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SuperCloud Developer Console (SDX) is an AI workload execution and acceleration platform that enables efficient GPU sharing across multi-tenant environments, providing a unified, secure, and scalable developer environment that reduces setup time, maximizes GPU ROI, and accelerates model delivery across both enterprise and hyperscale deployments.

Check out what your future in Developer experience could look like!

SDX empowers teams to go from code to model faster, safer, and cheaper

Developer-First Experience

  • One-click GPUaaS + ML pipeline provisioning.
  • Admin can preload datasets and share them with designated tenants.
  • Service Catalog allows admins to create application deployment templates for users to deploy based on their needs.
  • GPU slicing across NVIDIA and AMD GPUs, with full-device allocation support for Intel Gaudi accelerators.

Al-Native Workflows

  • Supports training, fine-tuning, inference, and benchmarking use cases.
  • Integrated observability for infrastructure metrics such as GPU utilization and VRAM usage.
  • Self-service workflows allow users to submit resource pool and quota requests for admin approval and automated updates.

Multi-Tenant & Secure by Design

  • RBAC, SSO, secrets management, project-based access.
  • Quota management helps control tenant allocation and prevents cluster resources from being monopolized.
  • Enterprise-ready audit trails and compliance controls.

Hybrid & Edge Ready

  • Same developer experience across datacenter, cloud, and edge.
  • Built-in terminal access helps cluster administrators troubleshoot and debug environments directly.

Operational Agility

  • 50-60% faster time-to-experiment.
  • Provides API-based integration for external tools and platform automation.
  • Compatible with Upstream and Leading Kubernetes Distribution
  • Integrated Observability
Software Partners
Customer Stories

Our requirement is to distribute GPU resources fairly and to facilitate management. After the introduction of Developer Console system, AI artificial intelligence teaching will be simplified. Teachers and students can focus on the establishment and training of AI models without spending a lot of time on the operation of the system. It can speed up stepping into the application areas of AI artificial intelligence and machine learning.

From Takming University of Science and Technology Computer Center