Strengths
- Highly efficient hybrid parallelism techniques.
- Open-source with active community and research backing.
- Significant memory optimization enabling larger model training.
- Supports both training and inference acceleration.
- Seamless integration with popular deep learning frameworks.
Limitations
- Steep learning curve for beginners unfamiliar with distributed training.
- Documentation can be technical and dense for new users.
- Primarily focused on research and may lack enterprise-level support.
- Hardware requirements can still be high for extremely large models.