ScalingOpt Optimization at Scale
Discover, compare, and contribute to cutting-edge optimization algorithms designed for large-scale deep learning.
Platform Statistics
Real-time data from our comprehensive optimizer database
Featured Optimizers
Discover the most powerful and innovative optimization algorithms powering modern AI
Apollo (2)
2024SGD-like Memory, AdamW-level Performance
Conda
2025Column-Normalized Adam for Training LLMs Faster
Muon
2024Orthogonal weight updates via Newton-Schulz iteration
SOAP
2024Improving and Stabilizing Shampoo using Adam
Industry-Optimized Implementations
Production-ready libraries with improved distributed support and hardware optimization
Hugging Face
Optimizers integrated into Transformers (AdamW, AdaFactor) with native support for distributed training and mixed precision.
Meta Research
Cutting-edge optimization algorithms like Distributed Shampoo developed by Meta for large-scale model training.
NVIDIA TensorRT
Advanced model optimization toolkit for NVIDIA GPUs, focusing on quantization and inference acceleration.
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 efficient AI and optimization algorithms. Discover, learn, and contribute to the future of machine learning optimization.