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Decision brief
ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
Good fit when
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- Your project involves heterogeneous training environments or distributed computing setups requiring fine-tuned optimization.
Avoid when
- You are working in an environment that does not support Linux OS, as ColossalAI currently offers no support for other operating systems.
- Your current CUDA version is less than 11.0 or your GPU compute capability is below 7.0 (pre-V100/RTX20 series).
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Active (24d since push)
- As of 1w
- Provenance
- Not a fork · Organization account
- As of 1w
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install ColossalAISimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
ColossalAI is a Python library that aims to reduce the cost and increase the speed of developing large-scale AI models through advanced parallelism techniques like data-parallelism, model-parallelism, and pipeline-parallelism.
Capability facts
- Languages
- python
Source: github.language · Aug 7, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Tags
README
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Installation
Requirements:
- PyTorch >= 2.2
- Python >= 3.7
- CUDA >= 11.0
- NVIDIA GPU Compute Capability >= 7.0 (V100/RTX20 and higher)
- Linux OS
If you encounter any problem with 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 BUILD_EXT=1.
BUILD_EXT=1 pip install colossalai
Otherwise, CUDA kernels will be built during runtime when you actually need them.
We also keep releasing the nightly version to PyPI every week. This allows you to access the unreleased features and bug fixes in the main branch. Installation can be made via
pip install colossalai-nightly
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):
```shell
BUILD_EXT=1 pip install .
For Users with CUDA 10.2, you can still build ColossalAI from source. However, you need to manually download the cub library and copy it to the corresponding directory.
---
# install
BUILD_EXT=1 pip install .
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