ScalingOpt Optimization at Scale
Discover, compare, and contribute to cutting-edge optimization algorithms designed for large-scale deep learning. From foundational methods to state-of-the-art scalable optimizers.
Platform Statistics
Real-time data from our comprehensive optimizer database
Featured Optimizers
Discover the most powerful and innovative optimization algorithms powering modern AI
SGD
1999Stochastic Gradient Descent - foundational and reliable optimizer
AdamW
2017Adam with decoupled weight decay - excellent for transformers
Adam-mini
2024Memory-efficient Adam variant with fewer learning rates
Muon
2024Orthogonal weight updates via Newton-Schulz iteration
Why Choose ScalingOpt?
Everything you need to understand, implement, and scale optimization algorithms for modern AI
Extensive Optimizer Library
Explore all optimization algorithms from foundational SGD to cutting-edge Adam-mini and Muon, with detailed implementations and PyTorch code.
Research & Learning Hub
Access research papers, tutorials, and educational content covering optimization theory, implementation guides, and latest developments.
Open Source & Community
Contribute to open-source implementations, join GitHub discussions, and collaborate with researchers worldwide on optimization algorithms.
Join the Optimization Community
Connect with researchers and practitioners exploring optimization algorithms and efficient AI. Discover, learn, and contribute to the future of machine learning optimization.