{"data":{"slug":"hpcaitech-colossalai","name":"ColossalAI","tagline":"Making large AI models cheaper, faster and more accessible","github_url":"https://github.com/hpcaitech/ColossalAI","owner":"hpcaitech","repo":"ColossalAI","owner_avatar_url":"https://avatars.githubusercontent.com/u/88699314?v=4","primary_language":"Python","stars":41408,"forks":4504,"topics":["ai","big-model","data-parallelism","deep-learning","distributed-computing","foundation-models","heterogeneous-training","hpc","inference","large-scale","model-parallelism","pipeline-parallelism"],"archived":false,"github_pushed_at":"2026-05-25T17:39:11+00:00","maintenance_label":"Steady","url":"https://www.graphcanon.com/tools/hpcaitech-colossalai","markdown_url":"https://www.graphcanon.com/tools/hpcaitech-colossalai.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/hpcaitech-colossalai","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=hpcaitech-colossalai","description":"Making large AI models cheaper, faster and more accessible","homepage_url":"https://www.colossalai.org","license":"Apache-2.0","open_issues":501,"watchers":378,"ai_summary":"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.","readme_excerpt":"## Instant Access Top Open Models at Half the Cost\n\nSkip the hassle. Access powerful, long-context LLMs seamlessly through [**HPC-AI Model APIs**](https://hpc-ai.com/model-apis?utm_source=github&utm_medium=social&utm_campaign=promotion-colossalai).\n\nBuild your AI agents, chatbots, and RAG applications with HPC-AI Model APIs!\n\n* **Latest & Greatest Models**: Experience state-of-the-art performance with Kimi 2.5, MiniMax 2.5, and GLM 5.1. Perfect for massive 2M+ context windows and complex coding tasks.\n\n* **Unbeatable Pricing**: Stop overpaying for API endpoints. Get premier inference speed at up to 50% cheaper than OpenRouter.\n\n[**Get Started Now & Claim Your $4 Free Credits →**](https://www.hpc-ai.com/account/signup?redirectUrl=/models-console/models&invitation_code=HPCAI-MAPI&utm_source=google&utm_medium=social&utm_id=newlaunch)\n\n
\n \n \n \n
\n\n---\n\n## Installation\n\nRequirements:\n- PyTorch >= 2.2\n- Python >= 3.7\n- CUDA >= 11.0\n- [NVIDIA GPU Compute Capability](https://developer.nvidia.com/cuda-gpus) >= 7.0 (V100/RTX20 and higher)\n- Linux OS\n\nIf you encounter any problem with installation, you may want to raise an [issue](https://github.com/hpcaitech/ColossalAI/issues/new/choose) in this repository.\n\n---\n\n### Install from PyPI\n\nYou can easily install Colossal-AI with the following command. **By default, we do not build PyTorch extensions during installation.**\n\n```bash\npip install colossalai\n```\n\n**Note: only Linux is supported for now.**\n\nHowever, if you want to build the PyTorch extensions during installation, you can set `BUILD_EXT=1`.\n\n```bash\nBUILD_EXT=1 pip install colossalai\n```\n\n**Otherwise, CUDA kernels will be built during runtime when you actually need them.**\n\nWe 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.\nInstallation can be made via\n\n```bash\npip install colossalai-nightly\n```\n\n---\n\n# install colossalai\npip install .\n```\n\nBy default, we do not compile CUDA/C++ kernels. ColossalAI will build them during runtime.\nIf you want to install and enable CUDA kernel fusion (compulsory installation when using fused optimizer):\n\n```shell\nBUILD_EXT=1 pip install .\n```\n\nFor 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.\n\n```bash\n\n---\n\n# install\nBUILD_EXT=1 pip install .\n```\n\n

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