Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native
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arXiv
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| Format: | Preprint |
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2024
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| author | Lu, Yao Bian, Song Chen, Lequn He, Yongjun Hui, Yulong Lentz, Matthew Li, Beibin Liu, Fei Li, Jialin Liu, Qi Liu, Rui Liu, Xiaoxuan Ma, Lin Rong, Kexin Wang, Jianguo Wu, Yingjun Wu, Yongji Zhang, Huanchen Zhang, Minjia Zhang, Qizhen Zhou, Tianyi Zhuo, Danyang |
| author_facet | Lu, Yao Bian, Song Chen, Lequn He, Yongjun Hui, Yulong Lentz, Matthew Li, Beibin Liu, Fei Li, Jialin Liu, Qi Liu, Rui Liu, Xiaoxuan Ma, Lin Rong, Kexin Wang, Jianguo Wu, Yingjun Wu, Yongji Zhang, Huanchen Zhang, Minjia Zhang, Qizhen Zhou, Tianyi Zhuo, Danyang |
| contents | In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in their capabilities, face challenges like escalating costs and demand for high-end GPUs. Drawing analogies between large-model-as-a-service (LMaaS) and cloud database-as-a-service (DBaaS), we describe an AI-native computing paradigm that harnesses the power of both cloud-native technologies (e.g., multi-tenancy and serverless computing) and advanced machine learning runtime (e.g., batched LoRA inference). These joint efforts aim to optimize costs-of-goods-sold (COGS) and improve resource accessibility. The journey of merging these two domains is just at the beginning and we hope to stimulate future research and development in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_12230 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native Lu, Yao Bian, Song Chen, Lequn He, Yongjun Hui, Yulong Lentz, Matthew Li, Beibin Liu, Fei Li, Jialin Liu, Qi Liu, Rui Liu, Xiaoxuan Ma, Lin Rong, Kexin Wang, Jianguo Wu, Yingjun Wu, Yongji Zhang, Huanchen Zhang, Minjia Zhang, Qizhen Zhou, Tianyi Zhuo, Danyang Distributed, Parallel, and Cluster Computing Machine Learning In this paper, we investigate the intersection of large generative AI models and cloud-native computing architectures. Recent large models such as ChatGPT, while revolutionary in their capabilities, face challenges like escalating costs and demand for high-end GPUs. Drawing analogies between large-model-as-a-service (LMaaS) and cloud database-as-a-service (DBaaS), we describe an AI-native computing paradigm that harnesses the power of both cloud-native technologies (e.g., multi-tenancy and serverless computing) and advanced machine learning runtime (e.g., batched LoRA inference). These joint efforts aim to optimize costs-of-goods-sold (COGS) and improve resource accessibility. The journey of merging these two domains is just at the beginning and we hope to stimulate future research and development in this area. |
| title | Computing in the Era of Large Generative Models: From Cloud-Native to AI-Native |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2401.12230 |