--- title: "tensorflow-federated vs ColossalAI" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/google-parfait-tensorflow-federated-vs-hpcaitech-colossalai" tools: ["google-parfait-tensorflow-federated", "hpcaitech-colossalai"] --- # tensorflow-federated vs ColossalAI *GraphCanon updated Jul 11, 2026* ## Verdict Pick tensorflow-federated when tags unique to tensorflow-federated: python; pick ColossalAI when tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training. [tensorflow-federated](https://github.com/google-parfait/tensorflow-federated) reports 2.4k GitHub stars, 605 forks, and 290 open issues, last pushed Jul 10, 2026. [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 [tensorflow-federated's repository](https://github.com/google-parfait/tensorflow-federated) and [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI). | | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Tagline | An open-source framework for machine learning and other computations on decentralized data. | Making large AI models cheaper, faster and more accessible | | Stars | 2,442 | 41,408 | | Forks | 605 | 4,504 | | Open issues | 290 | 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 | Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [tensorflow-federated](/tools/google-parfait-tensorflow-federated.md) | [ColossalAI](/tools/hpcaitech-colossalai.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Steady (60%) | | Days since push | 1d | 46d | | Open issues (now) | 290 | 501 | | Full report | [trust report](/tools/google-parfait-tensorflow-federated/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 tensorflow-federated if… - Tags unique to tensorflow-federated: python. - More recently updated (last pushed Jul 10, 2026). ### Choose ColossalAI if… - Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ## When NOT to use tensorflow-federated - 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 tensorflow-federated and ColossalAI? tensorflow-federated: An open-source framework for machine learning and other computations on decentralized data.. 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 tensorflow-federated over ColossalAI? Choose tensorflow-federated over ColossalAI when Tags unique to tensorflow-federated: python; More recently updated (last pushed Jul 10, 2026). ### When should I choose ColossalAI over tensorflow-federated? Choose ColossalAI over tensorflow-federated when Tags unique to ColossalAI: deep-learning, ai, big-model, heterogeneous-training; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I avoid tensorflow-federated? 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 tensorflow-federated or ColossalAI more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 2,442). Stars measure visibility, not whether either tool fits your constraints. ### Are tensorflow-federated and ColossalAI open source? Yes - both are open-source projects on GitHub (tensorflow-federated: Apache-2.0, ColossalAI: Apache-2.0). ### Where can I find alternatives to tensorflow-federated or ColossalAI? GraphCanon lists graph-backed alternatives at [tensorflow-federated alternatives](/tools/google-parfait-tensorflow-federated/alternatives) and [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) ([tensorflow-federated markdown twin](/tools/google-parfait-tensorflow-federated/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/google-parfait-tensorflow-federated-vs-hpcaitech-colossalai.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, tensorflow-federated or ColossalAI? tensorflow-federated: Very active. 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 tensorflow-federated and ColossalAI? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [tensorflow-federated trust report](/tools/google-parfait-tensorflow-federated/trust); [ColossalAI trust report](/tools/hpcaitech-colossalai/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=google-parfait-tensorflow-federated`](/api/graphcanon/graph?tool=google-parfait-tensorflow-federated) - 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/_