--- title: "Awesome-LLMs-ICLR-24 vs Awesome-LLM-Compression" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-huangowen-awesome-llm-compression" tools: ["azminewasi-awesome-llms-iclr-24", "huangowen-awesome-llm-compression"] --- # Awesome-LLMs-ICLR-24 vs Awesome-LLM-Compression *GraphCanon updated Aug 8, 2026* ## Verdict Pick Awesome-LLMs-ICLR-24 if awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024; 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. [Awesome-LLMs-ICLR-24](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) reports 72 GitHub stars, 5 forks, and 0 open issues, last pushed Apr 4, 2024. [Awesome-LLM-Compression](https://github.com/HuangOwen/Awesome-LLM-Compression) has 1.9k stars, 129 forks, and 1 open issues, last pushed Jun 30, 2026. Figures are from public GitHub metadata via [Awesome-LLMs-ICLR-24's repository](https://github.com/azminewasi/Awesome-LLMs-ICLR-24) and [Awesome-LLM-Compression's repository](https://github.com/HuangOwen/Awesome-LLM-Compression). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | Awesome LLM compression research papers and tools to accelerate LLM training and inference. | | Stars | 72 | 1,859 | | Forks | 5 | 129 | | Open issues | 0 | 1 | | Language | - | - | | Adopt for | Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. | 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. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT License | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Inference & Serving, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-LLM-Compression](/tools/huangowen-awesome-llm-compression.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Steady (60%) | | Days since push | 856d | 37d | | Open issues (now) | 0 | 1 | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/huangowen-awesome-llm-compression/trust.md) | ## Decision facts: Awesome-LLMs-ICLR-24 - **Adopt for:** Awesome-LLMs-ICLR-24 is an essential resource hub for researchers and developers working with large language models, focusing on LLM research papers accepted at ICLR in 2024. ## 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 ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Evaluation & Observability, Model Training. - If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### 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. ## When NOT to use Awesome-LLMs-ICLR-24 - If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. - For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ## 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. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and Awesome-LLM-Compression? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. Awesome-LLM-Compression: Awesome LLM compression research papers and tools to accelerate LLM training and inference.. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over Awesome-LLM-Compression? Choose Awesome-LLMs-ICLR-24 over Awesome-LLM-Compression when Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability, Model Training; If you are focusing specifically on recent advancements in Large Language Models discussed in the context of ICLR 2024, this repository will provide cutting-edge research papers and insights. ### When should I choose Awesome-LLM-Compression over Awesome-LLMs-ICLR-24? Choose Awesome-LLM-Compression over Awesome-LLMs-ICLR-24 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 avoid Awesome-LLMs-ICLR-24? If you are looking for more general resources that cover a wider time span or different conferences than ICLR 2024. For projects where immediate practical application of models without understanding the underlying research is prioritized over detailed exploration and analysis. ### 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. ### Is Awesome-LLMs-ICLR-24 or Awesome-LLM-Compression more popular on GitHub? Awesome-LLM-Compression has more GitHub stars (1,859 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and Awesome-LLM-Compression open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, Awesome-LLM-Compression: MIT). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or Awesome-LLM-Compression? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [Awesome-LLM-Compression alternatives](/tools/huangowen-awesome-llm-compression/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [Awesome-LLM-Compression markdown twin](/tools/huangowen-awesome-llm-compression/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/azminewasi-awesome-llms-iclr-24-vs-huangowen-awesome-llm-compression.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, Awesome-LLMs-ICLR-24 or Awesome-LLM-Compression? Awesome-LLMs-ICLR-24: Dormant. Awesome-LLM-Compression: Steady. 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-LLMs-ICLR-24 and Awesome-LLM-Compression? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [Awesome-LLMs-ICLR-24 trust report](/tools/azminewasi-awesome-llms-iclr-24/trust); [Awesome-LLM-Compression trust report](/tools/huangowen-awesome-llm-compression/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24`](/api/graphcanon/graph?tool=azminewasi-awesome-llms-iclr-24) - 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/_