GPU resource management & AI Education solutions

AI Education solution with multi-tenancy and hierarchical architecture. Professors and Teaching assistants can set up separate project for each student. Teachers and students could concentrate on AI model network development instead of wasting time on system and GPU resource management.

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Gemini AI Education Cloud Introduction

Deployment Teaching Environment

Teachers can quickly deploy shared GPUs environment with teaching tools for students.

Teaching and Experiment

Teachers can monitor resource usage at any time during class, and can manage resources flexibly in needed.

Resource Recycling

Teachers can easily recycle resources after class, and can allocate resources for students to do homework or projects.


  • GPU Partitioning Sharing
  • Course-specific teaching environment
  • Automated batch deployment before class
  • Automatically mount student-specific folders
  • Graphical Web AI Development Editor
  • Supports multiple GPU-accelerated computing
  • Instantly mount GPU resources during development
  • Customize AI workflows
  • Automated resource scheduling
  • AI Tuning Tools


Quick to use

Quickly activate the service and operate directly with the Jupyter interface, which greatly reduces the tedious system operation process!

GPU Partitioning

A single GPU can be allocated to multiple students, making efficient use of precious GPU resources!

Rich Tools

There are multiple built-in AI development frameworks, and the teaching software installation package can be customized to best meet the needs of the course.

Resource Control

Customize resource quotas and group member management permissions according to different course and research needs.

Resource Integration

It can integrate campus account and storage services to quickly start exclusive solutions!

professional service

Provide all kinds of documents and education training, and provide up to 7x24 hours of local professional services!

Gemini AI Education Cloud users

Professors & Teaching Assistants

Project Research, Teaching Environment Construction

PhD student

Use independent computing resources

Master student

Research assignments are used as needed independent or shared computing resources.

College Students

Use CPU/GPU shared resources

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