--- title: "DeepSpeed vs raft" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-nvidia-raft" tools: ["deepspeedai-deepspeed", "nvidia-raft"] --- # DeepSpeed vs raft *GraphCanon updated Aug 23, 2026* ## 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. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Aug 6, 2026. [raft](https://docs.rapids.ai/api/raft/stable/) has 1.0k stars, 248 forks, and 446 open issues, last pushed Aug 22, 2026. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [raft's repository](https://github.com/NVIDIA/raft). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | A collection of CUDA-accelerated algorithms for building high-performance machine learning and information retrieval applications. | | Stars | 42,870 | 1,036 | | Forks | 4,920 | 248 | | Open issues | 1,308 | 446 | | Language | Python | Cuda | | Adopt for | 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 is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Data & Retrieval, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [raft](/tools/nvidia-raft.md) | | --- | --- | --- | | Days since push | 0d | 1d | | Open issues (now) | 1.3k | 446 | | Stars delta | Unknown | +5 (30d) | | Open issues delta | Unknown | +2 (30d) | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/nvidia-raft/trust.md) | ## Decision facts: DeepSpeed - **Adopt for:** 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. ## Decision facts: raft - **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. - **Adopt for:** RAFT is a collection of CUDA-accelerated algorithms for high-performance machine learning and information retrieval applications. ## Choose when ### 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) ### 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 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 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. ## 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](/tools/deepspeedai-deepspeed/alternatives) and [raft alternatives](/tools/nvidia-raft/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [raft markdown twin](/tools/nvidia-raft/alternatives.md)), 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](/compare/deepspeedai-deepspeed-vs-nvidia-raft.md) 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](/tools/deepspeedai-deepspeed/trust); [raft trust report](/tools/nvidia-raft/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepspeedai-deepspeed`](/api/graphcanon/graph?tool=deepspeedai-deepspeed) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_