--- title: "ColossalAI vs RLTF" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-zyq-scut-rltf" tools: ["hpcaitech-colossalai", "zyq-scut-rltf"] --- # ColossalAI vs RLTF *GraphCanon updated Jul 11, 2026* ## Verdict Pick ColossalAI when license: ColossalAI is Apache-2.0, RLTF is BSD-3-Clause; pick RLTF when license: RLTF is BSD-3-Clause, ColossalAI is Apache-2.0. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [RLTF](https://github.com/Zyq-scut/RLTF) has 135 stars, 7 forks, and 0 open issues, last pushed Oct 5, 2024. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [RLTF's repository](https://github.com/Zyq-scut/RLTF). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Accepted by Transactions on Machine Learning Research (TMLR) | | Stars | 41,408 | 135 | | Forks | 4,504 | 7 | | Open issues | 501 | 0 | | 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 | BSD-3-Clause | | Categories | Model Training, Inference & Serving | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [RLTF](/tools/zyq-scut-rltf.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 46d | 644d | | Open issues (now) | 501 | 0 | | Owner type | Organization | User | | Security scan | No lockfile | 75 low (75 low) | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/zyq-scut-rltf/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [RLTF](/tools/zyq-scut-rltf.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… - License: ColossalAI is Apache-2.0, RLTF is BSD-3-Clause. - 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. ### Choose RLTF if… - License: RLTF is BSD-3-Clause, ColossalAI is Apache-2.0. - Tags unique to RLTF: python. - Leaner open-issue backlog (0). ## 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 RLTF - Last GitHub push was 644 days ago (dormant maintenance, Oct 5, 2024). Validate activity before betting a new project on RLTF. - 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 RLTF? ColossalAI: Making large AI models cheaper, faster and more accessible. RLTF: Accepted by Transactions on Machine Learning Research (TMLR). See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over RLTF? Choose ColossalAI over RLTF when License: ColossalAI is Apache-2.0, RLTF is BSD-3-Clause; 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 choose RLTF over ColossalAI? Choose RLTF over ColossalAI when License: RLTF is BSD-3-Clause, ColossalAI is Apache-2.0; Tags unique to RLTF: python; Leaner open-issue backlog (0). ### 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 RLTF? Last GitHub push was 644 days ago (dormant maintenance, Oct 5, 2024). Validate activity before betting a new project on RLTF. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is ColossalAI or RLTF more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 135). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and RLTF open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, RLTF: BSD-3-Clause). ### Where can I find alternatives to ColossalAI or RLTF? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [RLTF alternatives](/tools/zyq-scut-rltf/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [RLTF markdown twin](/tools/zyq-scut-rltf/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-zyq-scut-rltf.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or RLTF? ColossalAI: Steady. RLTF: Dormant. 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 RLTF? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [RLTF trust report](/tools/zyq-scut-rltf/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/_