--- title: "ColossalAI vs ray" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/hpcaitech-colossalai-vs-ray-project-ray" tools: ["hpcaitech-colossalai", "ray-project-ray"] --- # ColossalAI vs ray *GraphCanon updated Jul 11, 2026* ## Verdict Pick ColossalAI if colossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models; pick ray if ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. [ColossalAI](https://www.colossalai.org) reports 41k GitHub stars, 4.5k forks, and 501 open issues, last pushed May 25, 2026. [ray](https://ray.io) has 43k stars, 7.8k forks, and 3.5k open issues, last pushed Jul 11, 2026. Figures are from public GitHub metadata via [ColossalAI's repository](https://github.com/hpcaitech/ColossalAI) and [ray's repository](https://github.com/ray-project/ray). | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Tagline | Making large AI models cheaper, faster and more accessible | Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads. | | Stars | 41,408 | 43,190 | | Forks | 4,504 | 7,785 | | Open issues | 501 | 3,461 | | 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. | Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project. | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [ColossalAI](/tools/hpcaitech-colossalai.md) | [ray](/tools/ray-project-ray.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 46d | 0d | | Open issues (now) | 501 | 3.5k | | Full report | [trust report](/tools/hpcaitech-colossalai/trust.md) | [trust report](/tools/ray-project-ray/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. ## Decision facts: ray - **Adopt for:** Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python. - **License detail:** Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project. ## Choose when ### Choose ColossalAI if… - 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. - Leaner open-issue backlog (501). ### Choose ray if… - Tags unique to ray: data-science, deployment, distributed, hyperparameter-optimization. - When you need to develop applications that require the distribution of tasks across multiple machines. - More GitHub stars (43k 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 ray - For simplistic projects or single-machine use cases, as Ray's distributed architecture may introduce unnecessary complexity. - If your project strictly adheres to languages other than Python, since most of the ecosystem and support revolves around Python. - When an environment already heavily utilizes another distributed computing framework that integrates deeply with specific needs, moving to Ray might not offer additional advantages over sticking with, - for example, an existing, well-integrated solution like Apache Spark for data processing. ## Common questions ### What is the difference between ColossalAI and ray? ColossalAI: Making large AI models cheaper, faster and more accessible. ray: Ray is an AI compute engine with a core distributed runtime and AI Libraries for accelerating ML workloads.. See the comparison table for live GitHub stats and shared categories. ### When should I choose ColossalAI over ray? Choose ColossalAI over ray when 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; Leaner open-issue backlog (501). ### When should I choose ray over ColossalAI? Choose ray over ColossalAI when Tags unique to ray: data-science, deployment, distributed, hyperparameter-optimization; When you need to develop applications that require the distribution of tasks across multiple machines; More GitHub stars (43k 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 ray? For simplistic projects or single-machine use cases, as Ray's distributed architecture may introduce unnecessary complexity. If your project strictly adheres to languages other than Python, since most of the ecosystem and support revolves around Python. When an environment already heavily utilizes another distributed computing framework that integrates deeply with specific needs, moving to Ray might not offer additional advantages over sticking with, for example, an existing, well-integrated solution like Apache Spark for data processing. ### Is ColossalAI or ray more popular on GitHub? ray has more GitHub stars (43,190 vs 41,408). Stars measure visibility, not whether either tool fits your constraints. ### Are ColossalAI and ray open source? Yes - both are open-source projects on GitHub (ColossalAI: Apache-2.0, ray: Apache-2.0). ### Where can I find alternatives to ColossalAI or ray? GraphCanon lists graph-backed alternatives at [ColossalAI alternatives](/tools/hpcaitech-colossalai/alternatives) and [ray alternatives](/tools/ray-project-ray/alternatives) ([ColossalAI markdown twin](/tools/hpcaitech-colossalai/alternatives.md), [ray markdown twin](/tools/ray-project-ray/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-ray-project-ray.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, ColossalAI or ray? ColossalAI: Steady. ray: 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 ray? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [ColossalAI trust report](/tools/hpcaitech-colossalai/trust); [ray trust report](/tools/ray-project-ray/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/_