--- title: "scaling-book vs awesome-LLM-resources" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jax-ml-scaling-book-vs-wangrongsheng-awesome-llm-resources" tools: ["jax-ml-scaling-book", "wangrongsheng-awesome-llm-resources"] --- # scaling-book vs awesome-LLM-resources *GraphCanon updated Aug 25, 2026* ## Verdict Pick scaling-book if scaling-book; pick awesome-LLM-resources if awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a. [scaling-book](https://jax-ml.github.io/scaling-book/) reports 1.4k GitHub stars, 191 forks, and 8 open issues, last pushed Aug 20, 2026. [awesome-LLM-resources](https://github.com/WangRongsheng/awesome-LLM-resources) has 8.8k stars, 950 forks, and 23 open issues, last pushed Aug 14, 2026. Figures are from public GitHub metadata via [scaling-book's repository](https://github.com/jax-ml/scaling-book) and [awesome-LLM-resources's repository](https://github.com/WangRongsheng/awesome-LLM-resources). | | [scaling-book](/tools/jax-ml-scaling-book.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Tagline | Guide on scaling LLMs on TPUs | Summary of the world's best LLM resources. | | Stars | 1,368 | 8,845 | | Forks | 191 | 950 | | Open issues | 8 | 23 | | Language | HTML | - | | Adopt for | scaling-book | awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 | | Categories | Inference & Serving, LLM Frameworks | AI Agents, Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [scaling-book](/tools/jax-ml-scaling-book.md) | [awesome-LLM-resources](/tools/wangrongsheng-awesome-llm-resources.md) | | --- | --- | --- | | Days since push | 4d | 2d | | Open issues (now) | 8 | 23 | | Stars delta | +77 (30d) | +142 (30d) | | Open issues delta | +1 (30d) | -13 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/jax-ml-scaling-book/trust.md) | [trust report](/tools/wangrongsheng-awesome-llm-resources/trust.md) | ## Decision facts: scaling-book - **Adopt for:** scaling-book ## Decision facts: awesome-LLM-resources - **Adopt for:** awesome-LLM-resources offers a curated and comprehensive list of resources related to Large Language Models (LLMs), including materials for specialized areas like RAG (Retrieval-Augmented Generation) and agentic RL, as a ## Choose when ### Choose scaling-book if… - License: scaling-book is MIT, awesome-LLM-resources is Apache-2.0. - Tags unique to scaling-book: jax, llm-inference, llms, roofline. - You are working specifically with machine learning models that leverage Tensor Processing Units (TPUs) for performance and are looking to understand optimization techniques. ### Choose awesome-LLM-resources if… - License: awesome-LLM-resources is Apache-2.0, scaling-book is MIT. - Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models. - Also covers AI Agents, Developer Tools, Evaluation & Observability, Model Training. - - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ## When NOT to use scaling-book - Do not use if your project focuses exclusively on GPU scaling or other hardware not aligned with Tensor Processing Units (TPUs). - If you are looking for a general approach to any framework's scalability without emphasis on TPUs. - This resource is unsuitable if you need information about model training phases, as it emphasizes inference and serving phases. ## When NOT to use awesome-LLM-resources - - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ## Common questions ### What is the difference between scaling-book and awesome-LLM-resources? scaling-book: Guide on scaling LLMs on TPUs. awesome-LLM-resources: Summary of the world's best LLM resources.. See the comparison table for live GitHub stats and shared categories. ### When should I choose scaling-book over awesome-LLM-resources? Choose scaling-book over awesome-LLM-resources when License: scaling-book is MIT, awesome-LLM-resources is Apache-2.0; Tags unique to scaling-book: jax, llm-inference, llms, roofline; You are working specifically with machine learning models that leverage Tensor Processing Units (TPUs) for performance and are looking to understand optimization techniques. ### When should I choose awesome-LLM-resources over scaling-book? Choose awesome-LLM-resources over scaling-book when License: awesome-LLM-resources is Apache-2.0, scaling-book is MIT; Tags unique to awesome-LLM-resources: awesome-list, book, course, large language models; Also covers AI Agents, Developer Tools, Evaluation & Observability, Model Training; - It's ideal when you seek an exhaustive and up-to-date compilation covering extensive knowledge points in LLM technologies. ### When should I avoid scaling-book? Do not use if your project focuses exclusively on GPU scaling or other hardware not aligned with Tensor Processing Units (TPUs). If you are looking for a general approach to any framework's scalability without emphasis on TPUs. This resource is unsuitable if you need information about model training phases, as it emphasizes inference and serving phases. ### When should I avoid awesome-LLM-resources? - Avoid using this resource if you specifically need detailed step-by-step guides or hands-on tutorials that focus deeply on a single technology rather than broad coverage. - It might not be the best choice when you are looking for resources in languages other than English, especially given its extensive English content. ### Is scaling-book or awesome-LLM-resources more popular on GitHub? awesome-LLM-resources has more GitHub stars (8,845 vs 1,368). Stars measure visibility, not whether either tool fits your constraints. ### Are scaling-book and awesome-LLM-resources open source? Yes - both are open-source projects on GitHub (scaling-book: MIT, awesome-LLM-resources: Apache-2.0). ### Where can I find alternatives to scaling-book or awesome-LLM-resources? GraphCanon lists graph-backed alternatives at [scaling-book alternatives](/tools/jax-ml-scaling-book/alternatives) and [awesome-LLM-resources alternatives](/tools/wangrongsheng-awesome-llm-resources/alternatives) ([scaling-book markdown twin](/tools/jax-ml-scaling-book/alternatives.md), [awesome-LLM-resources markdown twin](/tools/wangrongsheng-awesome-llm-resources/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/jax-ml-scaling-book-vs-wangrongsheng-awesome-llm-resources.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, scaling-book or awesome-LLM-resources? scaling-book: Very active. awesome-LLM-resources: 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 scaling-book and awesome-LLM-resources? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [scaling-book trust report](/tools/jax-ml-scaling-book/trust); [awesome-LLM-resources trust report](/tools/wangrongsheng-awesome-llm-resources/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jax-ml-scaling-book`](/api/graphcanon/graph?tool=jax-ml-scaling-book) - 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/_