[2306.07629] SqueezeLLM: Dense-and-Sparse Quantization

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack

[2306.07629] SqueezeLLM: Dense-and-Sparse Quantization Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Computation and Language arXiv:2306.07629 (cs) [Submitted on 13 Jun 2023 ( v1 ), last revised 5 Jun 2024 (this version, v4)] Title: SqueezeLLM: Dense-and-Sparse Quantization Authors: Sehoon Kim , Coleman Hooper , Amir Gholami , Zhen Dong , Xiuyu Li , Sheng Shen , Michael W. Mahoney , Kurt Keutzer View a PDF of the paper titled SqueezeLLM: Dense-and-Sparse Quantization, by Sehoon K

related reading

• LLM Inference Performance Engineering: Best Practices | Databricks Blogdatabricks.com
• A Guide to Quantization in LLMs | Symbl.aisymbl.ai
• Optimizing inference · Hugging Facehuggingface.co
• How is LLaMa.cpp possible?finbarr.ca
• TurboQuant: Redefining AI efficiency with extreme compressionresearch.google
• Reiner Pope – The math behind how LLMs are trained and serveddwarkesh.com
• SmoothQuant: Accurate and EfficientPost-Training Quantization for Large Language Modelsarxiv.org
• ⭐️ Fast LLM Inference From Scratchandrewkchan.dev
• LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Modelsarxiv.org
• LLM Resourcesforrestbicker.com
• Best practices to accelerate inference for large-scale production workloadstogether.ai
• Transformers Inference Optimization Toolset | AstraBlogastralord.github.io