OptiReduce

OptiReduce is a resilient and tail-optimal collective-communication framework for distributed deep learning in the cloud.
It integrates seamlessly with PyTorch Distributed Data Parallel (DDP) to deliver faster, more efficient training in environments with high tail latency.

Why OptiReduce?

Faster, Smarter Training

Eliminates performance bottlenecks from stragglers and slow nodes
Optimized for high-tail latency cloud environments
Reduces overall training time, compute cost, and resource usage

Key Capabilities

Bounded-Loss Reliability – Achieves faster convergence by tolerating controlled loss
Tail Latency Optimization – Adaptive algorithms to handle unpredictable network delays
Native PyTorch Integration – Drop-in compatibility with DDP for effortless adoption

Technical Highlights

Supports Mellanox ConnectX NICs and RDMA networking
Efficient memory pool management for dynamic workloads
Zero-copy operations for peak communication throughput

Get Started

Choose your path:

Goal	Resource
Install and configure OptiReduce	Installation Guide
Run your first distributed job	Getting Started
Understand runtime usage	Usage Guide

Learn More

Topic	Resource
Deep dive into architecture and design	Technical Details
Performance and evaluation	Benchmarking Guide
Citing or contributing	Publications • Contributing

Support

Need help or want to get involved?

Browse the OptiReduce repo
Open an issue on GitHub
Setup troubleshooting installation guide