Strengths
- Enables training of extremely large models with limited hardware.
- Significantly reduces memory consumption and training time.
- Open-source with active community and Microsoft backing.
- Seamless integration with PyTorch ecosystem.
- Supports elastic and mixed precision training for flexibility.
Limitations
- Steep learning curve for beginners unfamiliar with distributed training.
- Primarily optimized for PyTorch; limited support for other frameworks.
- Requires significant infrastructure setup for large-scale distributed training.
- Documentation can be complex for advanced features.