GH Repository · hpcaitech
ColossalAI
Making large AI models cheaper, faster and more accessible
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pythonotherpipeline-parallelismhpclarge-scaledata-parallelismheterogeneous-trainingbig-modelmodel-parallelismdeep-learningaidistributed-computingPythoninferencefoundation-models
ColossalAI is a Python toolchain for training and running large AI models on distributed hardware, aiming to make them cheaper, faster and more accessible. Its topics indicate support for data, pipeline and model parallelism, heterogeneous training, and inference.
Use it when you need to scale big-model training or inference across multiple devices without building distributed plumbing yourself.
Use it to
- Train large models with data, pipeline and model parallelism
- Run heterogeneous training across mixed hardware
- Serve large-model inference at scale
- Reduce cost of foundation-model training
- Explore distributed-computing setups for deep learning
For Engineers training or serving large AI models
- Role
- other
- Language
- Python
- Licence
- Apache-2.0
- Forks
- 4,493
- Open issues
- 444
- Last push
- 2026-09-14
- Latest release
- v0.0.1-beta · 2021-10-28
topicsdeep-learningdistributed-computingmodel-parallelismpipeline-parallelisminferencefoundation-models