プロジェクト情報
Colossal-AI
Distributed deep-learning training framework optimised for efficient large-model training
- 機関
- NUS HPC-AI Lab
- グループ
- University / research
- カテゴリー
- Distributed training framework
- ステータス
- Actively maintained
- ローンチ
- 2021-10
- 言語 / 形態
- Python
- ライセンス
- Apache-2.0
- GitHub Stars
- 41,376
- 情報更新
- 2026-05-04
Colossal-AI is one of the most globally visible open-source projects from Singapore’s university ecosystem: it tackles memory, parallelism, and cost problems in large-model training.
説明
Colossal-AI is a distributed AI training system. Developers use it for tensor parallelism, pipeline parallelism, ZeRO, heterogeneous memory management, and large-model inference optimization, splitting workloads that would overwhelm a single machine across multi-GPU and multi-node environments.
It was incubated by the NUS HPC-AI Lab and later grew into a global open-source engineering project.
AIとの関係
Large-model competition is not only about model weights; it is also about training systems. Colossal-AI turns "can we afford to train this" into an engineering problem: reduce memory pressure, improve throughput, and bring large-model training closer to research teams and smaller companies.
This kind of infrastructure may not face end users directly, but it affects the cost curve of model development.
シンガポールとの関係
Colossal-AI shows that Singapore’s universities are not limited to applied AI; they can also have a presence in global AI infrastructure. It complements model projects such as SEA-LION: one addresses training systems, the other regional model supply.
For sgai.md, it is a long-running sample of whether Singapore can export general AI engineering infrastructure.
重要マイルストーン
- 2021-10Colossal-AI repository created
- 2023-2024Moves into the mainstream LLM training-tool conversation