--- title: "DeepSpeed vs mindspore" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepspeedai-deepspeed-vs-mindspore-ai-mindspore" tools: ["deepspeedai-deepspeed", "mindspore-ai-mindspore"] --- # DeepSpeed vs mindspore *GraphCanon updated Jul 12, 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 mindspore if mindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. [DeepSpeed](https://www.deepspeed.ai/) reports 43k GitHub stars, 4.9k forks, and 1.3k open issues, last pushed Jul 11, 2026. [mindspore](https://gitee.com/mindspore/mindspore) has 4.7k stars, 752 forks, and 225 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [DeepSpeed's repository](https://github.com/deepspeedai/DeepSpeed) and [mindspore's repository](https://github.com/mindspore-ai/mindspore). | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [mindspore](/tools/mindspore-ai-mindspore.md) | | --- | --- | --- | | Tagline | Deep learning optimization library for efficient distributed training and inference | An open-source deep learning framework for mobile, edge and cloud scenarios. | | Stars | 42,685 | 4,694 | | Forks | 4,883 | 752 | | Open issues | 1,302 | 225 | | 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. | MindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Inference & Serving, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSpeed](/tools/deepspeedai-deepspeed.md) | [mindspore](/tools/mindspore-ai-mindspore.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 0d | 712d | | Open issues (now) | 1.3k | 225 | | Security scan | No lockfile | 103 low (103 low) | | Full report | [trust report](/tools/deepspeedai-deepspeed/trust.md) | [trust report](/tools/mindspore-ai-mindspore/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: mindspore - **Adopt for:** MindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. ## Choose when ### Choose DeepSpeed if… - DeepSpeed is primarily Python; mindspore is C++. - Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu. - - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters) ### Choose mindspore if… - mindspore is primarily C++; DeepSpeed is Python. - Tags unique to mindspore: ascend910, cpu support, gpu support, inference framework. - When working with Huawei's Ascend hardware like Ascend910 ## 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 mindspore - Avoid if only NVIDIA GPUs without CUDA 10.1 support are available - Not ideal for users requiring non-LINUX (excluding Windows) environments beyond specified Ubuntu/CentOS/x86 versions - If development primarily targets hardware not covered by MindSpore's Ascend, CUDA, or CPU setups ## Common questions ### What is the difference between DeepSpeed and mindspore? DeepSpeed: Deep learning optimization library for efficient distributed training and inference. mindspore: An open-source deep learning framework for mobile, edge and cloud scenarios.. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSpeed over mindspore? Choose DeepSpeed over mindspore when DeepSpeed is primarily Python; mindspore is C++; Tags unique to DeepSpeed: billion-parameters, compression, data-parallelism, gpu; - When training or inferring with PyTorch on large datasets or complex deep learning models (up to trillion parameters). ### When should I choose mindspore over DeepSpeed? Choose mindspore over DeepSpeed when mindspore is primarily C++; DeepSpeed is Python; Tags unique to mindspore: ascend910, cpu support, gpu support, inference framework; When working with Huawei's Ascend hardware like Ascend910. ### 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 mindspore? Avoid if only NVIDIA GPUs without CUDA 10.1 support are available Not ideal for users requiring non-LINUX (excluding Windows) environments beyond specified Ubuntu/CentOS/x86 versions If development primarily targets hardware not covered by MindSpore's Ascend, CUDA, or CPU setups ### Is DeepSpeed or mindspore more popular on GitHub? DeepSpeed has more GitHub stars (42,685 vs 4,694). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSpeed and mindspore open source? Yes - both are open-source projects on GitHub (DeepSpeed: Apache-2.0, mindspore: Apache-2.0). ### Where can I find alternatives to DeepSpeed or mindspore? GraphCanon lists graph-backed alternatives at [DeepSpeed alternatives](/tools/deepspeedai-deepspeed/alternatives) and [mindspore alternatives](/tools/mindspore-ai-mindspore/alternatives) ([DeepSpeed markdown twin](/tools/deepspeedai-deepspeed/alternatives.md), [mindspore markdown twin](/tools/mindspore-ai-mindspore/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-mindspore-ai-mindspore.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSpeed or mindspore? DeepSpeed: Very active. mindspore: 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 mindspore? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSpeed trust report](/tools/deepspeedai-deepspeed/trust); [mindspore trust report](/tools/mindspore-ai-mindspore/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/_