--- title: "open-r1 vs litgpt" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huggingface-open-r1-vs-lightning-ai-litgpt" tools: ["huggingface-open-r1", "lightning-ai-litgpt"] --- # open-r1 vs litgpt *GraphCanon updated Jul 12, 2026* ## Verdict Pick open-r1 if open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training; pick litgpt if litGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. [open-r1](https://github.com/huggingface/open-r1) reports 26k GitHub stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 2026. [litgpt](https://lightning.ai) has 13k stars, 1.5k forks, and 267 open issues, last pushed Jul 6, 2026. Figures are from public GitHub metadata via [open-r1's repository](https://github.com/huggingface/open-r1) and [litgpt's repository](https://github.com/Lightning-AI/litgpt). | | [open-r1](/tools/huggingface-open-r1.md) | [litgpt](/tools/lightning-ai-litgpt.md) | | --- | --- | --- | | Tagline | Fully open reproduction of DeepSeek-R1 | High-performance LLMs with recipes for pretraining, finetuning and deployment | | Stars | 26,401 | 13,473 | | Forks | 2,446 | 1,468 | | Open issues | 340 | 267 | | Language | Python | Python | | Adopt for | Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. | LitGPT offers extensive support for high-performance LLMs with comprehensive workflows for pretraining, fine-tuning, and deployment. | | Persona | - | - | | Runtime | - | - | | License | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | LitGPT operates under the open-source Apache-2.0 license, providing permissive terms for use and modification. | | Categories | Inference & Serving, Model Training | Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [open-r1](/tools/huggingface-open-r1.md) | [litgpt](/tools/lightning-ai-litgpt.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 100d | 4d | | Open issues (now) | 340 | 267 | | Full report | [trust report](/tools/huggingface-open-r1/trust.md) | [trust report](/tools/lightning-ai-litgpt/trust.md) | ## Shared compatibility - **Python**: [open-r1](/tools/huggingface-open-r1.md) - Python runtime; [litgpt](/tools/lightning-ai-litgpt.md) - Python runtime ## Decision facts: open-r1 - **Requirements:** Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical. - **Adopt for:** Open-R1 is an open-source effort to replicate DeepSeek-R1's models and training pipelines involving model distillation, RL pipeline replication, and multi-stage training. - **License detail:** The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. ## 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. ## Choose when ### Choose open-r1 if… - Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.. - Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation. - Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### Choose litgpt if… - 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, deep-learning, large-language-models. - 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 NOT to use open-r1 - Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. - Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ## 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. ## Common questions ### What is the difference between open-r1 and litgpt? open-r1: Fully open reproduction of DeepSeek-R1. litgpt: High-performance LLMs with recipes for pretraining, finetuning and deployment. See the comparison table for live GitHub stats and shared categories. ### When should I choose open-r1 over litgpt? Choose open-r1 over litgpt when Requirements: Min 8 GB RAM; Installation requires CUDA version 12.4 and PyTorch v2.6.0, with specific dependencies like vLLM and FlashAttention that are critical.; Tags unique to open-r1: cuda, deepseek-r1, flashattention, model distillation; Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ### When should I choose litgpt over open-r1? Choose litgpt over open-r1 when 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, deep-learning, large-language-models; 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 avoid open-r1? Avoid Open-R1 if your hardware does not support CUDA 12.4 or cannot run PyTorch `v2.6.0`, as this may lead to errors. Do not use it if the need for rapid experimentation outweighs the value of detailed replication, since the multi-stage training and datasets curation process can be time-consuming. ### 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. ### Is open-r1 or litgpt more popular on GitHub? open-r1 has more GitHub stars (26,401 vs 13,473). Stars measure visibility, not whether either tool fits your constraints. ### Are open-r1 and litgpt open source? Yes - both are open-source projects on GitHub (open-r1: Apache-2.0, litgpt: Apache-2.0). ### Where can I find alternatives to open-r1 or litgpt? GraphCanon lists graph-backed alternatives at [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) and [litgpt alternatives](/tools/lightning-ai-litgpt/alternatives) ([open-r1 markdown twin](/tools/huggingface-open-r1/alternatives.md), [litgpt markdown twin](/tools/lightning-ai-litgpt/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-open-r1-vs-lightning-ai-litgpt.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, open-r1 or litgpt? open-r1: Slowing. litgpt: 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 open-r1 and litgpt? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [open-r1 trust report](/tools/huggingface-open-r1/trust); [litgpt trust report](/tools/lightning-ai-litgpt/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huggingface-open-r1`](/api/graphcanon/graph?tool=huggingface-open-r1) - 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/_