Awesome-LLM-Compression
Awesome LLM compression research papers and tools to accelerate LLM training and inference.
GraphCanon updated Oct 11, 2026 · GitHub synced Oct 11, 2026
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Decision brief
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.
Good fit when
- When you need to explore the latest advancements in LLM compression techniques and their impact on both training and inference.
- If your project requires a detailed survey of model compression for large language models covering various aspects like quantization, pruning, distillation, and efficient prompting.
Avoid when
- 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.
- Requirements:
- The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable.
Observed Jul 11, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Steady (44d since push)
- As of Oct 11, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 11, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
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Install
git clone https://github.com/HuangOwen/Awesome-LLM-CompressionHow it fits your stack(1)
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Overview
Compilation of research papers and tools focused on compressing large language models for improved computational efficiency during both training and serving phases.
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README
<div align="center" <h1 Awesome LLM Compression</h1 <a href="https://awesome.re" <img src="https://awesome.re/badge.svg"/ </a <img src=https://img.shields.io/github/stars/HuangOwen/Awesome LLM Compression.svg?style=social <img src=https://img.shields.io/github/watchers/HuangOwen/...
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