Comparison
DeepSpeed vs ColossalAI
Verdict
Pick DeepSpeed if decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression; 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.
Markdown twin · DeepSpeed alternatives · ColossalAI alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | DeepSpeed | ColossalAI |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Steady (46d 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
- DeepSpeed
- Deep learning optimization library for efficient distributed training and inference
- ColossalAI
- Making large AI models cheaper, faster and more accessible
Stars
- DeepSpeed
- 43k
- ColossalAI
- 41k
Forks
- DeepSpeed
- 4.9k
- ColossalAI
- 4.5k
Open issues
- DeepSpeed
- 1.3k
- ColossalAI
- 501
Language
- DeepSpeed
- Python
- ColossalAI
- Python
Adopt for
- DeepSpeed
- Decisions for DeepSpeed use are driven by its capacity to handle large models efficiently using techniques such as data parallelism, model parallelism, pipeline parallelism, and compression.
- ColossalAI
- ColossalAI is a Python library that leverages advanced parallelism techniques for more efficient and cost-effective development of large-scale AI models.
Persona
- DeepSpeed
- -
- ColossalAI
- -
Runtime
- DeepSpeed
- -
- ColossalAI
- -
License
- DeepSpeed
- Apache-2.0
- ColossalAI
- Apache-2.0
Last pushed
- DeepSpeed
- Jul 11, 2026
- ColossalAI
- May 25, 2026
Categories
- DeepSpeed
- Model Training, Inference & Serving
- ColossalAI
- Model Training, Inference & Serving
Trust and health
Maintenance
- DeepSpeed
- Very active (96%)
- ColossalAI
- Steady (60%)
Days since push
- DeepSpeed
- 0d
- ColossalAI
- 46d
Open issues (now)
- DeepSpeed
- 1.3k
- ColossalAI
- 501
Full report
- DeepSpeed
- Trust report
- ColossalAI
- Trust report
Choose DeepSpeed if…
- Tags unique to DeepSpeed: gpu, compression, machine-learning, billion-parameters.
- - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)
- More GitHub stars (43k vs 41k) - visibility, not fit.
When NOT to use DeepSpeed
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs
- - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
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.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Jul 11, 2026
- Last push (deepspeedai/DeepSpeed) · 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 (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 on cards: DeepSpeed 43k · ColossalAI 41k (synced Jul 11, 2026).
Common questions
- What is the difference between DeepSpeed and ColossalAI?
- DeepSpeed: Deep learning optimization library for efficient distributed training and inference. ColossalAI: Making large AI models cheaper, faster and more accessible. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepSpeed over ColossalAI?
- Choose DeepSpeed over ColossalAI when Tags unique to DeepSpeed: gpu, compression, machine-learning, billion-parameters; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters); More GitHub stars (43k vs 41k) - visibility, not fit.
- When should I choose ColossalAI over DeepSpeed?
- Choose ColossalAI over DeepSpeed 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 avoid DeepSpeed?
- - When you are working in an environment that only supports CPU-based training without access to CUDA or ROCm compatible GPUs - If your project's PyTorch version is less than 2.0, DeepSpeed may not support all of its features and optimizations effectively
- 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.
- Is DeepSpeed or ColossalAI more popular on GitHub?
- DeepSpeed has more GitHub stars (42,685 vs 41,408). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSpeed and ColossalAI open source?
- Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, ColossalAI: Apache-2.0).
- Where can I find alternatives to DeepSpeed or ColossalAI?
- GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and ColossalAI alternatives (DeepSpeed markdown twin, ColossalAI 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, DeepSpeed or ColossalAI?
- DeepSpeed: Very active. ColossalAI: 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 DeepSpeed and ColossalAI?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; ColossalAI trust report.