Optimization & Troubleshooting
Tips for better performance and solving common issues when self-hosting LLMs.
đ Performance Optimization
Use GPU Acceleration
[Placeholder: Ensure your model is using GPU, not CPU]
[Placeholder: Command to check GPU usage]Choose Right Quantization
[Placeholder: Lower quantization = faster inference]
â Learn about quantizationAdjust Context Window
[Placeholder: Smaller context = less memory, faster speed]
Batch Processing
[Placeholder: Process multiple prompts together for efficiency]
đ§ Common Issues & Solutions
â Out of Memory Error
Problem: [Placeholder: Model too large for available VRAM/RAM]
Solutions:
- Try a lower quantization (Q4 instead of Q5)
- Use a smaller model variant
- Reduce context window size
- Close other applications
â ī¸ Very Slow Inference
Problem: [Placeholder: Model running on CPU instead of GPU]
Solutions:
- Check GPU drivers are installed
- Verify CUDA/ROCm is configured
- Use smaller quantization
- Try a smaller model
â ī¸ Poor Quality Responses
Problem: [Placeholder: Model giving low-quality outputs]
Solutions:
- Try higher quantization (Q5 or Q8)
- Use a larger model if you have VRAM
- Improve your prompts
- Adjust temperature settings
âšī¸ Model Download Failed
Problem: [Placeholder: Download interrupted or failed]
Solutions:
- Check internet connection
- Retry the download
- Verify disk space
- Try a different mirror/source
đĄ Advanced Tips
Layer Offloading
[Placeholder: Manually control which layers run on GPU vs CPU]
Flash Attention
[Placeholder: Use optimized attention for faster inference]
Model Caching
[Placeholder: Keep model loaded in memory for faster subsequent runs]
Learn More
[Placeholder: Optimization techniques guide]
Advanced performance tuning
[Placeholder: Hardware troubleshooting]
GPU and driver issues