--- title: "litgpt vs DeepLearningExamples" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/lightning-ai-litgpt-vs-nvidia-deeplearningexamples" tools: ["lightning-ai-litgpt", "nvidia-deeplearningexamples"] --- # litgpt vs DeepLearningExamples *GraphCanon updated Aug 17, 2026* ## Verdict Pick litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment; pick DeepLearningExamples if curated facts for DeepLearningExamples, tailored to its unique features and offerings. [litgpt](https://lightning.ai) reports 14k GitHub stars, 1.5k forks, and 272 open issues, last pushed Jul 20, 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 [litgpt's repository](https://github.com/Lightning-AI/litgpt) and [DeepLearningExamples's repository](https://github.com/NVIDIA/DeepLearningExamples). | | [litgpt](/tools/lightning-ai-litgpt.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Tagline | High-performance LLMs with recipes for pretraining, finetuning and deployment | State-of-the-Art Deep Learning scripts for various applications | | Stars | 13,605 | 14,844 | | Forks | 1,483 | 3,408 | | Open issues | 272 | 321 | | Language | Python | Jupyter Notebook | | Adopt for | LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. | Curated facts for DeepLearningExamples, tailored to its unique features and offerings. | | Persona | - | - | | Runtime | - | - | | License | LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. | - | | Categories | Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [litgpt](/tools/lightning-ai-litgpt.md) | [DeepLearningExamples](/tools/nvidia-deeplearningexamples.md) | | --- | --- | --- | | Maintenance | Active (82%) | Dormant (18%) | | Days since push | 17d | 734d | | Open issues (now) | 272 | 321 | | Stars delta | +137 (30d) | +14 (30d) | | Open issues delta | +6 (30d) | -1 (30d) | | Full report | [trust report](/tools/lightning-ai-litgpt/trust.md) | [trust report](/tools/nvidia-deeplearningexamples/trust.md) | ## Decision facts: litgpt - **Pricing:** freemium - The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models. - **Requirements:** Min 16 GB RAM - **Adopt for:** LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. - **License detail:** LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. ## Decision facts: DeepLearningExamples - **Adopt for:** Curated facts for DeepLearningExamples, tailored to its unique features and offerings. ## Choose when ### Choose litgpt if… - litgpt is primarily Python; DeepLearningExamples is Jupyter Notebook. - Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.. - Requirements: Min 16 GB RAM. - Tags unique to litgpt: ai, artificial-intelligence, llm-inference, llms. - Also covers LLM Frameworks. - If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application. ### Choose DeepLearningExamples if… - DeepLearningExamples is primarily Jupyter Notebook; litgpt is Python. - Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, mxnet. - 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 litgpt - If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. - When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable. ## 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 litgpt and DeepLearningExamples? litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. 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 litgpt over DeepLearningExamples? Choose litgpt over DeepLearningExamples when litgpt is primarily Python; DeepLearningExamples is Jupyter Notebook; Pricing: The core LitGPT framework is free to use under an open source license, but users might encounter costs when deploying at scale or using high-performance models.; Requirements: Min 16 GB RAM; Tags unique to litgpt: ai, artificial-intelligence, llm-inference, llms; Also covers LLM Frameworks; If you are focusing on a project that requires rapid prototyping or experimentation with over 20 different LLMs to find the best fit for your application. ### When should I choose DeepLearningExamples over litgpt? Choose DeepLearningExamples over litgpt when DeepLearningExamples is primarily Jupyter Notebook; litgpt is Python; Tags unique to DeepLearningExamples: computer-vision, drug-discovery, forecasting, mxnet; 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 litgpt? If you need a tool specifically optimized for resource-constrained devices, as LitGPT focuses on high-performance LLMs and may require more resources. When your project is strictly limited to only one or two types of specific LLMs; in this case, another specialized framework that caters narrowly might be preferable. ### 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 litgpt or DeepLearningExamples more popular on GitHub? DeepLearningExamples has more GitHub stars (14,844 vs 13,605). Stars measure visibility, not whether either tool fits your constraints. ### Are litgpt and DeepLearningExamples open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to litgpt or DeepLearningExamples? GraphCanon lists graph-backed alternatives at [litgpt alternatives](/tools/lightning-ai-litgpt/alternatives) and [DeepLearningExamples alternatives](/tools/nvidia-deeplearningexamples/alternatives) ([litgpt markdown twin](/tools/lightning-ai-litgpt/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/lightning-ai-litgpt-vs-nvidia-deeplearningexamples.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, litgpt or DeepLearningExamples? litgpt: Active. 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 litgpt and DeepLearningExamples? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [litgpt trust report](/tools/lightning-ai-litgpt/trust); [DeepLearningExamples trust report](/tools/nvidia-deeplearningexamples/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=lightning-ai-litgpt`](/api/graphcanon/graph?tool=lightning-ai-litgpt) - 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/_