Practical overview
When it fits
Use Colossalai when you need framework-level components for distributed model training or inference and can work in the documented Linux, Python, PyTorch, CUDA and NVIDIA GPU environment.
Who it is for
- Developers building distributed training workflows for large AI models.
- Teams creating model-parallel or pipeline-parallel training setups.
- Developers exploring inference workflows using the framework.
Documented uses
- Distribute large-model training across compute resources.
- Structure a training workflow using data, model or pipeline parallelism.
- Explore heterogeneous training configurations.
- Build AI inference workflows with the framework.
Requirements & limits
The framework is available from a public GitHub repository declaring an Apache-2.0 license. The README documents installation from PyPI or from source and states Linux support.
- The README specifies Linux and particular Python, PyTorch, CUDA and NVIDIA GPU requirements.
- PyTorch extensions are not built by default during installation; the README documents
BUILD_EXT=1for building extensions. - The repository provides examples and a separate Colossal-Inference code area for exploring project use.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Install Colossalai from PyPI on Linux
Install the package in an environment that meets the repository's stated requirements.
Before you start
- Linux
- Python 3.10 or later and below 3.13
- PyTorch 2.2 or later
- CUDA 11.0 or later
- An NVIDIA GPU with compute capability 7.0 or higher
Steps
Prepare an environment matching the listed installation requirements.
Run the command below.
pip install colossalai
Expected outcome
The Colossalai package is installed from PyPI; PyTorch extensions are not built during installation by default.
Guide limitations
- The README says only Linux is supported.
- CUDA kernels are built at runtime when needed unless extensions are built during installation.
Install Colossalai from the repository
Install the repository's main-branch version using the documented source steps.
Before you start
- Linux
- Python 3.10 or later and below 3.13
- PyTorch 2.2 or later
- CUDA 11.0 or later
- An NVIDIA GPU with compute capability 7.0 or higher
Steps
Clone
https://github.com/hpcaitech/ColossalAI.git.Change into the
ColossalAIdirectory.Run the command below.
pip install .
Expected outcome
Colossalai is installed from the cloned repository; the README says CUDA/C++ kernels are not compiled by default and are built at runtime when needed.
Guide limitations
- The repository installation follows the main branch.
- The README says only Linux is supported.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Colossalai | Distribute large-model training across compute resources. | Linux, Self-hosted | The framework is available from a public GitHub repository declaring an Apache-2.0 license. The README documents installation from PyPI or from source and states Linux support. |
| Cloud AI SDK | Adapt and run documented model recipes for computer-vision and NLP inference. | Cloud, Server | The GitHub repository is public. Its public visibility does not establish license terms. |
| Turboquant | Replace a model’s existing inference cache with a compressed cache. | Cloud, Linux, macOS, Self-hosted | The project describes itself as open source. The README documents installation through pip or from a source checkout. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- hpcaitech/ColossalAI · Official homepage title · retained in its source language