VS Code와 Cursor에서 로컬 LLM 사용하기: 설정 및 모범 사례

# 1. Install Continue from VS Code marketplace
# Search "Continue" and click Install

# 2. Make sure Ollama is running
ollama serve

# 3. Open Continue settings (Ctrl+Shift+P → Continue: Open Settings)
# config.json opens

# 4. Configure for your local model:
# Replace the default settings with:
{
  "models": [{
    "title": "Ollama",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b",
    "apiBase": "http://localhost:11434"
  }],
  "tabAutocompleteModel": {
    "title": "Ollama",
    "provider": "ollama",
    "model": "qwen2.5-coder:7b"
  }
}

# 5. Start typing code and press Tab for completions
# Or Ctrl+Shift+\ to manually trigger completions

# 1. Download Cursor from cursor.sh
# 2. Make sure Ollama is running
ollama serve

# 3. Open Cursor Settings (Cmd/Ctrl + ,)
# 4. Search "Model" and set:
#    - Model Provider: "Ollama"
#    - Model: "qwen2.5-coder:7b" (or your choice)
#    - API Base: "http://localhost:11434"

# 5. Type code and press Tab for inline completions
# 6. Ctrl+K for multi-line completions

# config.json advanced settings
{
  "tabAutocompleteModel": {
    "contextLength": 2048,     # How much code context to send
    "maxTokens": 50            # Max tokens per completion
  },
  "completionOptions": {
    "maxContextTokens": 1024,
    "maxSuggestionsCount": 5,
    "debounceWaitMs": 200      # Wait before showing completions (ms)
  },
  # For faster inference, use smaller context:
  "models": [{
    "contextLength": 1024      # Smaller context = faster
  }]
}

# For best speed on 8GB machines:
# - Use 7B model (not 13B)
# - Set maxTokens to 30
# - Set debounceWaitMs to 500 (less flickering)