--- title: "UER-py vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/dbiir-uer-py-vs-hpcaitech-colossalai" tools: ["dbiir-uer-py", "hpcaitech-colossalai"] --- # UER-py vs ColossalAI *GraphCanon updated Jul 11, 2026* ## Verdict Pick UER-py when tags unique to UER-py: albert, bart, bert, chinese; pick ColossalAI when tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning. [UER-py](https://github.com/dbiir/UER-py/wiki) reports 3.1k GitHub stars, 520 forks, and 136 open issues, last pushed May 9, 2024. [ColossalAI](https://www.colossalai.org) has 41k stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. Figures are from public GitHub metadata via [UER-py's repository](https://github.com/dbiir/UER-py) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [UER-py](/tools/dbiir-uer-py.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo | Making large AI models cheaper, faster and more accessible | | Stars | 3,109 | 41,408 | | Forks | 520 | 4,504 | | Open issues | 136 | 501 | | Language | Python | Python | | Adopt for | - | ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [UER-py](/tools/dbiir-uer-py.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 793d | 46d | | Open issues (now) | 136 | 501 | | Full report | [trust report](/tools/dbiir-uer-py/trust.md) | [trust report](/tools/hpcaitech-colossalai/trust.md) | ## Decision facts: ColossalAI - **Adopt for:** ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models. ## Choose when ### Choose UER-py if… - Tags unique to UER-py: albert, bart, bert, chinese. - Leaner open-issue backlog (136). ### Choose ColossalAI if… - Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ## When NOT to use UER-py - Last GitHub push was 793 days ago (dormant maintenance, May 9, 2024). Validate activity before betting a new project on UER-py. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## When NOT to use ColossalAI - 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). - You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ## Common questions ### What is the difference between UER-py and ColossalAI? UER-py: Open Source Pre-training Model Framework in PyTorch & Pre-trained Model Zoo. ColossalAI: Making large AI models cheaper, faster and more accessible. See the comparison table for live GitHub stats and shared categories. ### When should I choose UER-py over ColossalAI? Choose UER-py over ColossalAI when Tags unique to UER-py: albert, bart, bert, chinese; Leaner open-issue backlog (136). ### When should I choose ColossalAI over UER-py? Choose ColossalAI over UER-py when Tags unique to ColossalAI: ai, big-model, data-parallelism, deep-learning; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I avoid UER-py? Last GitHub push was 793 days ago (dormant maintenance, May 9, 2024). Validate activity before betting a new project on UER-py. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### When should I avoid ColossalAI? 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). You cannot satisfy the minimum hardware and software requirements specified, such as PyTorch >= 2.2 and Python >= 3.7. ### Is UER-py or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 3,109). Stars measure visibility, not whether either tool fits your constraints. ### Are UER-py and ColossalAI open source? Yes - both are open-source projects on GitHub (UER-py: Apache-2.0, ColossalAI: Apache-2.0). ### Where can I find alternatives to UER-py or ColossalAI? GraphCanon lists graph-backed alternatives at [UER-py alternatives](/tools/dbiir-uer-py/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([UER-py markdown twin](/tools/dbiir-uer-py/alternatives.md), [ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md)), ranked by typed relationship edges rather than popularity votes. ### Is there a machine-readable version of this comparison? Yes. The markdown twin at [this comparison](/compare/dbiir-uer-py-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, UER-py or ColossalAI? UER-py: Dormant. ColossalAI: Steady. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance. ### Where are the full trust reports for UER-py and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [UER-py trust report](/tools/dbiir-uer-py/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=dbiir-uer-py`](/api/graphcanon/graph?tool=dbiir-uer-py) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_