--- title: "DeepLearningExamples vs Megatron-LM" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/nvidia-deeplearningexamples-vs-nvidia-megatron-lm" tools: ["nvidia-deeplearningexamples", "nvidia-megatron-lm"] --- # DeepLearningExamples vs Megatron-LM *GraphCanon updated Aug 17, 2026* ## Verdict Pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings; pick Megatron-LM if megatron-LM from NVIDIA is a research-focused tool for developing and training large-scale language models with transformer architectures, emphasizing efficient parallelism across multiple GPUs. [DeepLearningExamples](https://github.com/NVIDIA/DeepLearningExamples) reports 15k GitHub stars, 3.4k forks, and 321 open issues, last pushed Aug 12, 2024. [Megatron-LM](https://docs.nvidia.com/megatron-core/developer-guide/latest/get-started/quickstart.html) has 17k stars, 4.3k forks, and 1.1k open issues, last pushed Aug 6, 2026. Figures are from public GitHub metadata via [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples) and [Megatron-LM's repository](https://github.com/NVIDIA/Megatron-LM). | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [Megatron-LM](/tools/nvidia-megatron-lm.md) | | --- | --- | --- | | Tagline | State-of-the-Art Deep Learning scripts for various applications | Ongoing research training transformer models at scale | | Stars | 14,844 | 17,341 | | Forks | 3,408 | 4,333 | | Open issues | 321 | 1,112 | | Language | Jupyter Notebook | Python | | Adopt for | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | Megatron-LM from NVIDIA is a research-focused tool for developing and training large-scale language models with transformer architectures, emphasizing efficient parallelism across multiple GPUs. | | Persona | - | - | | Runtime | - | - | | License | - | Other | | Categories | Inference & Serving, Model Training | Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | [Megatron-LM](/tools/nvidia-megatron-lm.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Very active (96%) | | Days since push | 734d | 0d | | Open issues (now) | 321 | 1.1k | | Stars delta | +14 (30d) | +353 (30d) | | Open issues delta | -1 (30d) | +122 (30d) | | Full report | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | [trust report](/tools/nvidia-megatron-lm/trust.md) | **Typed relationship:** DeepLearningExamples _(integrates with)_ Megatron-LM NVIDIA DeepLearningExamples provides training scripts that could leverage Megatron-LM for large-scale transformer-based models, thereby integrating both resources efficiently for high-performance model training. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Decision facts: Megatron-LM - **Requirements:** Min 32 GB RAM; Requires NVIDIA GPUs for optimized performance. Non-GPU usage is not supported or recommended.; Installation from source can be resource-intensive and may require limiting parallel compilation jobs to avoid running out of memory. - **Adopt for:** Megatron-LM from NVIDIA is a research-focused tool for developing and training large-scale language models with transformer architectures, emphasizing efficient parallelism across multiple GPUs. ## Choose when ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; Megatron-LM is Python. - NVIDIA DeepLearningExamples provides training scripts that could leverage Megatron-LM for large-scale transformer-based models, thereby integrating both resources efficiently for high-performance model training. - 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. ### Choose Megatron-LM if… - Megatron-LM is primarily Python; DeepLearningExamples is Jupyter Notebook. - Requirements: Min 32 GB RAM; Requires NVIDIA GPUs for optimized performance. Non-GPU usage is not supported or recommended.; Installation from source can be resource-intensive and may require limiting parallel compilation jobs to avoid running out of memory.. - NVIDIA DeepLearningExamples provides training scripts that could leverage Megatron-LM for large-scale transformer-based models, thereby integrating both resources efficiently for high-performance model training. - Tags unique to Megatron-LM: model-para, transformers. - The tool is particularly beneficial when your project is GPU-centric and benefits from advanced parallelism techniques such as Tensor, Pipeline, Data, Expert, and Cluster Parallelisms (TP, PP, DP, EP, ## 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 ## When NOT to use Megatron-LM - Avoid Megatron-LM if your computational setup does not include NVIDIA GPUs as it leverages GPU-specific features and parallelisms that may not be available or efficient on non-NVIDIA hardware. - If you need portability across various hardware without depending on proprietary optimizations, other tools might better serve your needs. ## Common questions ### What is the difference between DeepLearningExamples and Megatron-LM? DeepLearningExamples: State-of-the-Art Deep Learning scripts for various applications. Megatron-LM: Ongoing research training transformer models at scale. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepLearningExamples over Megatron-LM? Choose DeepLearningExamples over Megatron-LM when DeepLearningExamples is primarily Jupyter Notebook; Megatron-LM is Python; NVIDIA DeepLearningExamples provides training scripts that could leverage Megatron-LM for large-scale transformer-based models, thereby integrating both resources efficiently for high-performance model training; 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 choose Megatron-LM over DeepLearningExamples? Choose Megatron-LM over DeepLearningExamples when Megatron-LM is primarily Python; DeepLearningExamples is Jupyter Notebook; Requirements: Min 32 GB RAM; Requires NVIDIA GPUs for optimized performance. Non-GPU usage is not supported or recommended.; Installation from source can be resource-intensive and may require limiting parallel compilation jobs to avoid running out of memory.; NVIDIA DeepLearningExamples provides training scripts that could leverage Megatron-LM for large-scale transformer-based models, thereby integrating both resources efficiently for high-performance model training; Tags unique to Megatron-LM: model-para, transformers; The tool is particularly beneficial when your project is GPU-centric and benefits from advanced parallelism techniques such as Tensor, Pipeline, Data, Expert, and Cluster Parallelisms (TP, PP, DP, EP,. ### 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 ### When should I avoid Megatron-LM? Avoid Megatron-LM if your computational setup does not include NVIDIA GPUs as it leverages GPU-specific features and parallelisms that may not be available or efficient on non-NVIDIA hardware. If you need portability across various hardware without depending on proprietary optimizations, other tools might better serve your needs. ### Is DeepLearningExamples or Megatron-LM more popular on GitHub? Megatron-LM has more GitHub stars (17,341 vs 14,844). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepLearningExamples and Megatron-LM open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to DeepLearningExamples or Megatron-LM? GraphCanon lists graph-backed alternatives at [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) and [Megatron-LM alternatives](/tools/nvidia-megatron-lm/alternatives) ([DeepLearningExamples markdown twin](/tools/nvidia-deeplearningexamples/alternatives.md), [Megatron-LM markdown twin](/tools/nvidia-megatron-lm/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/nvidia-deeplearningexamples-vs-nvidia-megatron-lm.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepLearningExamples or Megatron-LM? DeepLearningExamples: Dormant. Megatron-LM: 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 DeepLearningExamples and Megatron-LM? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust); [Megatron-LM trust report](/tools/nvidia-megatron-lm/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=nvidia-deeplearningexamples`](/api/graphcanon/graph?tool=nvidia-deeplearningexamples) - 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/_