Computing in the Era of Large Generative Models: From Cloud-Native to
  AI-Native
Yao Lu Song Bian Lequn Chen Yongjun He Yulong Hui Matthew Lentz Beibin Li Fei Liu Jialin Li Qi Liu Rui Liu Xiaoxuan Liu Lin Ma Kexin Rong Jianguo Wang Yingjun Wu Yongji Wu Huanchen Zhang Minjia Zhang Qizhen Zhang Tianyi Zhou Danyang Zhuo
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.