Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?

Latest commit

Co-authored-by: YuliangLiu0306 <>

Git stats


Failed to load latest commit information.
Latest commit message
Commit time
March 20, 2023 10:52
March 24, 2023 18:44
February 15, 2022 11:31
March 20, 2023 17:57
March 24, 2023 13:33
March 10, 2023 14:54



Colossal-AI: Making large AI models cheaper, faster and more accessible

Paper | Documentation | Examples | Forum | Blog

GitHub Repo stars Build Documentation CodeFactor HuggingFace badge slack badge WeChat badge

| English | 中文 |

Latest News

Table of Contents

Why Colossal-AI

Prof. James Demmel (UC Berkeley): Colossal-AI makes training AI models efficient, easy, and scalable.

(back to top)


Colossal-AI provides a collection of parallel components for you. We aim to support you to write your distributed deep learning models just like how you write your model on your laptop. We provide user-friendly tools to kickstart distributed training and inference in a few lines.

(back to top)

Parallel Training Demo


  • Save 50% GPU resources, and 10.7% acceleration


  • 11x lower GPU memory consumption, and superlinear scaling efficiency with Tensor Parallelism

  • 24x larger model size on the same hardware
  • over 3x acceleration


  • 2x faster training, or 50% longer sequence length



  • Open Pretrained Transformer (OPT), a 175-Billion parameter AI language model released by Meta, which stimulates AI programmers to perform various downstream tasks and application deployments because public pretrained model weights.
  • 45% speedup fine-tuning OPT at low cost in lines. [Example] [Online Serving]

Please visit our documentation and examples for more details.


  • 14x larger batch size, and 5x faster training for Tensor Parallelism = 64

Recommendation System Models

  • Cached Embedding, utilize software cache to train larger embedding tables with a smaller GPU memory budget.

(back to top)

Single GPU Training Demo


  • 20x larger model size on the same hardware

  • 120x larger model size on the same hardware (RTX 3080)


  • 34x larger model size on the same hardware

(back to top)

Inference (Energon-AI) Demo

  • Energon-AI: 50% inference acceleration on the same hardware

  • OPT Serving: Try 175-billion-parameter OPT online services

  • BLOOM: Reduce hardware deployment costs of 176-billion-parameter BLOOM by more than 10 times.

(back to top)

Colossal-AI in the Real World


A low-cost ChatGPT equivalent implementation process. [code] [blog]

  • Up to 7.73 times faster for single server training and 1.42 times faster for single-GPU inference

  • Up to 10.3x growth in model capacity on one GPU
  • A mini demo training process requires only 1.62GB of GPU memory (any consumer-grade GPU)

  • Increase the capacity of the fine-tuning model by up to 3.7 times on a single GPU
  • Keep in a sufficiently high running speed

(back to top)


Acceleration of AIGC (AI-Generated Content) models such as Stable Diffusion v1 and Stable Diffusion v2.

  • Training: Reduce Stable Diffusion memory consumption by up to 5.6x and hardware cost by up to 46x (from A100 to RTX3060).

  • Inference: Reduce inference GPU memory consumption by 2.5x.

(back to top)


Acceleration of AlphaFold Protein Structure

  • FastFold: Accelerating training and inference on GPU Clusters, faster data processing, inference sequence containing more than 10000 residues.

  • xTrimoMultimer: accelerating structure prediction of protein monomers and multimer by 11x.

(back to top)



  • PyTorch >= 1.11 (PyTorch 2.x in progress)
  • Python >= 3.7
  • CUDA >= 11.0

If you encounter any problem about installation, you may want to raise an issue in this repository.

Install from PyPI

You can easily install Colossal-AI with the following command. By default, we do not build PyTorch extensions during installation.

pip install colossalai

Note: only Linux is supported for now.

However, if you want to build the PyTorch extensions during installation, you can set CUDA_EXT=1.

CUDA_EXT=1 pip install colossalai

Otherwise, CUDA kernels will be built during runtime when you actually need it.

We also keep release the nightly version to PyPI on a weekly basis. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via

pip install colossalai-nightly

Download From Source

The version of Colossal-AI will be in line with the main branch of the repository. Feel free to raise an issue if you encounter any problem. :)

git clone
cd ColossalAI

# install colossalai
pip install .

By default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime. If you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):

CUDA_EXT=1 pip install .

(back to top)

Use Docker

Pull from DockerHub

You can directly pull the docker image from our DockerHub page. The image is automatically uploaded upon release.

Build On Your Own

Run the following command to build a docker image from Dockerfile provided.

Building Colossal-AI from scratch requires GPU support, you need to use Nvidia Docker Runtime as the default when doing docker build. More details can be found here. We recommend you install Colossal-AI from our project page directly.

cd ColossalAI
docker build -t colossalai ./docker

Run the following command to start the docker container in interactive mode.

docker run -ti --gpus all --rm --ipc=host colossalai bash

(back to top)


Join the Colossal-AI community on Forum, Slack, and WeChat(微信) to share your suggestions, feedback, and questions with our engineering team.


Referring to the successful attempts of BLOOM and Stable Diffusion, any and all developers and partners with computing powers, datasets, models are welcome to join and build the Colossal-AI community, making efforts towards the era of big AI models!

You may contact us or participate in the following ways:

  1. Leaving a Star to show your like and support. Thanks!
  2. Posting an issue, or submitting a PR on GitHub follow the guideline in Contributing
  3. Send your official proposal to email

Thanks so much to all of our amazing contributors!

The order of contributor avatars is randomly shuffled.

(back to top)


We leverage the power of GitHub Actions to automate our development, release and deployment workflows. Please check out this documentation on how the automated workflows are operated.

Cite Us

This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the Reference List.

To cite this project, you can use the following BibTeX citation.

  title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},
  author={Bian, Zhengda and Liu, Hongxin and Wang, Boxiang and Huang, Haichen and Li, Yongbin and Wang, Chuanrui and Cui, Fan and You, Yang},
  journal={arXiv preprint arXiv:2110.14883},

Colossal-AI has been accepted as official tutorials by top conference SC, AAAI, PPoPP, CVPR, ISC, etc.

(back to top)