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Blue Iris – Performance Optimization

🔹 Overview​

This page provides practical guidance for improving Blue Iris performance and system stability.

🎯 Objective​

  • Reduce CPU and system load
  • Improve responsiveness
  • Maintain stable multi-camera operation
  • Balance quality vs performance

🧠 Key Concepts​

  • Performance is cumulative across all cameras
  • Stability matters more than maximum settings
  • Small changes can have large impact at scale

🛠️ Core Areas​

Camera Load​

  • Total number of cameras matters
  • Resolution and FPS multiply system load
  • Add cameras gradually and test

Codec​

  • H.264 is often more stable
  • H.265 may reduce bandwidth but increase decode load
  • Test before committing system-wide

Frame Rate​

  • ~15 FPS is a strong baseline
  • Higher FPS increases CPU, storage, and decode requirements

Resolution​

  • Use resolution appropriate for the scene
  • Avoid unnecessary high-resolution streams on low-value areas

Direct-to-Disk​

  • Reduces CPU load
  • Improves recording efficiency
  • Should be used where stable

Hardware Acceleration​

  • Can improve performance when working correctly
  • Must be tested for stability in your environment

AI Load​

  • AI adds processing demand
  • Scale gradually across cameras
  • Monitor impact before expanding

📊 Recommended Baseline​

  • H.264
  • ~15 FPS
  • Direct-to-disk enabled
  • Moderate camera count before scaling
  • AI applied selectively

🧠 Real-World Notes​

  • Performance problems often appear after incremental changes
  • System stability degrades gradually, not all at once
  • Simple configurations scale better over time

⚠️ Common Mistakes​

  • Adding too many cameras at once
  • Increasing FPS without need
  • Running maximum settings everywhere
  • Enabling AI on all cameras immediately
  • Ignoring storage performance

📊 Related Pages​

✅ Result​

A stable, responsive Blue Iris system that balances performance and quality.