Local Models Overview

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Running Models Locally with Cline

Run Cline completely offline with genuinely capable models on your own hardware. No API costs, no data leaving your machine, no internet dependency. Local models have reached a turning point where they’re now practical for real development work. This guide covers everything you need to know about running Cline with local models.

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Quick Start

1. Check your hardware - 32GB+ RAM minimum
2. Choose your runtime - LM Studio or Ollama
3. Download Qwen3 Coder 30B - The recommended model
4. Configure settings - Enable compact prompts, set max context
5. Start coding - Completely offline

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Hardware Requirements

Your RAM determines which models you can run effectively:

RAM	Recommended Model	Quantization	Performance Level
32GB	Qwen3 Coder 30B	4-bit	Entry-level local coding
64GB	Qwen3 Coder 30B	8-bit	Full Cline features
128GB+	GLM-4.5-Air	4-bit	Cloud-competitive performance

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Recommended Models

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Primary Recommendation: Qwen3 Coder 30B

After extensive testing, Qwen3 Coder 30B is the most reliable model under 70B parameters for Cline:

• 256K native context window - Handle entire repositories
• Strong tool-use capabilities - Reliable command execution
• Repository-scale understanding - Maintains context across files
• Proven reliability - Consistent outputs with Cline’s tool format

Download sizes:

• 4-bit: ~17GB (recommended for 32GB RAM)
• 8-bit: ~32GB (recommended for 64GB RAM)
• 16-bit: ~60GB (requires 128GB+ RAM)

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Why Not Smaller Models?

Most models under 30B parameters (7B-20B) fail with Cline because they:

• Produce broken tool-use outputs
• Refuse to execute commands
• Can’t maintain conversation context
• Struggle with complex coding tasks

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Runtime Options

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LM Studio

• Pros: User-friendly GUI, easy model management, built-in server
• Cons: Memory overhead from UI, limited to single model at a time
• Best for: Desktop users who want simplicity
• Setup Guide →

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Ollama

• Pros: Command-line based, lower memory overhead, scriptable
• Cons: Requires terminal comfort, manual model management
• Best for: Power users and server deployments
• Setup Guide →

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Critical Configuration

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Required Settings

In Cline:

• ✅ Enable “Use Compact Prompt” - Reduces prompt size by 90%
• ✅ Set appropriate model in settings
• ✅ Configure Base URL to match your server

In LM Studio:

• Context Length: 262144 (maximum)
• KV Cache Quantization: OFF (critical for proper function)
• Flash Attention: ON (if available on your hardware)

In Ollama:

• Set context window: num_ctx 262144
• Enable flash attention if supported

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Understanding Quantization

Quantization reduces model precision to fit on consumer hardware:

Type	Size Reduction	Quality	Use Case
4-bit	~75%	Good	Most coding tasks, limited RAM
8-bit	~50%	Better	Professional work, more nuance
16-bit	None	Best	Maximum quality, requires high RAM

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Model Formats

GGUF (Universal)

• Works on all platforms (Windows, Linux, Mac)
• Extensive quantization options
• Broader tool compatibility
• Recommended for most users

MLX (Mac only)

• Optimized for Apple Silicon (M1/M2/M3)
• Leverages Metal and AMX acceleration
• Faster inference on Mac
• Requires macOS 13+

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Performance Expectations

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What’s Normal

• Initial load time: 10-30 seconds for model warmup
• Token generation: 5-20 tokens/second on consumer hardware
• Context processing: Slower with large codebases
• Memory usage: Close to your quantization size

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Performance Tips

1. Use compact prompts - Essential for local inference
2. Limit context when possible - Start with smaller windows
3. Choose right quantization - Balance quality vs speed
4. Close other applications - Free up RAM for the model
5. Use SSD storage - Faster model loading

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Use Case Comparison

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When to Use Local Models

✅ Perfect for:

• Offline development environments
• Privacy-sensitive projects
• Learning without API costs
• Unlimited experimentation
• Air-gapped environments
• Cost-conscious development

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When to Use Cloud Models

☁️ Better for:

• Very large codebases (>256K tokens)
• Multi-hour refactoring sessions
• Teams needing consistent performance
• Latest model capabilities
• Time-critical projects

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Troubleshooting

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Common Issues & Solutions

“Shell integration unavailable”

• Switch to bash in Cline Settings → Terminal → Default Terminal Profile
• Resolves 90% of terminal integration problems

“No connection could be made”

• Verify server is running (LM Studio or Ollama)
• Check Base URL matches server address
• Ensure no firewall blocking connection
• Default ports: LM Studio (1234), Ollama (11434)

Slow or incomplete responses

• Normal for local models (5-20 tokens/sec typical)
• Try smaller quantization (4-bit instead of 8-bit)
• Enable compact prompts if not already
• Reduce context window size

Model confusion or errors

• Verify KV Cache Quantization is OFF (LM Studio)
• Ensure compact prompts enabled
• Check context length set to maximum
• Confirm sufficient RAM for quantization

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Performance Optimization

For faster inference:

1. Use 4-bit quantization
2. Enable Flash Attention
3. Reduce context window if not needed
4. Close unnecessary applications
5. Use NVMe SSD for model storage

For better quality:

1. Use 8-bit or higher quantization
2. Maximize context window
3. Ensure adequate cooling
4. Allocate maximum RAM to model

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Advanced Configuration

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Multi-GPU Setup

If you have multiple GPUs, you can split model layers:

• LM Studio: Automatic GPU detection
• Ollama: Set num_gpu parameter

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Custom Models

While Qwen3 Coder 30B is recommended, you can experiment with:

• DeepSeek Coder V2
• Codestral 22B
• StarCoder2 15B

Note: These may require additional configuration and testing.

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Community & Support

• Discord: Join our community for real-time help
• Reddit: r/cline for discussions
• GitHub: Report issues

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Next Steps

Ready to get started? Choose your path:

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Summary

Local models with Cline are now genuinely practical. While they won’t match top-tier cloud APIs in speed, they offer complete privacy, zero costs, and offline capability. With proper configuration and the right hardware, Qwen3 Coder 30B can handle most coding tasks effectively. The key is proper setup: adequate RAM, correct configuration, and realistic expectations. Follow this guide, and you’ll have a capable coding assistant running entirely on your hardware.