Comparison
ColossalAI vs awesome-mlops
Verdict
Pick ColossalAI when tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; pick awesome-mlops when tags unique to awesome-mlops: engineering, data-science, ml, machine-learning.
Markdown twin · ColossalAI alternatives · awesome-mlops alternatives
GraphCanon updated today
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Trust & integrity
| Signal | ColossalAI | awesome-mlops |
|---|---|---|
| Maintenance | Steady (46d since push) As of today · github_public_v1 | Dormant (597d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of today · github_public_v1 | Not a fork · Personal 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
- awesome-mlops
- A curated list of references for MLOps
Stars
- ColossalAI
- 41k
- awesome-mlops
- 14k
Forks
- ColossalAI
- 4.5k
- awesome-mlops
- 2.1k
Open issues
- ColossalAI
- 501
- awesome-mlops
- 42
Language
- ColossalAI
- Python
- awesome-mlops
- -
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.
- awesome-mlops
- -
Persona
- ColossalAI
- -
- awesome-mlops
- -
Runtime
- ColossalAI
- -
- awesome-mlops
- -
License
- ColossalAI
- Apache-2.0
- awesome-mlops
- -
Last pushed
- ColossalAI
- May 25, 2026
- awesome-mlops
- Nov 21, 2024
Categories
- ColossalAI
- Model Training, Inference & Serving
- awesome-mlops
- Vector Databases, Model Training, Inference & Serving
Trust and health
Maintenance
- ColossalAI
- Steady (60%)
- awesome-mlops
- Dormant (18%)
Days since push
- ColossalAI
- 46d
- awesome-mlops
- 597d
Open issues (now)
- ColossalAI
- 501
- awesome-mlops
- 42
Owner type
- ColossalAI
- Organization
- awesome-mlops
- User
Full report
- ColossalAI
- Trust report
- awesome-mlops
- Trust report
Shared compatibility
- Python · ColossalAI: Python runtime · awesome-mlops: Python runtime
Choose ColossalAI if…
- Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models.
- You require handling extremely large AI models with massive context windows, such as over 2M tokens.
- More GitHub stars (41k vs 14k) - 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.
Choose awesome-mlops if…
- Tags unique to awesome-mlops: engineering, data-science, ml, machine-learning.
- Also covers Vector Databases.
- Leaner open-issue backlog (42).
When NOT to use awesome-mlops
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops.
- Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate.
- Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
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 (visenger/awesome-mlops) · observed Jul 11, 2026
- GitHub forks (visenger/awesome-mlops) · observed Jul 11, 2026
- Last push (visenger/awesome-mlops) · observed Nov 21, 2024
- License file (unknown) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: ColossalAI 41k · awesome-mlops 14k (synced Jul 11, 2026).
Common questions
- What is the difference between ColossalAI and awesome-mlops?
- ColossalAI: Making large AI models cheaper, faster and more accessible. awesome-mlops: A curated list of references for MLOps. See the comparison table for live GitHub stats and shared categories.
- When should I choose ColossalAI over awesome-mlops?
- Choose ColossalAI over awesome-mlops when Tags unique to ColossalAI: deep-learning, big-model, heterogeneous-training, foundation models; You require handling extremely large AI models with massive context windows, such as over 2M tokens; More GitHub stars (41k vs 14k) - visibility, not fit.
- When should I choose awesome-mlops over ColossalAI?
- Choose awesome-mlops over ColossalAI when Tags unique to awesome-mlops: engineering, data-science, ml, machine-learning; Also covers Vector Databases; Leaner open-issue backlog (42).
- 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 awesome-mlops?
- Last GitHub push was 597 days ago (dormant maintenance, Nov 21, 2024). Validate activity before betting a new project on awesome-mlops. Vector Databases: Don't reach for a dedicated vector DB under ~100k vectors; pgvector on your existing Postgres is simpler to operate. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. Inference & Serving: Self-hosting rarely beats a hosted API on cost until you have steady, high-volume traffic.
- Is ColossalAI or awesome-mlops more popular on GitHub?
- ColossalAI has more GitHub stars (41,408 vs 13,952). Stars measure visibility, not whether either tool fits your constraints.
- Are ColossalAI and awesome-mlops open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to ColossalAI or awesome-mlops?
- GraphCanon lists graph-backed alternatives at ColossalAI alternatives and awesome-mlops alternatives (ColossalAI markdown twin, awesome-mlops 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 awesome-mlops?
- ColossalAI: Steady. awesome-mlops: 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 awesome-mlops?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: ColossalAI trust report; awesome-mlops trust report.