--- title: "amazon-sagemaker-examples vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/aws-amazon-sagemaker-examples-vs-hpcaitech-colossalai" tools: ["aws-amazon-sagemaker-examples", "hpcaitech-colossalai"] --- # amazon-sagemaker-examples vs ColossalAI *GraphCanon updated Jul 15, 2026* ## Verdict Pick amazon-sagemaker-examples when amazon-sagemaker-examples is primarily Jupyter Notebook; ColossalAI is Python; pick ColossalAI when colossalAI is primarily Python; amazon-sagemaker-examples is Jupyter Notebook. [amazon-sagemaker-examples](https://sagemaker-examples.readthedocs.io) reports 11k GitHub stars, 7.0k forks, and 849 open issues, last pushed Jul 7, 2026. [ColossalAI](https://www.colossalai.org) has 41k stars, 4.5k forks, and 499 open issues, last pushed Jul 13, 2026. Figures are from public GitHub metadata via [amazon-sagemaker-examples's repository](https://github.com/aws/amazon-sagemaker-examples) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker. | Making large AI models cheaper, faster and more accessible | | Stars | 10,971 | 41,413 | | Forks | 6,969 | 4,502 | | Open issues | 849 | 499 | | Language | Jupyter Notebook | 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 | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [amazon-sagemaker-examples](/tools/aws-amazon-sagemaker-examples.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Active (82%) | Very active (96%) | | Days since push | 7d | 0d | | Open issues (now) | 849 | 499 | | Full report | [trust report](/tools/aws-amazon-sagemaker-examples/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 amazon-sagemaker-examples if… - amazon-sagemaker-examples is primarily Jupyter Notebook; ColossalAI is Python. - Tags unique to amazon-sagemaker-examples: aws, data-science, examples, inference. ### Choose ColossalAI if… - ColossalAI is primarily Python; amazon-sagemaker-examples is Jupyter Notebook. - Tags unique to ColossalAI: ai, big model, data-parallelism, distributed-computing. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ## When NOT to use amazon-sagemaker-examples - Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. - 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 amazon-sagemaker-examples and ColossalAI? amazon-sagemaker-examples: Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.. 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 amazon-sagemaker-examples over ColossalAI? Choose amazon-sagemaker-examples over ColossalAI when amazon-sagemaker-examples is primarily Jupyter Notebook; ColossalAI is Python; Tags unique to amazon-sagemaker-examples: aws, data-science, examples, inference. ### When should I choose ColossalAI over amazon-sagemaker-examples? Choose ColossalAI over amazon-sagemaker-examples when ColossalAI is primarily Python; amazon-sagemaker-examples is Jupyter Notebook; Tags unique to ColossalAI: ai, big model, data-parallelism, distributed-computing; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I avoid amazon-sagemaker-examples? Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic. 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 amazon-sagemaker-examples or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,413 vs 10,971). Stars measure visibility, not whether either tool fits your constraints. ### Are amazon-sagemaker-examples and ColossalAI open source? Yes - both are open-source projects on GitHub (amazon-sagemaker-examples: Apache-2.0, ColossalAI: Apache-2.0). ### Where can I find alternatives to amazon-sagemaker-examples or ColossalAI? GraphCanon lists graph-backed alternatives at [amazon-sagemaker-examples alternatives](/tools/aws-amazon-sagemaker-examples/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([amazon-sagemaker-examples markdown twin](/tools/aws-amazon-sagemaker-examples/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/aws-amazon-sagemaker-examples-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, amazon-sagemaker-examples or ColossalAI? amazon-sagemaker-examples: Active. ColossalAI: Very active. 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 amazon-sagemaker-examples and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [amazon-sagemaker-examples trust report](/tools/aws-amazon-sagemaker-examples/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples`](/api/graphcanon/graph?tool=aws-amazon-sagemaker-examples) - 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/_