--- title: "ColossalAI vs awesome-mlops" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-kelvins-awesome-mlops" tools: ["hpcaitech-colossalai", "kelvins-awesome-mlops"] --- # ColossalAI vs awesome-mlops *GraphCanon updated Jul 11, 2026* ## Verdict Pick ColossalAI when tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; pick awesome-mlops when tags unique to awesome-mlops: awesome, data-science, ml, mle. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [awesome-mlops](https://github.com/kelvins/awesome-mlops) has 5.2k stars, 757 forks, and 67 open issues, last pushed Apr 29, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [awesome-mlops's repository](https://github.com/kelvins/awesome-mlops). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | :sunglasses: A curated list of awesome MLOps tools | | Stars | 41,408 | 5,208 | | Forks | 4,504 | 757 | | Open issues | 501 | 67 | | 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 | - | | Categories | Model Training, Inference & Serving | Model Training, Computer Vision, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [awesome-mlops](/tools/kelvins-awesome-mlops.md) | | --- | --- | --- | | Days since push | 46d | 73d | | Open issues (now) | 501 | 67 | | Owner type | Organization | User | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/kelvins-awesome-mlops/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [awesome-mlops](/tools/kelvins-awesome-mlops.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 ColossalAI if… - Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. - More GitHub stars (41k vs 5.2k) - visibility, not fit. ### Choose awesome-mlops if… - Tags unique to awesome-mlops: awesome, data-science, ml, mle. - Also covers Computer Vision. - Leaner open-issue backlog (67). ## 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. ## When NOT to use awesome-mlops - 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. ## Common questions ### What is the difference between ColossalAI and awesome-mlops? ColossalAI: Making large AI models cheaper, faster and more accessible. awesome-mlops: :sunglasses: A curated list of awesome MLOps tools. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over awesome-mlops? Choose ColossalAI over awesome-mlops when Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; You require handling extremely large AI models with massive context windows, such as over 2M tokens; More GitHub stars (41k vs 5.2k) - visibility, not fit. ### When should I choose awesome-mlops over ColossalAI? Choose awesome-mlops over ColossalAI when Tags unique to awesome-mlops: awesome, data-science, ml, mle; Also covers Computer Vision; Leaner open-issue backlog (67). ### 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. ### When should I avoid awesome-mlops? 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. ### Is ColossalAI or awesome-mlops more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 5,208). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and awesome-mlops open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to ColossalAI or awesome-mlops? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [awesome-mlops alternatives](/tools/kelvins-awesome-mlops/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [awesome-mlops markdown twin](/tools/kelvins-awesome-mlops/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/hpcaitech-colossalai-vs-kelvins-awesome-mlops.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or awesome-mlops? ColossalAI: Steady. awesome-mlops: 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 ColossalAI and awesome-mlops? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [awesome-mlops trust report](/tools/kelvins-awesome-mlops/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=hpcaitech-colossalai`](/api/graphcanon/graph?tool=hpcaitech-colossalai) - 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/_