Overview
Colossal-AI is an open-source system for large-scale distributed training and high-performance inference. It provides data/tensor/pipeline/sequence parallelism, heterogeneous memory management, and Colossal-Inference for accelerated serving, helping reduce resource cost and improve reproducibility for large model training and deployment.
Key Features
- Multi-parallelism strategies: data, tensor (1D/2D/2.5D/3D), pipeline, and sequence parallelism.
- Heterogeneous memory management: memory allocation and scheduling to lower GPU memory footprint and enable larger models.
- High-performance inference: Colossal-Inference accelerates model serving and reduces memory usage.
- Extensive examples and documentation: many tutorials and production-ready docs for fast onboarding.
Use Cases
- Distributed training and fine-tuning of large models (LLMs, Transformers, MoE).
- High-throughput inference and production deployment.
- Research and education on parallel strategies and performance optimization.
Technical Characteristics
- PyTorch-based with examples from single-node to multi-node setups.
- Provides optimizers, schedulers, and auto-parallelization tools to lower the barrier for distributed programming.
- Active community and rich ecosystem (examples, Docker/Cloud integrations, third-party model support).