为企业 AI 构建本地 GPU 服务架构

The design of GPU as a Service (GPUaaS) architecture is crucial for on-premises enterprise AI deployments. Key factors for efficient GPU resource utilization include managing multi-tenancy, optimizing resource scheduling, and constructing cost models. Utilizing the Kubernetes platform allows for dynamic allocation and management of GPU resources, supporting concurrent operations of multiple users and applications. An effective scheduling strategy maximizes GPU utilization and reduces overall operational costs for enterprises. Additionally, establishing accurate cost models aids organizations in making informed decisions regarding resource allocation and budget control. Overall, developing an efficient GPUaaS solution will accelerate the growth of enterprise AI applications.