--- title: "AI-Infra-from-Zero-to-Hero vs open-r1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huaizhengzhang-ai-infra-from-zero-to-hero-vs-huggingface-open-r1" tools: ["huaizhengzhang-ai-infra-from-zero-to-hero", "huggingface-open-r1"] --- # AI-Infra-from-Zero-to-Hero vs open-r1 *GraphCanon updated Jul 12, 2026* ## Verdict Pick AI-Infra-from-Zero-to-Hero if aI-Infra-from-Zero-to-Hero is an extensive repository that curates a wide range of resources related to AI infrastructure, including tutorials and research papers in the areas of machine learning and large language model; 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. [AI-Infra-from-Zero-to-Hero](https://huaizheng.xyz/) reports 4.2k GitHub stars, 402 forks, and 14 open issues, last pushed Jul 25, 2025. [open-r1](https://github.com/huggingface/open-r1) has 26k stars, 2.4k forks, and 340 open issues, last pushed Apr 2, 2026. Figures are from public GitHub metadata via [AI-Infra-from-Zero-to-Hero's repository](https://github.com/HuaizhengZhang/AI-Infra-from-Zero-to-Hero) and [open-r1's repository](https://github.com/huggingface/open-r1). | | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | [open-r1](/tools/huggingface-open-r1.md) | | --- | --- | --- | | Tagline | 🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. ⚡️ System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI). 🍻 OSDI, NSDI, SIGCOMM, SoCC, MLSys | Fully open reproduction of DeepSeek-R1 | | Stars | 4,176 | 26,401 | | Forks | 402 | 2,446 | | Open issues | 14 | 340 | | Language | - | Python | | Adopt for | AI-Infra-from-Zero-to-Hero is an extensive repository that curates a wide range of resources related to AI infrastructure, including tutorials and research papers in the areas of machine learning and large language model | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | The project is licensed under Apache-2.0, providing a permissive license that allows for free use, modification, and distribution. | | Categories | LLM Frameworks, Model Training, Inference & Serving | Model Training, Inference & Serving | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | [open-r1](/tools/huggingface-open-r1.md) | | --- | --- | --- | | Days since push | 351d | 100d | | Open issues (now) | 14 | 340 | | Owner type | User | Organization | | Full report | [trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/trust.md) | [trust report](/tools/huggingface-open-r1/trust.md) | ## Decision facts: AI-Infra-from-Zero-to-Hero - **Adopt for:** AI-Infra-from-Zero-to-Hero is an extensive repository that curates a wide range of resources related to AI infrastructure, including tutorials and research papers in the areas of machine learning and large language model ## 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. ## Choose when ### Choose AI-Infra-from-Zero-to-Hero if… - License: AI-Infra-from-Zero-to-Hero is MIT, open-r1 is Apache-2.0. - Tags unique to AI-Infra-from-Zero-to-Hero: llmsys, model-training, mlsys, genai. - Also covers LLM Frameworks. - When you require detailed resource curation on ML systems and LLM infrastructures, as AI-Infra-from-Zero-to-Hero offers comprehensive information. ### Choose open-r1 if… - License: open-r1 is Apache-2.0, AI-Infra-from-Zero-to-Hero is MIT. - 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: deepseek-r1, rl pipeline, vllm, python. - Use Open-R1 when you need a detailed understanding of how DeepSeek-R1 operates, considering the project closely mirrors its architecture and processes. ## When NOT to use AI-Infra-from-Zero-to-Hero - If you seek real-time support or interactive forums, as AI-Infra-from-Zero-to-Hero is primarily a resource repository without live assistance. - For hands-on coding exercises or practical projects as the tool focuses mostly on curating resources like tutorials and academic papers but does not provide step-by-step coding guides. ## 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. ## Common questions ### What is the difference between AI-Infra-from-Zero-to-Hero and open-r1? AI-Infra-from-Zero-to-Hero: 🚀 Awesome System for Machine Learning ⚡️ AI System Papers and Industry Practice. ⚡️ System for Machine Learning, LLM (Large Language Model), GenAI (Generative AI). 🍻 OSDI, NSDI, SIGCOMM, SoCC, MLSys. open-r1: Fully open reproduction of DeepSeek-R1. See the comparison table for live GitHub stats and shared categories. ### When should I choose AI-Infra-from-Zero-to-Hero over open-r1? Choose AI-Infra-from-Zero-to-Hero over open-r1 when License: AI-Infra-from-Zero-to-Hero is MIT, open-r1 is Apache-2.0; Tags unique to AI-Infra-from-Zero-to-Hero: llmsys, model-training, mlsys, genai; Also covers LLM Frameworks; When you require detailed resource curation on ML systems and LLM infrastructures, as AI-Infra-from-Zero-to-Hero offers comprehensive information. ### When should I choose open-r1 over AI-Infra-from-Zero-to-Hero? Choose open-r1 over AI-Infra-from-Zero-to-Hero when License: open-r1 is Apache-2.0, AI-Infra-from-Zero-to-Hero is MIT; 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: deepseek-r1, rl pipeline, vllm, python; 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 avoid AI-Infra-from-Zero-to-Hero? If you seek real-time support or interactive forums, as AI-Infra-from-Zero-to-Hero is primarily a resource repository without live assistance. For hands-on coding exercises or practical projects as the tool focuses mostly on curating resources like tutorials and academic papers but does not provide step-by-step coding guides. ### 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. ### Is AI-Infra-from-Zero-to-Hero or open-r1 more popular on GitHub? open-r1 has more GitHub stars (26,401 vs 4,176). Stars measure visibility, not whether either tool fits your constraints. ### Are AI-Infra-from-Zero-to-Hero and open-r1 open source? Yes - both are open-source projects on GitHub (AI-Infra-from-Zero-to-Hero: MIT, open-r1: Apache-2.0). ### Where can I find alternatives to AI-Infra-from-Zero-to-Hero or open-r1? GraphCanon lists graph-backed alternatives at [AI-Infra-from-Zero-to-Hero alternatives](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/alternatives) and [open-r1 alternatives](/tools/huggingface-open-r1/alternatives) ([AI-Infra-from-Zero-to-Hero markdown twin](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/alternatives.md), [open-r1 markdown twin](/tools/huggingface-open-r1/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/huaizhengzhang-ai-infra-from-zero-to-hero-vs-huggingface-open-r1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, AI-Infra-from-Zero-to-Hero or open-r1? AI-Infra-from-Zero-to-Hero: Slowing. open-r1: Slowing. 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 AI-Infra-from-Zero-to-Hero and open-r1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [AI-Infra-from-Zero-to-Hero trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/trust); [open-r1 trust report](/tools/huggingface-open-r1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huaizhengzhang-ai-infra-from-zero-to-hero`](/api/graphcanon/graph?tool=huaizhengzhang-ai-infra-from-zero-to-hero) - 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/_