Comparison
DeepSpeed vs raft
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 raft if rAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications.
Markdown twin · DeepSpeed alternatives · raft alternatives
GraphCanon updated today
Trust & integrity
| Signal | DeepSpeed | raft |
|---|---|---|
| Maintenance | Very active (0d since push) As of 2w · github_public_v1 | Very active (1d since push) As of today · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of today · github_public_v1 |
| OSV dependency advisories | No lockfile (source not queried) As of 1mo · osv@v1 | No lockfile (source not queried) As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- DeepSpeed
- Deep learning optimization library for efficient distributed training and inference
- raft
- A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.
Stars
- DeepSpeed
- 43k
- raft
- 1.0k
Forks
- DeepSpeed
- 4.9k
- raft
- 248
Open issues
- DeepSpeed
- 1.3k
- raft
- 446
Language
- DeepSpeed
- Python
- raft
- Cuda
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.
- raft
- RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications.
Persona
- DeepSpeed
- -
- raft
- -
Runtime
- DeepSpeed
- -
- raft
- -
License
- DeepSpeed
- Apache-2.0
- raft
- Apache-2.0
Last pushed
- DeepSpeed
- Aug 6, 2026
- raft
- Aug 22, 2026
Categories
- DeepSpeed
- Inference & Serving, Model Training
- raft
- Data & Retrieval, Model Training
Trust and health
Days since push
- DeepSpeed
- 0d
- raft
- 1d
Open issues (now)
- DeepSpeed
- 1.3k
- raft
- 446
Stars delta
- DeepSpeed
- Unknown
- raft
- +5 (30d)
Open issues delta
- DeepSpeed
- Unknown
- raft
- +2 (30d)
Full report
- DeepSpeed
- Trust report
- raft
- Trust report
Choose DeepSpeed if…
- DeepSpeed is primarily Python; raft is Cuda.
- Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning.
- Also covers Inference & Serving.
- - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters)
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 raft if…
- raft is primarily Cuda; DeepSpeed is Python.
- Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT..
- Tags unique to raft: anns, building-blocks, clustering, cuda.
- Also covers Data & Retrieval.
- - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.
When NOT to use raft
- - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains.
- - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.
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 Aug 7, 2026
- GitHub forks (deepspeedai/DeepSpeed) · observed Aug 7, 2026
- Last push (deepspeedai/DeepSpeed) · observed Aug 6, 2026
- License file (Apache-2.0) · observed Aug 7, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (NVIDIA/raft) · observed Aug 23, 2026
- GitHub forks (NVIDIA/raft) · observed Aug 23, 2026
- Last push (NVIDIA/raft) · observed Aug 22, 2026
- License file (Apache-2.0) · observed Aug 23, 2026
- Decision facts (enrichment) · observed Jul 12, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepSpeed 43k · raft 1.0k (synced Aug 7, 2026).
Common questions
- What is the difference between DeepSpeed and raft?
- DeepSpeed: Deep learning optimization library for efficient distributed training and inference. raft: A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications.. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepSpeed over raft?
- Choose DeepSpeed over raft when DeepSpeed is primarily Python; raft is Cuda; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, deep-learning; Also covers Inference & Serving; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters).
- When should I choose raft over DeepSpeed?
- Choose raft over DeepSpeed when raft is primarily Cuda; DeepSpeed is Python; Requirements: Ensure access to NVIDIA GPUs; Compatibility with CUDA is essential for utilizing the RAFT algorithms effectively.; The user must have familiarity or develop understanding of CUDA programming to optimize their application integration with RAFT.; Tags unique to raft: anns, building-blocks, clustering, cuda; Also covers Data & Retrieval; - You are developing on an NVIDIA GPU architecture and require optimized, CUDA-accelerated primitives.
- 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 raft?
- - Your application does not have access to NVIDIA GPUs, as RAFT's algorithms leverage CUDA specifically for performance gains. - If your workload requires more generalized machine learning libraries without a dependency on GPU-accelerated primitives and you are working in a multi-platform or cross-vendor environment.
- Is DeepSpeed or raft more popular on GitHub?
- DeepSpeed has more GitHub stars (42,870 vs 1,036). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSpeed and raft open source?
- Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, raft: Apache-2.0).
- Where can I find alternatives to DeepSpeed or raft?
- GraphCanon lists graph-backed alternatives at DeepSpeed alternatives and raft alternatives (DeepSpeed markdown twin, raft 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 raft?
- DeepSpeed: Very active. raft: 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 DeepSpeed and raft?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSpeed trust report; raft trust report.