Awesome-LLM-Compression
Awesome LLM compression research papers and tools to accelerate LLM training and inference.
GraphCanon updated today · GitHub synced today
Trust & integrity
Full report- Maintenance
- Active (10d since push)
- As of today · Source: github_public_v1
- Provenance
- Not a fork · Personal account
- As of today · Source: github_public_v1
- Security (OSV)
- No lockfile
- As of today · Source: none
Public GitHub metadata and optional OSV dependency scans. Signals, not a guarantee. Trust methodology.
Overview
Compilation of research papers and tools focused on compressing large language models for improved computational efficiency during both training and serving phases.
Capability facts
No sourced capability facts yet. Facts appear after ingest scans repo manifests (Dockerfile, package.json, MCP configs).
Categories
Tags
README
Awesome LLM Compression
Awesome LLM compression research papers and tools to accelerate LLM training and inference.
Contents
- 📑 Papers
- Survey
- Quantization
- Pruning and Sparsity
- Distillation
- Efficient Prompting
- KV Cache Compression
- Other
- 🔧 Tools
- 🙌 Contributing
- 🌟 Star History
Papers
Survey
-
Compressed but Compromised? A Study of Jailbreaking in Compressed LLMs
NeurIPS Lock-LLM Workshop 2025 [Paper] [[Blog]] (https://namburisrinath.medium.com/compressed-but-compromised-a-study-of-jailbreaking-in-compressed-llms-02a6e40aaf17) -
A Survey on Model Compression for Large Language Models
TACL [Paper] -
The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models
EMNLP 2023 [Paper] [Code] -
The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
Arxiv 2023 [Paper] -
Efficient Large Language Models: A Survey
TMLR [Paper] [GitHub Page] -
Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
ICML 2024 Tutorial [Paper] [Tutorial] -
Understanding LLMs: A Comprehensive Overview from Training to Inference
Arxiv 2024 [Paper] -
Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
IJCAI 2024 (Survey Track) [Paper] [GitHub Page] -
A Survey of Resource-efficient LLM and Multimodal Foundation Models
Arxiv 2024 [Paper] -
A Survey on Hardware Accelerators for Large Language Models
Arxiv 2024 [Paper] -
A Comprehensive Survey of Compression Algorithms for Language Models
Arxiv 2024 [Paper] -
A Survey on Transformer Compression
Arxiv 2024 [Paper] -
Model Compression and Efficient Inference for Large Language Models: A Survey
Arxiv 2024 [Paper] -
LLM Inference Unveiled: Survey and Roofline Model Insights
Arxiv 2024 [Paper] -
A Survey on Knowledge Distillation of Large Language Models
Arxiv 2024 [Paper] [GitHub Page] -
Efficient Prompting Methods for Large Language Models: A Survey
Arxiv 2024 [Paper] -
Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Arxiv 2024 [Paper] -
On-Device Language Models: A Comprehensive Review
Arxiv 2024 [Paper] [GitHub Page] [Download On-device LLMs] -
A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms
Arxiv 2024 [Paper] -
Contextual Com