Read Me First
Last updated
Last updated
Cline is a powerful AI coding assistant that uses tool-calling to help you write, analyze, and modify code. While running models locally can save on API costs, there's an important trade-off: local models are significantly less reliable at using these essential tools.
When you run a "local version" of a model, you're actually running a drastically simplified copy of the original. This process, called distillation, is like trying to compress a professional chef's knowledge into a basic cookbook – you keep the simple recipes but lose the complex techniques and intuition.
Local models are created by training a smaller model to imitate a larger one, but they typically only retain 1-26% of the original model's capacity. This massive reduction means:
Less ability to understand complex contexts
Reduced capability for multi-step reasoning
Limited tool-use abilities
Simplified decision-making process
Think of it like running your development environment on a calculator instead of a computer – it might handle basic tasks, but complex operations become unreliable or impossible.
When you run a local model with Cline:
Responses are 5-10x slower than cloud services
System resources (CPU, GPU, RAM) get heavily utilized
Your computer may become less responsive for other tasks
Code analysis becomes less accurate
File operations may be unreliable
Browser automation capabilities are reduced
Terminal commands might fail more often
Complex multi-step tasks often break down
You'll need at minimum:
Modern GPU with 8GB+ VRAM (RTX 3070 or better)
32GB+ system RAM
Fast SSD storage
Good cooling solution
Even with this hardware, you'll be running smaller, less capable versions of models:
7B models
Basic coding, limited tool use
14B models
Better coding, unstable tool use
32B models
Good coding, inconsistent tool use
70B models
Best local performance, but requires expensive hardware
Put simply, the cloud (API) versions of these models are the full-bore version of the model. The full version of DeepSeek-R1 is 671B. These distilled models are essentially "watered-down" versions of the cloud model.
Use cloud models for:
Complex development tasks
When tool reliability is crucial
Multi-step operations
Critical code changes
Use local models for:
Simple code completion
Basic documentation
When privacy is paramount
Learning and experimentation
Start with smaller models
Keep tasks simple and focused
Save work frequently
Be prepared to switch to cloud models for complex operations
Monitor system resources
"Tool execution failed": Local models often struggle with complex tool chains. Simplify your prompt.
"No connection could be made because the target machine actively refused it": This usually means that the Ollama or LM Studio server isn't running, or is running on a different port/address than Cline is configured to use. Double-check the Base URL address in your API Provider settings.
"Cline is having trouble...": Increase your model's context length to its maximum size.
Slow or incomplete responses: Local models can be slower than cloud-based models, especially on less powerful hardware. If performance is an issue, try using a smaller model. Expect significantly longer processing times.
System stability: Watch for high GPU/CPU usage and temperature
Context limitations: Local models often have smaller context windows than cloud models. Break tasks down into smaller pieces.
Local model capabilities are improving, but they're not yet a complete replacement for cloud services, especially for Cline's tool-based functionality. Consider your specific needs and hardware capabilities carefully before committing to a local-only approach.
Check the latest compatibility guides
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Remember: When in doubt, prioritize reliability over cost savings for important development work.
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