--- title: "Awesome-LLMs-ICLR-24 vs Awesome-LLM-Inference" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/azminewasi-awesome-llms-iclr-24-vs-xlite-dev-awesome-llm-inference" tools: ["azminewasi-awesome-llms-iclr-24", "xlite-dev-awesome-llm-inference"] --- # Awesome-LLMs-ICLR-24 vs Awesome-LLM-Inference *GraphCanon updated Aug 24, 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-Inference if awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention. [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-Inference](https://github.com/xlite-dev/Awesome-LLM-Inference) has 5.5k stars, 429 forks, and 6 open issues, last pushed Aug 14, 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-Inference's repository](https://github.com/xlite-dev/Awesome-LLM-Inference). | | [Awesome-LLMs-ICLR-24](/tools/azminewasi-awesome-llms-iclr-24.md) | [Awesome-LLM-Inference](/tools/xlite-dev-awesome-llm-inference.md) | | --- | --- | --- | | Tagline | Compilation of LLM papers from ICLR 2024 | A curated list of LLM/VLM inference papers with codes | | Stars | 72 | 5,477 | | Forks | 5 | 429 | | Open issues | 0 | 6 | | Language | - | Python | | 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-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention. | | Persona | - | - | | Runtime | - | - | | License | MIT | The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs. | | Categories | Developer Tools, Evaluation & Observability, Inference & Serving, LLM Frameworks, Model Training | Inference & Serving | ## 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-Inference](/tools/xlite-dev-awesome-llm-inference.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Active (82%) | | Days since push | 856d | 10d | | Open issues (now) | 0 | 6 | | Stars delta | Unknown | +62 (30d) | | Open issues delta | Unknown | 0 (30d) | | Owner type | User | Organization | | Full report | [trust report](/tools/azminewasi-awesome-llms-iclr-24/trust.md) | [trust report](/tools/xlite-dev-awesome-llm-inference/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-Inference - **Requirements:** Requires Python for the use of included codes and to understand the methods described in the associated papers. - **Adopt for:** Awesome-LLM-Inference is a well-curated list of papers and codes related to efficient inference techniques for large language models and vision-language models, featuring methods like Flash-Attention and Paged-Attention. - **License detail:** The tool is licensed under GPL-3.0, which may affect how it can be integrated into other projects depending on their licensing needs. ## Choose when ### Choose Awesome-LLMs-ICLR-24 if… - License: Awesome-LLMs-ICLR-24 is MIT, Awesome-LLM-Inference is GPL-3.0. - Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework. - Also covers Developer Tools, Evaluation & Observability, LLM Frameworks, 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-Inference if… - License: Awesome-LLM-Inference is GPL-3.0, Awesome-LLMs-ICLR-24 is MIT. - Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers.. - Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4. - Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention. ## 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-Inference - Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements. - Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets. ## Common questions ### What is the difference between Awesome-LLMs-ICLR-24 and Awesome-LLM-Inference? Awesome-LLMs-ICLR-24: Compilation of LLM papers from ICLR 2024. Awesome-LLM-Inference: A curated list of LLM/VLM inference papers with codes. See the comparison table for live GitHub stats and shared categories. ### When should I choose Awesome-LLMs-ICLR-24 over Awesome-LLM-Inference? Choose Awesome-LLMs-ICLR-24 over Awesome-LLM-Inference when License: Awesome-LLMs-ICLR-24 is MIT, Awesome-LLM-Inference is GPL-3.0; Tags unique to Awesome-LLMs-ICLR-24: large-language-model, llm-agent, llm-evaluation, llm-framework; Also covers Developer Tools, Evaluation & Observability, LLM Frameworks, 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-Inference over Awesome-LLMs-ICLR-24? Choose Awesome-LLM-Inference over Awesome-LLMs-ICLR-24 when License: Awesome-LLM-Inference is GPL-3.0, Awesome-LLMs-ICLR-24 is MIT; Requirements: Requires Python for the use of included codes and to understand the methods described in the associated papers.; Tags unique to Awesome-LLM-Inference: flash-attention, paged-attention, parallelism, wint8/4; Use Awesome-LLM-Inference when you are looking to optimize the performance of your large language model or vision-language model inference with cutting-edge techniques such as Flash-Attention. ### 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-Inference? Do not use Awesome-LLM-Inference if your project strictly conforms to licenses different from GPL-3.0, as its licensing could be incompatible with your project's license requirements. Avoid using this tool for immediate production implementation of inference techniques without additional vetting since the repository itself may contain unvetted research papers and code snippets. ### Is Awesome-LLMs-ICLR-24 or Awesome-LLM-Inference more popular on GitHub? Awesome-LLM-Inference has more GitHub stars (5,477 vs 72). Stars measure visibility, not whether either tool fits your constraints. ### Are Awesome-LLMs-ICLR-24 and Awesome-LLM-Inference open source? Yes - both are open-source projects on GitHub (Awesome-LLMs-ICLR-24: MIT, Awesome-LLM-Inference: GPL-3.0). ### Where can I find alternatives to Awesome-LLMs-ICLR-24 or Awesome-LLM-Inference? GraphCanon lists graph-backed alternatives at [Awesome-LLMs-ICLR-24 alternatives](/tools/azminewasi-awesome-llms-iclr-24/alternatives) and [Awesome-LLM-Inference alternatives](/tools/xlite-dev-awesome-llm-inference/alternatives) ([Awesome-LLMs-ICLR-24 markdown twin](/tools/azminewasi-awesome-llms-iclr-24/alternatives.md), [Awesome-LLM-Inference markdown twin](/tools/xlite-dev-awesome-llm-inference/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-xlite-dev-awesome-llm-inference.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-Inference? Awesome-LLMs-ICLR-24: Dormant. Awesome-LLM-Inference: 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-LLMs-ICLR-24 and Awesome-LLM-Inference? 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-Inference trust report](/tools/xlite-dev-awesome-llm-inference/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/_