 50–100K tokens of accumulated context. Frontier API models make this look easy; local models make it an honest capability test.
That's not a reason to skip local Cline — it's the reason to configure it deliberately. Done right (correct tier of model, context raised, tasks scoped), local Cline does real work with total privacy and zero marginal cost. This page is the local-specific configuration and the honest tier list; the tool matrix covers when Cline is even the right tool.
Install Cline from the VS Code marketplace. In its settings, choose Ollama as the API provider — base URL http://localhost:11434 — and select your model from the detected list. No API key, no account. (LM Studio works identically via its OpenAI-compatible server on port 1234.)
# 24 GB — dependable autonomy
ollama pull qwen3.6:27b
# 16 GB — the agentic floor
ollama pull devstral:22b
# Workstation / 96 GB+ Mac — the best local Cline gets
ollama pull qwen3-coder:80b-a3b-q4Below 16 GB, Cline technically runs on Qwen3-Coder 8B — treat it as a single-file assistant with tools, not an autonomous agent. Per-tier reasoning: best local coding models.
Ollama's default context silently truncates Cline's system prompt and file context, producing the classic symptoms: forgotten instructions, hallucinated file contents, tool calls that reference nothing. Create a long-context variant and select it in Cline:
cat > Modelfile <<'EOF'
FROM qwen3.6:27b
PARAMETER num_ctx 32768
EOF
ollama create qwen3.6:27b-32k -f ModelfileMind the VRAM: 32K of KV cache on a 27B eats several extra GB — the checker models the exact cost per context size. On 16 GB cards, 16K is usually the ceiling that still fits.
Local Cline succeeds on tasks with clear boundaries and verifiable outcomes: "add pagination to this endpoint and update the test" — not "improve the API layer". Use Plan mode first (cheap tokens, catches misunderstandings before edits), approve checkpoints instead of auto-approving, and keep sessions short: context accumulates, and local models degrade faster than frontier ones as it fills.
The honest failure mode: a task keeps derailing at your tier's model. Options, in cost order: split the task smaller; step up a model tier (or run Qwen3-Coder 80B-A3B with expert offload if you have 32 GB RAM); or point Cline at a metered API for that one task — the hybrid pattern from our cost comparison exists precisely for the hard 5%. Privacy-critical repos simply take the first two options.