--- title: "autoai vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/blobcity-autoai-vs-hpcaitech-colossalai" tools: ["blobcity-autoai", "hpcaitech-colossalai"] --- # autoai vs ColossalAI *GraphCanon updated Jul 11, 2026* ## Verdict Pick autoai when tags unique to autoai: automl, ml, machine-learning, codegen; pick ColossalAI when tags unique to ColossalAI: big-model, heterogeneous-training, foundation models, data-parallelism. [autoai](https://github.com/blobcity/autoai) reports 186 GitHub stars, 46 forks, and 9 open issues, last pushed Mar 25, 2025. [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 [autoai's repository](https://github.com/blobcity/autoai) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [autoai](/tools/blobcity-autoai.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation. | Making large AI models cheaper, faster and more accessible | | Stars | 186 | 41,408 | | Forks | 46 | 4,504 | | Open issues | 9 | 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, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [autoai](/tools/blobcity-autoai.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 473d | 46d | | Open issues (now) | 9 | 501 | | Security scan | 12 low (12 low) | No lockfile | | Full report | [trust report](/tools/blobcity-autoai/trust.md) | [trust report](/tools/hpcaitech-colossalai/trust.md) | ## Shared compatibility - **Python**: [autoai](/tools/blobcity-autoai.md) - Python runtime; [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime ## 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 autoai if… - Tags unique to autoai: automl, ml, machine-learning, codegen. - Leaner open-issue backlog (9). ### Choose ColossalAI if… - Tags unique to ColossalAI: big-model, heterogeneous-training, foundation models, data-parallelism. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. - More GitHub stars (41k vs 186) - visibility, not fit. ## When NOT to use autoai - Last GitHub push was 474 days ago (dormant maintenance, Mar 25, 2025). Validate activity before betting a new project on autoai. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ## 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 autoai and ColossalAI? autoai: Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.. 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 autoai over ColossalAI? Choose autoai over ColossalAI when Tags unique to autoai: automl, ml, machine-learning, codegen; Leaner open-issue backlog (9). ### When should I choose ColossalAI over autoai? Choose ColossalAI over autoai when Tags unique to ColossalAI: big-model, heterogeneous-training, foundation models, data-parallelism; You require handling extremely large AI models with massive context windows, such as over 2M tokens; More GitHub stars (41k vs 186) - visibility, not fit. ### When should I avoid autoai? Last GitHub push was 474 days ago (dormant maintenance, Mar 25, 2025). Validate activity before betting a new project on autoai. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. ### 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 autoai or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 186). Stars measure visibility, not whether either tool fits your constraints. ### Are autoai and ColossalAI open source? Yes - both are open-source projects on GitHub (autoai: Apache-2.0, ColossalAI: Apache-2.0). ### Where can I find alternatives to autoai or ColossalAI? GraphCanon lists graph-backed alternatives at [autoai alternatives](/tools/blobcity-autoai/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([autoai markdown twin](/tools/blobcity-autoai/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/blobcity-autoai-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, autoai or ColossalAI? autoai: 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 autoai and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [autoai trust report](/tools/blobcity-autoai/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=blobcity-autoai`](/api/graphcanon/graph?tool=blobcity-autoai) - 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/_