Comparison
ColossalAI vs ray
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.
Markdown twin · ColossalAI alternatives · ray alternatives
GraphCanon updated today
Trust & integrity
| Signal | ColossalAI | ray |
|---|---|---|
| Maintenance | Steady (46d since push) As of today · github_public_v1 | Very active (0d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| Security (OSV) | No lockfile As of today · none | No lockfile As of today · none |
Tagline
- 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.
Stars
- ColossalAI
- 41k
- ray
- 43k
Forks
- ColossalAI
- 4.5k
- ray
- 7.8k
Open issues
- ColossalAI
- 501
- ray
- 3.5k
Language
- ColossalAI
- Python
- ray
- Python
Adopt for
- ColossalAI
- ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
- ray
- Ray offers a core distributed runtime and specialized libraries for optimizing ML workloads in Python.
Persona
- ColossalAI
- -
- ray
- -
Runtime
- ColossalAI
- -
- ray
- -
License
- ColossalAI
- Apache-2.0
- ray
- Apache-2.0 license allows for both commercial and private use without the need to open-source your entire project.
Last pushed
- ColossalAI
- May 25, 2026
- ray
- Jul 11, 2026
Categories
- ColossalAI
- Model Training, Inference & Serving
- ray
- Model Training, Inference & Serving
Trust and health
Maintenance
- ColossalAI
- Steady (60%)
- ray
- Very active (96%)
Days since push
- ColossalAI
- 46d
- ray
- 0d
Open issues (now)
- ColossalAI
- 501
- ray
- 3.5k
Full report
- ColossalAI
- Trust report
- ray
- Trust report
Choose ColossalAI if…
- Tags unique to ColossalAI: ai, big-model, heterogeneous-training, foundation-models.
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- Leaner open-issue backlog (501).
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.
Choose ray if…
- Tags unique to ray: data-science, distributed, deployment, machine-learning.
- 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 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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (hpcaitech/ColossalAI) · observed Jul 11, 2026
- GitHub forks (hpcaitech/ColossalAI) · observed Jul 11, 2026
- Last push (hpcaitech/ColossalAI) · observed May 25, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (ray-project/ray) · observed Jul 11, 2026
- GitHub forks (ray-project/ray) · observed Jul 11, 2026
- Last push (ray-project/ray) · observed Jul 11, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ColossalAI 41k · ray 43k (synced Jul 11, 2026).
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, heterogeneous-training, foundation-models; 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, distributed, deployment, machine-learning; 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 and ray alternatives (ColossalAI markdown twin, ray markdown twin), 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 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; ray trust report.