--- title: "DeepSpeed vs oneflow" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-oneflow-inc-oneflow" tools: ["deepspeedai-deepspeed", "oneflow-inc-oneflow"] --- # DeepSpeed vs oneflow *GraphCanon updated Aug 7, 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 oneflow if oneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Aug 6, 2026. [oneflow](http://www.oneflow.org) has 9.4k stars, 1.0k forks, and 644 open issues, last pushed Dec 4, 2025. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [oneflow's repository](https://github.com/Oneflow-Inc/oneflow). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [oneflow](/tools/oneflow-inc-oneflow.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient. | | Stars | 42,870 | 9,420 | | Forks | 4,920 | 1,013 | | Open issues | 1,308 | 644 | | Language | Python | C++ | | 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. | OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [oneflow](/tools/oneflow-inc-oneflow.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Slowing (36%) | | Days since push | 0d | 242d | | Open issues (now) | 1.3k | 644 | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/oneflow-inc-oneflow/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: oneflow - **Adopt for:** OneFlow is a deep learning framework built for user-friendly, scalable, and efficient performance in model training, with support via CUDA installations. ## Choose when ### Choose DeepSpeed if… - DeepSpeed is primarily Python; oneflow is C++. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu. - Also covers Inference & Serving. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose oneflow if… - oneflow is primarily C++; DeepSpeed is Python. - Tags unique to oneflow: cuda, distributed, neural-networks. - OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow. ## 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 oneflow - Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility. - If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely. - OneFlow may not be ideal when working in regions with difficulty accessing external libraries due to dependency management tailored towards certain geographic locations. ## Common questions ### What is the difference between DeepSpeed and oneflow? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. oneflow: OneFlow is a deep learning framework designed to be user-friendly, scalable and efficient.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSpeed over oneflow? Choose DeepSpeed over oneflow when DeepSpeed is primarily Python; oneflow is C++; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; 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 oneflow over DeepSpeed? Choose oneflow over DeepSpeed when oneflow is primarily C++; DeepSpeed is Python; Tags unique to oneflow: cuda, distributed, neural-networks; OneFlow is preferable when you need a user-friendly framework for both CPU and CUDA installations, aiming to streamline the deep learning workflow. ### 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 oneflow? Avoid OneFlow if your project requires extensive customization features not natively supported, as switching to another framework might offer better flexibility. If the development environment lacks support for CUDA or Python3-based installation methods, consider an alternative framework that suits your hardware and software environment more closely. OneFlow may not be ideal when working in regions with difficulty accessing external libraries due to dependency management tailored towards certain geographic locations. ### Is DeepSpeed or oneflow more popular on GitHub? DeepSpeed has more GitHub stars (42,870 vs 9,420). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and oneflow open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, oneflow: Apache-2.0). ### Where can I find alternatives to DeepSpeed or oneflow? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [oneflow alternatives](/tools/oneflow-inc-oneflow/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [oneflow markdown twin](/tools/oneflow-inc-oneflow/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-oneflow-inc-oneflow.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or oneflow? DeepSpeed: Very active. oneflow: Slowing. 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 oneflow? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [oneflow trust report](/tools/oneflow-inc-oneflow/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/_