{"data":{"slug":"huangowen-awesome-llm-compression","name":"Awesome-LLM-Compression","tagline":"Awesome LLM compression research papers and tools to accelerate LLM training and inference.","github_url":"https://github.com/HuangOwen/Awesome-LLM-Compression","owner":"HuangOwen","repo":"Awesome-LLM-Compression","owner_avatar_url":"https://avatars.githubusercontent.com/u/24937399?v=4","primary_language":null,"stars":1848,"forks":128,"topics":[],"archived":false,"github_pushed_at":"2026-06-30T15:26:46+00:00","maintenance_label":"Active","url":"https://www.graphcanon.com/tools/huangowen-awesome-llm-compression","markdown_url":"https://www.graphcanon.com/tools/huangowen-awesome-llm-compression.md","api_url":"https://www.graphcanon.com/api/graphcanon/tools/huangowen-awesome-llm-compression","graph_url":"https://www.graphcanon.com/api/graphcanon/graph?tool=huangowen-awesome-llm-compression","description":"Awesome LLM compression research papers and tools.","homepage_url":null,"license":"MIT","open_issues":0,"watchers":48,"ai_summary":"Compilation of research papers and tools focused on compressing large language models for improved computational efficiency during both training and serving phases.","readme_excerpt":"\n
Awesome LLM Compression
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![](\"https://awesome.re/badge.svg\"/)
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NeurIPS Lock-LLM Workshop 2025 [[Paper]](https://openreview.net/pdf?id=OkNfb8SmLh) [[Blog]] (https://namburisrinath.medium.com/compressed-but-compromised-a-study-of-jailbreaking-in-compressed-llms-02a6e40aaf17)\n\n- A Survey on Model Compression for Large Language Models
TACL [[Paper]](https://arxiv.org/abs/2308.07633)\n\n- The Cost of Compression: Investigating the Impact of Compression on Parametric Knowledge in Language Models
EMNLP 2023 [[Paper]](https://arxiv.org/abs/2312.00960) [[Code]](https://github.com/NamburiSrinath/LLMCompression)\n\n- The Efficiency Spectrum of Large Language Models: An Algorithmic Survey
Arxiv 2023 [[Paper]](https://arxiv.org/abs/2312.00678)\n\n- Efficient Large Language Models: A Survey
TMLR [[Paper]](https://arxiv.org/abs/2312.03863) [[GitHub Page]](https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey)\n\n- Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems
ICML 2024 Tutorial [[Paper]](https://arxiv.org/abs/2312.15234) [[Tutorial]](https://icml.cc/virtual/2024/tutorial/35229)\n\n- Understanding LLMs: A Comprehensive Overview from Training to Inference
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2401.02038) \n\n- Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
IJCAI 2024 (Survey Track) [[Paper]](https://arxiv.org/abs/2402.01799) [[GitHub Page]](https://github.com/nyunAI/Faster-LLM-Survey)\n\n- A Survey of Resource-efficient LLM and Multimodal Foundation Models
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2401.08092) \n\n- A Survey on Hardware Accelerators for Large Language Models
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2401.09890) \n\n- A Comprehensive Survey of Compression Algorithms for Language Models
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2401.15347)\n\n- A Survey on Transformer Compression
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2402.05964)\n\n- Model Compression and Efficient Inference for Large Language Models: A Survey
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2402.09748) \n\n- LLM Inference Unveiled: Survey and Roofline Model Insights
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2402.16363) \n\n- A Survey on Knowledge Distillation of Large Language Models
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2402.13116) [[GitHub Page]](https://github.com/Tebmer/Awesome-Knowledge-Distillation-of-LLMs)\n\n- Efficient Prompting Methods for Large Language Models: A Survey
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2404.01077)\n\n- Survey on Knowledge Distillation for Large Language Models: Methods, Evaluation, and Application
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2407.01885)\n\n- On-Device Language Models: A Comprehensive Review
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2409.00088) [[GitHub Page]](https://github.com/NexaAI/Awesome-LLMs-on-device) [[Download On-device LLMs]](https://nexaai.com/models)\n\n- A Survey of Low-bit Large Language Models: Basics, Systems, and Algorithms
Arxiv 2024 [[Paper]](https://arxiv.org/abs/2409.16694) \n\n- Contextual Com","github_created_at":"2023-05-30T06:26:11+00:00","created_at":"2026-07-11T10:32:40.865569+00:00","updated_at":"2026-07-12T02:13:32.59196+00:00","categories":[{"slug":"llm-frameworks","name":"LLM Frameworks","url":"https://www.graphcanon.com/categories/llm-frameworks","markdown_url":"https://www.graphcanon.com/categories/llm-frameworks.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/llm-frameworks"},{"slug":"inference-serving","name":"Inference & Serving","url":"https://www.graphcanon.com/categories/inference-serving","markdown_url":"https://www.graphcanon.com/categories/inference-serving.md","api_url":"https://www.graphcanon.com/api/graphcanon/categories/inference-serving"}],"tags":[{"slug":"compression","name":"compression"},{"slug":"research-papers","name":"research papers"},{"slug":"training-acceleration","name":"training acceleration"},{"slug":"efficiency","name":"efficiency"}],"trust":{"provenance":{"is_fork":false,"github_id":647138126,"owner_type":"User","methodology":"github_public_v1","parent_repo":null,"near_duplicate_slugs":[]},"computed_at":"2026-07-11T10:32:41.395Z","maintenance":{"label":"Active","score":82,"methodology":"github_public_v1","releases_90d":0,"days_since_push":10,"last_release_at":null},"security_summary":{"status":"no_lockfile","scanner":null,"low_count":0,"high_count":0,"last_scan_at":"2026-07-11T10:32:42.037Z","medium_count":0,"scan_profile":"none","critical_count":0}},"capability_facts":{"scan":{"source":"repo_scan","observed_at":"2026-07-11T11:18:23.675Z"},"license_spdx":{"value":"MIT","source":"github.license","observed_at":"2026-07-11T11:18:23.675Z"}},"decision_facts":{"hosting":null,"pricing":null,"requirements":{"notes":["The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable."]},"constraints":null,"when_to_use":["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.","For thorough understanding on how compression affects parametric knowledge, which can be critical before choosing specific compression methods."],"when_not_to_use":["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."],"source":"enrich:decision_facts","observed_at":"2026-07-11T11:18:59.188Z"},"constraint_facets":null,"decision_summary":[{"label":"Requirements","value":"The repository provides curated listings but does not develop its own software; hence specific language requirements are not applicable."},{"label":"Adopt for","value":"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."},{"label":"License detail","value":"MIT License"}]}}