DDR5 AI memory tuner for safe timing optimization and performance simulation; Streamlit UI, Windows installer, JEDEC compliance.
Looking for docs? See the consolidated docs hub in docs/ (index at docs/README.md).
Professional DDR5 optimization interface with simulation and AI optimization features
Comprehensive feature hub with dark/light theme, 3D charts, real-time monitoring, and AI assistant
Real hardware control with safety locks, advanced integration, and comprehensive databases
Option A — EXE installer (recommended):
- Download the Windows installer:
- Run the EXE (no admin required). It installs to
%LOCALAPPDATA%\DDR5-AI-Memory-Tuner, sets up a venv, installs dependencies, and adds Start Menu/Desktop shortcuts.
Option B — Scripted install (from source):
- Double-click
windows/install.bat(or right-clickwindows/install.ps1→ Run with PowerShell) - Creates the same per-user install/shortcuts as the EXE
- More details in
windows/README-windows.md
Launch options on Windows:
- From Desktop/Start Menu shortcut “DDR5 AI Memory Tuner”, or
- Run
%LOCALAPPDATA%\DDR5-AI-Memory-Tuner\run_ddr5_simulator.bat
git clone https://github.com/killerbotofthenewworld/ddr5-ai-memory-tuner.git
cd ddr5-ai-memory-tuner
python -m pip install -r requirements.txt
python -m streamlit run src/web_interface/main.py --server.port 8521- Dark/Light Theme with smooth animations
- Custom CSS styling and metric cards
- Progress indicators and loading spinners
- Responsive design for all screen sizes
- 3D Performance Charts - Interactive surface plots
- Real-time Graphs - Live memory bandwidth/latency
- Configuration Comparisons - Side-by-side analysis
- Optimization Landscapes - AI fitness visualization
- Multiple AI Engines - Genetic Algorithm, Neural Networks, RL
- AutoML Pipeline - Automated model training with Optuna
- LLM Integration - OpenAI, Anthropic, Ollama, Local models
- Plain English explanations and recommendations
- Live Tuning with safety locks and confirmations
- WebSocket Monitoring - Real-time metrics streaming
- Hardware Detection - Automatic system profiling
- Emergency Recovery - Instant parameter restoration
- Multi-level Confirmations for hardware changes
- Predictive Health Monitoring - Component lifespan estimation
- JEDEC Compliance checking and validation
- Damage Prevention - Voltage/timing safety limits
- Tool Imports/Exports - ASUS AI Suite, MSI Dragon Center, Intel XTU
- Database Integration - CPU, Motherboard, Memory Kit databases
- Cross-Brand Compatibility - Intel, AMD, NVIDIA platforms
- Benchmark Integration - Performance validation
| Component | Minimum | Recommended |
|---|---|---|
| Python | 3.9+ | 3.11+ |
| OS | Windows 10/11 or Linux | Ubuntu 22.04+/Fedora 38+ |
| RAM | 8GB | 16GB+ |
| Storage | 2GB | 5GB+ |
| Hardware Access | User | Admin/Root (for live tuning) |
| GPU | Optional | CUDA/ROCm (for AI acceleration) |
| Tab | Description | Features |
|---|---|---|
| 🎯 Simulation | Parameter tuning sandbox | Manual configuration, validation, JEDEC compliance |
| 🧠 AI Optimization | Automated optimization | Multiple AI engines, hyperparameter tuning |
| ⚡ Live Tuning | Real hardware control | Safety locks, backup/restore, emergency stops |
| 🚀 Enhanced Features | Advanced tools hub | 7 specialized sub-tabs with cutting-edge features |
| 🔧 Advanced Integration | Hardware databases | CPU/MB/RAM detection, cross-platform compatibility |
# Run tests
pytest tests/ -v
# Code quality checks
black src/ tests/ main.py
flake8 src/ tests/ main.py
mypy src/ --ignore-missing-imports
# Security scanning
bandit -r src/
safety checkSee also: docs/TESTING.md for a concise testing guide.
- Multi-level confirmations before applying changes
- Automatic parameter backup and instant recovery
- Real-time validation of voltage/timing relationships
- Emergency stop buttons with immediate effect
- Bounded optimization within safe parameter ranges
- JEDEC compliance checking for all configurations
- Stability scoring for parameter combinations
- Gradual tuning with incremental steps
- Privilege escalation warnings and confirmations
- System monitoring during live tuning sessions
- Rollback mechanisms for failed configurations
- Health monitoring with predictive maintenance
| Metric | Value | Status |
|---|---|---|
| Version | 6.0.2 | ✅ Current |
| Tests | 29/29 passing | ✅ All Green |
| Code Quality | A+ Grade | ✅ Excellent |
| Security | No Issues | ✅ Secure |
| Performance | <100ms response | ✅ Fast |
| Platform Support | Linux/Windows/macOS | ✅ Cross-platform |
| AI Models | 5+ engines | ✅ Advanced |
| Hardware Support | Intel/AMD/NVIDIA | ✅ Universal |
We welcome contributions! Here's how to get started:
- Fork the repository
- Create a feature branch:
git checkout -b feature/amazing-feature - Add tests for new functionality
- Run the test suite:
pytest tests/ - Submit a pull request
git clone https://github.com/killerbotofthenewworld/ddr5-ai-memory-tuner.git
cd ddr5-ai-memory-tuner
pip install -r requirements.txt -r requirements-dev.txt
pre-commit install
Your support helps fund:
- Hardware testing on diverse platforms
- AI model training and optimization
- New feature development
- Documentation and tutorials
This project is licensed under the MIT License - see the LICENSE file for details.
- ddr5, ddr5-memory, ram, memory-tuning, overclocking, timing-optimization
- ai, machine-learning, reinforcement-learning, genetic-algorithm, optuna
- pytorch, scikit-learn, xgboost, lightgbm
- jedec, hardware-detection, performance-tuning
- streamlit, windows-installer, simulator, benchmark, optimization
- 📖 Documentation: GitHub Pages (auto-published from /docs)
- 🐛 Bug Reports: https://github.com/killerbotofthenewworld/ddr5-ai-memory-tuner/issues
- 💬 Discussions: https://github.com/killerbotofthenewworld/ddr5-ai-memory-tuner/discussions
- 📦 Releases: https://github.com/killerbotofthenewworld/ddr5-ai-memory-tuner/releases
Built with ❤️ for the DDR5 optimization community
Empowering enthusiasts and professionals with AI-driven memory tuning