--- title: "AI-Infra-from-Zero-to-Hero vs Liger-Kernel" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huaizhengzhang-ai-infra-from-zero-to-hero-vs-linkedin-liger-kernel" tools: ["huaizhengzhang-ai-infra-from-zero-to-hero", "linkedin-liger-kernel"] --- # AI-Infra-from-Zero-to-Hero vs Liger-Kernel *GraphCanon updated Jul 12, 2026* ## Verdict Pick AI-Infra-from-Zero-to-Hero when license: AI-Infra-from-Zero-to-Hero is MIT, Liger-Kernel is BSD-2-Clause; pick Liger-Kernel when license: Liger-Kernel is BSD-2-Clause, AI-Infra-from-Zero-to-Hero is MIT. [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. [Liger-Kernel](https://linkedin.github.io/Liger-Kernel/) has 6.5k stars, 554 forks, and 161 open issues, last pushed Jul 6, 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 [Liger-Kernel's repository](https://github.com/linkedin/Liger-Kernel). | | [AI-Infra-from-Zero-to-Hero](/tools/huaizhengzhang-ai-infra-from-zero-to-hero.md) | [Liger-Kernel](/tools/linkedin-liger-kernel.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 | Efficient Triton Kernels for LLM Training | | Stars | 4,176 | 6,494 | | Forks | 402 | 554 | | Open issues | 14 | 161 | | 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 | - | | Persona | - | - | | Runtime | - | - | | License | MIT | BSD-2-Clause | | Categories | LLM Frameworks, Model Training, Inference & Serving | LLM Frameworks, Model Training | ## 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) | [Liger-Kernel](/tools/linkedin-liger-kernel.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Very active (96%) | | Days since push | 351d | 4d | | Open issues (now) | 14 | 161 | | Owner type | User | Organization | | Full report | [trust report](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/trust.md) | [trust report](/tools/linkedin-liger-kernel/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 ## Choose when ### Choose AI-Infra-from-Zero-to-Hero if… - License: AI-Infra-from-Zero-to-Hero is MIT, Liger-Kernel is BSD-2-Clause. - Tags unique to AI-Infra-from-Zero-to-Hero: llmsys, model-training, mlsys, genai. - Also covers Inference & Serving. - When you require detailed resource curation on ML systems and LLM infrastructures, as AI-Infra-from-Zero-to-Hero offers comprehensive information. ### Choose Liger-Kernel if… - License: Liger-Kernel is BSD-2-Clause, AI-Infra-from-Zero-to-Hero is MIT. - Tags unique to Liger-Kernel: llms, llama, mistral, gemma2. - More GitHub stars (6.5k vs 4.2k) - visibility, not fit. ## 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 Liger-Kernel - LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## Common questions ### What is the difference between AI-Infra-from-Zero-to-Hero and Liger-Kernel? 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. Liger-Kernel: Efficient Triton Kernels for LLM Training. See the comparison table for live GitHub stats and shared categories. ### When should I choose AI-Infra-from-Zero-to-Hero over Liger-Kernel? Choose AI-Infra-from-Zero-to-Hero over Liger-Kernel when License: AI-Infra-from-Zero-to-Hero is MIT, Liger-Kernel is BSD-2-Clause; Tags unique to AI-Infra-from-Zero-to-Hero: llmsys, model-training, mlsys, genai; Also covers Inference & Serving; 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 Liger-Kernel over AI-Infra-from-Zero-to-Hero? Choose Liger-Kernel over AI-Infra-from-Zero-to-Hero when License: Liger-Kernel is BSD-2-Clause, AI-Infra-from-Zero-to-Hero is MIT; Tags unique to Liger-Kernel: llms, llama, mistral, gemma2; More GitHub stars (6.5k vs 4.2k) - visibility, not fit. ### 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 Liger-Kernel? LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### Is AI-Infra-from-Zero-to-Hero or Liger-Kernel more popular on GitHub? Liger-Kernel has more GitHub stars (6,494 vs 4,176). Stars measure visibility, not whether either tool fits your constraints. ### Are AI-Infra-from-Zero-to-Hero and Liger-Kernel open source? Yes - both are open-source projects on GitHub (AI-Infra-from-Zero-to-Hero: MIT, Liger-Kernel: BSD-2-Clause). ### Where can I find alternatives to AI-Infra-from-Zero-to-Hero or Liger-Kernel? GraphCanon lists graph-backed alternatives at [AI-Infra-from-Zero-to-Hero alternatives](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/alternatives) and [Liger-Kernel alternatives](/tools/linkedin-liger-kernel/alternatives) ([AI-Infra-from-Zero-to-Hero markdown twin](/tools/huaizhengzhang-ai-infra-from-zero-to-hero/alternatives.md), [Liger-Kernel markdown twin](/tools/linkedin-liger-kernel/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-linkedin-liger-kernel.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 Liger-Kernel? AI-Infra-from-Zero-to-Hero: Slowing. Liger-Kernel: 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 AI-Infra-from-Zero-to-Hero and Liger-Kernel? 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); [Liger-Kernel trust report](/tools/linkedin-liger-kernel/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/_