Colossal-AI: A Scalable Framework for Distributed Training of Large Models

The talk begins by outlining the opportunities and challenges posed by the era of large models, noting that model sizes and data volumes have grown dramatically, requiring thousands of GPUs and weeks of training, which makes resource costs prohibitive.

To address these issues, Beijing Lucheng Technology introduces the Colossal‑AI framework, a comprehensive software stack that supports distributed training across heterogeneous hardware, integrates with PyTorch, Lightning, and other ecosystems, and provides a plug‑in architecture for N‑dimensional parallelism (tensor, pipeline, data, and sequence parallelism).

The N‑dimensional parallel system unifies state‑of‑the‑art algorithms such as tensor parallelism, pipeline parallelism, and sequence parallelism, offering a zero‑code conversion from single‑GPU scripts to distributed execution while minimizing communication overhead through optimized All‑reduce and Ring Self‑Attention techniques.

Colossal‑AI also features a heterogeneous storage subsystem that offloads optimizer states to CPU RAM, reducing GPU memory pressure, and employs chunk‑based tensor management to improve communication efficiency.

Usability, extensibility, and generality are emphasized through a plug‑in design that decouples parallel dimensions and allows model deployment strategies to replace operators for acceleration.

Benchmark results demonstrate up to 90% speedup with FP8 mixed‑precision training and 30‑40% gains with BF16 compared to vanilla PyTorch, while heterogeneous storage enables training of models with hundred‑fold larger capacity on the same hardware.

Real‑world applications include large‑scale language model training (e.g., BERT pre‑training in 76 minutes), video generation models using sequence parallelism, and inference acceleration (e.g., Grok‑1 achieving 3.8× speedup on H800 GPUs).

The presentation concludes with a Q&A session covering integration with PyTorch, differences between 2.5D and 3D tensor parallelism, and the benefits of zero‑bubble pipeline design.