--- title: "nanotron vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-nanotron-vs-nvidia-deeplearningexamples" tools: ["huggingface-nanotron", "nvidia-deeplearningexamples"] --- # nanotron vs DeepLearningExamples *GraphCanon updated Aug 17, 2026* ## Verdict Pick nanotron if nanotron specializes in minimalistic large language model 3D-parallelism training via efficient distributed computing techniques; pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings. [nanotron](https://github.com/huggingface/nanotron) reports 2.8k GitHub stars, 329 forks, and 149 open issues, last pushed May 26, 2026. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) has 15k stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. Figures are from public GitHub metadata via [nanotron's repository](https://github.com/huggingface/nanotron) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [nanotron](/tools/huggingface-nanotron.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | Minimalistic large language model 3D-parallelism training | State-of-the-Art Deep Learning scripts for various applications | | Stars | 2,775 | 14,844 | | Forks | 329 | 3,408 | | Open issues | 149 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | Nanotron specializes in minimalistic large language model 3D-parallelism training via efficient distributed computing techniques. | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | - | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [nanotron](/tools/huggingface-nanotron.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Dormant (18%) | | Days since push | 72d | 734d | | Open issues (now) | 149 | 321 | | Stars delta | Unknown | +14 (30d) | | Open issues delta | Unknown | -1 (30d) | | Full report | [trust report](/tools/huggingface-nanotron/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: nanotron - **Adopt for:** Nanotron specializes in minimalistic large language model 3D-parallelism training via efficient distributed computing techniques. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Choose when ### Choose nanotron if… - nanotron is primarily Python; DeepLearningExamples is Jupyter Notebook. - Tags unique to nanotron: 3d_parallelism, distributed-training, llm, pytorch. - You aim to implement 3D-parallelism for large language models with minimal code complexity and high efficiency. ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; nanotron is Python. - Tags unique to DeepLearningExamples: computer-vision, deep-learning, drug-discovery, forecasting. - Also covers Inference & Serving. - The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ## When NOT to use nanotron - You require robust integration capabilities that come with larger, more feature-rich training frameworks. - Need extensive out-of-the-box solutions for common data processing tasks as Nanotron focuses narrowly on parallelism and efficient computing, potentially missing broader functionalities. ## When NOT to use DeepLearningExamples - Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores. - If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n ## Common questions ### What is the difference between nanotron and DeepLearningExamples? nanotron: Minimalistic large language model 3D-parallelism training. DeepLearningExamples: State-of-the-Art Deep Learning scripts for various applications. See the comparison table for live GitHub stats and shared categories. ### When should I choose nanotron over DeepLearningExamples? Choose nanotron over DeepLearningExamples when nanotron is primarily Python; DeepLearningExamples is Jupyter Notebook; Tags unique to nanotron: 3d_parallelism, distributed-training, llm, pytorch; You aim to implement 3D-parallelism for large language models with minimal code complexity and high efficiency. ### When should I choose DeepLearningExamples over nanotron? Choose DeepLearningExamples over nanotron when DeepLearningExamples is primarily Jupyter Notebook; nanotron is Python; Tags unique to DeepLearningExamples: computer-vision, deep-learning, drug-discovery, forecasting; Also covers Inference & Serving; The NVIDIA GPU Cloud (NGC) Container Registry that integrates with this tool offers the latest updates every month along with rigorous quality assurance. ### When should I avoid nanotron? You require robust integration capabilities that come with larger, more feature-rich training frameworks. Need extensive out-of-the-box solutions for common data processing tasks as Nanotron focuses narrowly on parallelism and efficient computing, potentially missing broader functionalities. ### When should I avoid DeepLearningExamples? Avoid using DeepLearningExamples if you do not have access to NVIDIA GPUs, as it is heavily optimized for these specific hardware configurations to provide maximum utilization of Tensor Cores. If your project requires frameworks that are less common (e.g., MXNet or PaddlePaddle) without the same level of support as PyTorch and TensorFlow on this platform, consider other repositories that n ### Is nanotron or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,844 vs 2,775). Stars measure visibility, not whether either tool fits your constraints. ### Are nanotron and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to nanotron or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [nanotron alternatives](/tools/huggingface-nanotron/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([nanotron markdown twin](/tools/huggingface-nanotron/alternatives.md), [DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/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/huggingface-nanotron-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, nanotron or DeepLearningExamples? nanotron: Steady. DeepLearningExamples: 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 nanotron and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [nanotron trust report](/tools/huggingface-nanotron/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-nanotron`](/api/graphcanon/graph?tool=huggingface-nanotron) - 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/_