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.

探讨大型生成式 AI 模型与云原生计算架构的交叉点，提出了一种利用云原生技术和先进的机器学习运行时的 AI 本地化计算范式，旨在优化成本和提高资源可访问性。