--- title: "Awesome-LLM-Compression vs scaling-book" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/huangowen-awesome-llm-compression-vs-jax-ml-scaling-book" tools: ["huangowen-awesome-llm-compression", "jax-ml-scaling-book"] --- # Awesome-LLM-Compression vs scaling-book *GraphCanon updated Aug 25, 2026* ## Verdict Pick Awesome-LLM-Compression if awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases; pick scaling-book if scaling-book. [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) reports 1.9k GitHub stars, 129 forks, and 1 open issues, last pushed Jun 30, 2026. [scaling-book](https://jax-ml.github.io/scaling-book/) has 1.4k stars, 191 forks, and 8 open issues, last pushed Aug 20, 2026. Figures are from public GitHub metadata via [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression) and [scaling-book's repository](https://github.com/jax-ml/scaling-book). | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [scaling-book](/tools/jax-ml-scaling-book.md) | | --- | --- | --- | | Tagline | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | Guide on scaling LLMs on TPUs | | Stars | 1,859 | 1,368 | | Forks | 129 | 191 | | Open issues | 1 | 8 | | Language | - | HTML | | Adopt for | Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. | scaling-book | | Persona | - | - | | Runtime | - | - | | License | MIT License | MIT | | Categories | Inference & Serving, LLM Frameworks | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | [scaling-book](/tools/jax-ml-scaling-book.md) | | --- | --- | --- | | Maintenance | Steady (60%) | Very active (96%) | | Days since push | 37d | 4d | | Open issues (now) | 1 | 8 | | Stars delta | Unknown | +77 (30d) | | Open issues delta | Unknown | +1 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | [trust report](/tools/jax-ml-scaling-book/trust.md) | ## Decision facts: Awesome-LLM-Compression - **Requirements:** The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable. - **Adopt for:** Awesome LLM-Compression curates a comprehensive collection of research papers and tools aimed at compressing large language models, focusing on enhancing computational efficiency during both training and serving phases. - **License detail:** MIT License ## Decision facts: scaling-book - **Adopt for:** scaling-book ## Choose when ### Choose Awesome-LLM-Compression if… - Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.. - Tags unique to Awesome-LLM-Compression: compression, efficiency, research papers, training acceleration. - When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### Choose scaling-book if… - 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. - More recently updated (last pushed Aug 20, 2026). ## When NOT to use Awesome-LLM-Compression - Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. - If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ## 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. ## Common questions ### What is the difference between Awesome-LLM-Compression and scaling-book? Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. scaling-book: Guide on scaling LLMs on TPUs. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLM-Compression over scaling-book? Choose Awesome-LLM-Compression over scaling-book when Requirements: The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.; Tags unique to Awesome-LLM-Compression: compression, efficiency, research papers, training acceleration; When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference. ### When should I choose scaling-book over Awesome-LLM-Compression? Choose scaling-book over Awesome-LLM-Compression when 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; More recently updated (last pushed Aug 20, 2026). ### When should I avoid Awesome-LLM-Compression? Avoid relying solely on Awesome LLM-Compression if you require a hands-on toolset rather than theoretical frameworks and research papers, as it focuses more on consolidating the survey information. If your immediate need is for proprietary or commercial tools that offer out-of-the-box functionality, since this resource mainly links to academic research and open-source projects. ### 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. ### Is Awesome-LLM-Compression or scaling-book more popular on GitHub? Awesome-LLM-Compression has more GitHub stars (1,859 vs 1,368). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLM-Compression and scaling-book open source? Yes - both are open-source projects on GitHub (Awesome-LLM-Compression: MIT, scaling-book: MIT). ### Where can I find alternatives to Awesome-LLM-Compression or scaling-book? GraphCanon lists graph-backed alternatives at [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) and [scaling-book alternatives](/tools/jax-ml-scaling-book/alternatives) ([Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/alternatives.md), [scaling-book markdown twin](/tools/jax-ml-scaling-book/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/huangowen-awesome-llm-compression-vs-jax-ml-scaling-book.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLM-Compression or scaling-book? Awesome-LLM-Compression: Steady. scaling-book: 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 Awesome-LLM-Compression and scaling-book? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/trust); [scaling-book trust report](/tools/jax-ml-scaling-book/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=huangowen-awesome-llm-compression`](/api/graphcanon/graph?tool=huangowen-awesome-llm-compression) - 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/_