--- title: "ColossalAI vs CodeRL" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-salesforce-coderl" tools: ["hpcaitech-colossalai", "salesforce-coderl"] --- # ColossalAI vs CodeRL *GraphCanon updated Jul 11, 2026* ## Verdict Pick ColossalAI when license: ColossalAI is Apache-2.0, CodeRL is BSD-3-Clause; pick CodeRL when license: CodeRL 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. [CodeRL](https://github.com/salesforce/CodeRL) has 572 stars, 68 forks, and 42 open issues, last pushed Jun 2, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [CodeRL's repository](https://github.com/salesforce/CodeRL). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22). | | Stars | 41,408 | 572 | | Forks | 4,504 | 68 | | Open issues | 501 | 42 | | 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 | Inference & Serving, Model Training | Evaluation & Observability, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [CodeRL](/tools/salesforce-coderl.md) | | --- | --- | --- | | Days since push | 46d | 39d | | Open issues (now) | 501 | 42 | | Security scan | No lockfile | 29 low (29 low) | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/salesforce-coderl/trust.md) | ## Shared compatibility - **Python**: [ColossalAI](/tools/hpcaitech-colossalai.md) - Python runtime; [CodeRL](/tools/salesforce-coderl.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, CodeRL is BSD-3-Clause. - Tags unique to ColossalAI: big-model, data-parallelism, deep-learning, distributed-computing. - Also covers Inference & Serving. - You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### Choose CodeRL if… - License: CodeRL is BSD-3-Clause, ColossalAI is Apache-2.0. - Tags unique to CodeRL: codegeneration, languagemodel, machinelearning, programsynthesis. - Also covers Evaluation & Observability. ## 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 CodeRL - Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. - 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 CodeRL? ColossalAI: Making large AI models cheaper, faster and more accessible. CodeRL: This is the official code for the paper CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning (NeurIPS22).. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over CodeRL? Choose ColossalAI over CodeRL when License: ColossalAI is Apache-2.0, CodeRL is BSD-3-Clause; Tags unique to ColossalAI: big-model, data-parallelism, deep-learning, distributed-computing; Also covers Inference & Serving; You require handling extremely large AI models with massive context windows, such as over 2M tokens. ### When should I choose CodeRL over ColossalAI? Choose CodeRL over ColossalAI when License: CodeRL is BSD-3-Clause, ColossalAI is Apache-2.0; Tags unique to CodeRL: codegeneration, languagemodel, machinelearning, programsynthesis; Also covers Evaluation & Observability. ### 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 CodeRL? Evaluation & Observability: Defer heavyweight eval infra only until you have real traffic - never skip it once users depend on answers. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is ColossalAI or CodeRL more popular on GitHub? ColossalAI has more GitHub stars (41,408 vs 572). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and CodeRL open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, CodeRL: BSD-3-Clause). ### Where can I find alternatives to ColossalAI or CodeRL? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [CodeRL alternatives](/tools/salesforce-coderl/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [CodeRL markdown twin](/tools/salesforce-coderl/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-salesforce-coderl.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or CodeRL? ColossalAI: Steady. CodeRL: 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 CodeRL? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [CodeRL trust report](/tools/salesforce-coderl/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/_