Comparison
ColossalAI vs keras
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.
Markdown twin · ColossalAI alternatives · keras alternatives
GraphCanon updated today
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Trust & integrity
| Signal | ColossalAI | keras |
|---|---|---|
| Maintenance | Steady (46d since push) As of today · github_public_v1 | Very active (4d 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 criticals As of today · osv@v1 |
Tagline
- ColossalAI
- Making large AI models cheaper, faster and more accessible
- keras
- Deep Learning for humans
Stars
- ColossalAI
- 41k
- keras
- 64k
Forks
- ColossalAI
- 4.5k
- keras
- 20k
Open issues
- ColossalAI
- 501
- keras
- 228
Language
- ColossalAI
- Python
- keras
- 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.
- keras
- -
Persona
- ColossalAI
- -
- keras
- -
Runtime
- ColossalAI
- -
- keras
- -
License
- ColossalAI
- Apache-2.0
- keras
- Apache-2.0
Last pushed
- ColossalAI
- May 25, 2026
- keras
- Jul 7, 2026
Categories
- ColossalAI
- Model Training, Inference & Serving
- keras
- Model Training
Trust and health
Maintenance
- ColossalAI
- Steady (60%)
- keras
- Very active (96%)
Days since push
- ColossalAI
- 46d
- keras
- 4d
Open issues (now)
- ColossalAI
- 501
- keras
- 228
Security scan
- ColossalAI
- No lockfile
- keras
- No criticals
Full report
- ColossalAI
- Trust report
- keras
- Trust report
Shared compatibility
- Python · ColossalAI: Python runtime · keras: Python runtime
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.
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.
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 (keras-team/keras) · observed Jul 11, 2026
- GitHub forks (keras-team/keras) · observed Jul 11, 2026
- Last push (keras-team/keras) · observed Jul 7, 2026
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ColossalAI 41k · keras 64k (synced Jul 11, 2026).
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 and keras alternatives (ColossalAI markdown twin, keras 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 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; keras trust report.