AI Management Console


The development of artificial intelligence (AI) undoubtedly brings unprecedented technological progress in human history. Gemini AI Console helps data engineers to manage single or multiple GPU servers easily, allowing valuable GPU resources to be used more efficiently, further reduces the cost of Big Data and AI development.
From big data preprocessing, AI model training, all the way to the complex process of AI application deployment, enterprises often suffer from heterogeneous environments, multiple computing architectures, and even cross-departmental collaborations required at different stages. Gemini AI console aims to facilitate enterprise and organization for cross-unit AI project collaboration, therefore help enterprises more efficiently extract better business opportunities from massive data.



Features Description
User and Roles Management System administrator can create multiple projects and manage user accounts, roles and authorization. System administrator can use the Corporate LDAP or other authentication mechanisms to authorize users for specific services.
Jobs/Service Definition Gemini AI Console supports two kinds of operation models: Cloud Service Clusters (pay-by-capacity) and Batch Jobs (pay-by-utility). Users and administrators can upload and use specific VM and container image files through our private Image Registry.
Resource Management and Workload Management Gemini AI Console provides user self-provisioning for AI services which can run in heterogeneous environments (VM, Docker and GPU). In batch mode, jobs will be scheduled by the GOC workload manager. In addition, Gemini AI Console supports resource quota management by project and horizontal scale-out.
Integrate with Existing Service Architecture GOC architecture allows organizations to easily integrate existing system such as LDAP, NFS, and more. Therefore, existing users can log in to Gemini AI Console to directly use the file on the NFS server. In addition, companies can use existing organizational structure access lists (ACLs) to control user permissions and data.
Resource Monitoring and Reporting Through the user dashboard interface, the real-time usage statistics and historical data of all cloud services and jobs are presented, and the resource usage report can be provided for the billing basis.
Management Portal Gemini AI Console provides a user friendly portal for both system administrator and users to manage resources and services, including creating new service or submit jobs with few clicks. This management portal also provides automatic alarm notification for abnormal events.


  • Simplify IT complexity and optimize GPU management
    Manage the physical and virtual resources for multiple GPU and CPU Servers with a single platform. It can also optimize the utilization of GPU resources according to the needs of business organization and cross-unit AI projects.
  • Improve R&D efficiency and shorten development time
    Make it easier to prepare the complex infrastructure environment with simple browser interface for the deployment of Big Data and AI computing tools, which helps scientists to focus on their AI algorithm development and training.
  • Support different computing architectures and heterogeneous environments, brining perfect AI computing experience
    A single platform can manage big data services and AI machine learning clusters at the same time. No matter using virtual machines (VMs) or containers (Docker) with GPU, users can manage easily through the management portal. Support both ‘Cloud Service’ and ‘Batch Jobs‘, which can meet different usage scenarios and definitely enhance the AI development process experience.
  • Single Web portal for both IT operation team and AI development team
  • The single-entry web interface provides IT managers to manage resources and users, and to provide a cloud marketplace architecture that allows IT to quickly and dynamically deploy AI cloud services
  • Multi-cloud Management: support VM, container, object storage and file storage, and can be divided into two types of computing resources: dedicated zone and shard zone.
  • Provide complete Restful APIs with API Gateway for customized requirements