cube-studio: a cloud-native one-stop machine learning platform for managing diverse AI compute and storage resources

What it solves

Cube Studio is an open-source, cloud-native machine learning platform designed to provide a one-stop shop for the entire ML lifecycle. It addresses the complexity of managing resources, users, and infrastructure for AI development, training, and deployment in a large-scale environment.

How it works

It operates as a cloud-native platform that integrates various infrastructure capabilities. It manages compute resources (CPU, GPU, and specialized AI chips), storage (NFS, S3, etc.), and network configurations. It provides a centralized management interface for project groups, user roles (RBAC), and resource allocation across multiple Kubernetes clusters, including support for edge clusters and serverless modes (Tencent Cloud and Alibaba Cloud).

Who it’s for

It is designed for organizations and teams that need a scalable, enterprise-grade ML platform to manage their AI workloads across diverse hardware and cloud environments.

Highlights

• Broad Hardware Support: Supports a wide range of GPUs (T4, V100, A100) and domestic AI chips (DCU, NPU, MLU), as well as RDMA and vGPU.
• Enterprise Management: Includes built-in RBAC, SSO (LDAP, OID), and detailed resource metering and billing for development, training, and inference.
• Flexible Infrastructure: Supports multiple Kubernetes clusters, containerd, and a variety of distributed storage options (S3, MinIO, CephFS, etc.).
• Cloud-Native Integration: Offers serverless cluster modes for major cloud providers and supports edge cluster deployments for training and inference.