Awesome-LLM-Inference
A curated list of LLM/VLM inference papers with codes
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Decision brief
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.
Good fit when
- 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.
- If you need a comprehensive list of resources for efficient inference strategies that include both recent and foundational papers, this repository is a valuable resource.
Avoid when
- 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.
- Requirements:
- Requires Python for the use of included codes and to understand the methods described in the associated papers.
Observed Jul 14, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
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- Not a fork · Organization account
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Install
pip install Awesome-LLM-InferenceHow it fits your stack(1)
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Evidence and technical details
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Overview
Gathers information on various techniques for efficient large language model and vision-language model inference like Flash-Attention, Paged-Attention.
Capability facts
- Languages
- python
Source: github.language · Aug 24, 2026
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README
©️License
GNU General Public License v3.0
For agents
This page has a .md twin and JSON over the API.