Scale ML Models Effortlessly

FairScale enables model parallelism, sharding, and optimized execution for large-scale machine learning projects.

Explore Performance

Key Capabilities

Model Parallelism

Split massive models across multiple GPUs/TPUs seamlessly with our advanced sharding architecture.

PyTorch Integration

Full compatibility with PyTorch workflows while maintaining high performance on distributed systems.

Dynamic Scheduling

Intelligent task scheduling for maximum hardware utilization in heterogeneous environments.

Performance Boost

6.2x

Speed improvement on ResNet-50 training

78%

Reduction in inference latency

210M+

Parameters supported in a single model

Architecture Diagram

Start Optimizing with FairScale

Transform your machine learning workflow with cutting-edge optimization techniques designed for modern infrastructure.

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