Comparison
DeepSpeed vs FATE
Verdict
Pick DeepSpeed when tags unique to DeepSpeed: deep-learning, gpu, compression, billion-parameters; pick FATE when tags unique to FATE: fate, algorithm, python, federated-learning.
Markdown twin · DeepSpeed alternatives · FATE alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | DeepSpeed | FATE |
|---|---|---|
| Maintenance | Very active (0d since push) As of today · github_public_v1 | Dormant (599d 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
- FATE
- An Industrial Grade Federated Learning Framework
Stars
- DeepSpeed
- 43k
- FATE
- 6.1k
Forks
- DeepSpeed
- 4.9k
- FATE
- 1.6k
Open issues
- DeepSpeed
- 1.3k
- FATE
- 21
Language
- DeepSpeed
- Python
- FATE
- 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.
- FATE
- -
Persona
- DeepSpeed
- -
- FATE
- -
Runtime
- DeepSpeed
- -
- FATE
- -
License
- DeepSpeed
- Apache-2.0
- FATE
- Apache-2.0
Last pushed
- DeepSpeed
- Jul 11, 2026
- FATE
- Nov 19, 2024
Categories
- DeepSpeed
- Model Training, Inference & Serving
- FATE
- Model Training, Computer Vision, Inference & Serving
Trust and health
Maintenance
- DeepSpeed
- Very active (96%)
- FATE
- Dormant (18%)
Days since push
- DeepSpeed
- 0d
- FATE
- 599d
Open issues (now)
- DeepSpeed
- 1.3k
- FATE
- 21
Full report
- DeepSpeed
- Trust report
- FATE
- Trust report
Choose DeepSpeed if…
- Tags unique to DeepSpeed: deep-learning, gpu, compression, 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 6.1k) - 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 FATE if…
- Tags unique to FATE: fate, algorithm, python, federated-learning.
- Also covers Computer Vision.
- Leaner open-issue backlog (21).
When NOT to use FATE
- Last GitHub push was 599 days ago (dormant maintenance, Nov 19, 2024). Validate activity before betting a new project on FATE.
- 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 (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 (FederatedAI/FATE) · observed Jul 11, 2026
- GitHub forks (FederatedAI/FATE) · observed Jul 11, 2026
- Last push (FederatedAI/FATE) · observed Nov 19, 2024
- License file (Apache-2.0) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepSpeed 43k · FATE 6.1k (synced Jul 11, 2026).
Common questions
- What is the difference between DeepSpeed and FATE?
- DeepSpeed: Deep learning optimization library for efficient distributed training and inference. FATE: An Industrial Grade Federated Learning Framework. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepSpeed over FATE?
- Choose DeepSpeed over FATE when Tags unique to DeepSpeed: deep-learning, gpu, compression, 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 6.1k) - visibility, not fit.
- When should I choose FATE over DeepSpeed?
- Choose FATE over DeepSpeed when Tags unique to FATE: fate, algorithm, python, federated-learning; Also covers Computer Vision; Leaner open-issue backlog (21).
- 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 FATE?
- Last GitHub push was 599 days ago (dormant maintenance, Nov 19, 2024). Validate activity before betting a new project on FATE. 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 DeepSpeed or FATE more popular on GitHub?
- DeepSpeed has more GitHub stars (42,685 vs 6,084). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSpeed and FATE open source?
- Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, FATE: Apache-2.0).
- Where can I find alternatives to DeepSpeed or FATE?
- GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and FATE alternatives (DeepSpeed markdown twin, FATE 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 FATE?
- DeepSpeed: Very active. FATE: 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 DeepSpeed and FATE?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; FATE trust report.