--- title: "ColossalAI vs keras" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-keras-team-keras" tools: ["hpcaitech-colossalai", "keras-team-keras"] --- # ColossalAI vs keras *GraphCanon updated Jul 12, 2026* ## Verdict Pick ColossalAI when tags unique to ColossalAI: ai, big-model, heterogeneous-training, foundation models; pick keras when tags unique to keras: data-science, neural-networks, machine-learning, python. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [keras](http://keras.io/) has 64k stars, 20k forks, and 228 open issues, last pushed Jul 7, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [keras's repository](https://github.com/keras-team/keras). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [keras](/tools/keras-team-keras.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Deep Learning for humans | | Stars | 41,408 | 64,191 | | Forks | 4,504 | 19,752 | | Open issues | 501 | 228 | | 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._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [keras](/tools/keras-team-keras.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 46d | 4d | | Open issues (now) | 501 | 228 | | Security scan | No lockfile | No criticals | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/keras-team-keras/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [keras](/tools/keras-team-keras.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: ai, big-model, heterogeneous-training, foundation models. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose keras if… - Tags unique to keras: data-science, neural-networks, machine-learning, python. - More GitHub stars (64k vs 41k) - visibility, not fit. ## 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 keras - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between ColossalAI and keras? ColossalAI: Making large AI models cheaper, faster and more accessible. keras: Deep Learning for humans. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over keras? Choose ColossalAI over keras when Tags unique to ColossalAI: ai, big-model, heterogeneous-training, foundation models; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose keras over ColossalAI? Choose keras over ColossalAI when Tags unique to keras: data-science, neural-networks, machine-learning, python; More GitHub stars (64k vs 41k) - visibility, not fit. ### 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 keras? Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is ColossalAI or keras more popular on GitHub? keras has more GitHub stars (64,191 vs 41,408). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and keras open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, keras: Apache-2.0). ### Where can I find alternatives to ColossalAI or keras? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [keras alternatives](/tools/keras-team-keras/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [keras markdown twin](/tools/keras-team-keras/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-keras-team-keras.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or keras? ColossalAI: Steady. keras: 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 ColossalAI and keras? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [keras trust report](/tools/keras-team-keras/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/_