CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

Author:

Liu Yuhan1ORCID,Li Hanchen1ORCID,Cheng Yihua1ORCID,Ray Siddhant2ORCID,Huang Yuyang2ORCID,Zhang Qizheng3ORCID,Du Kuntai1ORCID,Yao Jiayi4ORCID,Lu Shan5ORCID,Ananthanarayanan Ganesh6ORCID,Maire Michael7ORCID,Hoffmann Henry2ORCID,Holtzman Ari8ORCID,Jiang Junchen2ORCID

Affiliation:

1. University of Chicago, Chicago, USA

2. University of Chicago, Chicago, United States of America

3. Stanford University, Palo Alto, United States of America

4. The Chinese University of Hong Kong, Shenzhen, Shenzhen, China

5. Microsoft Research, Seattle, United States of America

6. Microsoft, Seattle, United States of America

7. The University of Chicago, Chicago, United States of America

8. Meta, University of Chicago, Chicago, United States of America

Funder

NSF

UChicago CERES Center

MARIN and Stuart Rice Research Award

Chameleon Projects

Publisher

ACM

Reference157 articles.

1. 2021. How latency affects user engagement. https://pusher.com/blog/how-latency-affects-user-engagement/. (2021). (Accessed on 09/21/2023).

2. 2023. Best Practices for Deploying Large Language Models (LLMs) in Production. https://medium.com/@_aigeek/best-practices-for-deploying-large-language-models-llms-in-production-fdc5bf240d6a. (2023). (Accessed on 09/21/2023).

3. 2023. Building RAG-based LLM Applications for Production. https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1. (2023). Accessed: 2024-01-25.

4. 2024. Amazon Bedrock Pricing. https://aws.amazon.com/bedrock/pricing/. (2024). Accessed: 2024-01-25.

5. 2024. Anyscale Pricing. https://docs.endpoints.anyscale.com/pricing. (2024). Accessed: 2024-01-25.

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