Advantages

Vision

As AI training and inference workloads grow rapidly in scale, enterprises face challenges including low GPU utilization, complex multi-card scheduling, and uncontrolled compute costs. Traditional Kubernetes platforms lack deep support for heterogeneous GPU hardware, and the operational complexity forces AI engineers to write large amounts of complex YAML configurations.

Kube AI Hub addresses these challenges with a Kubernetes-native heterogeneous compute management platform. Through GPU/CPU resource pooling and vGPU virtualization, it helps enterprises improve compute utilization by 3–10x while providing comprehensive multi-tenant management, monitoring and alerting, and metering capabilities.

Why Kube AI Hub

The following are the key advantages of Kube AI Hub.

Unified Heterogeneous GPU Compute Management

Supports unified onboarding and scheduling of NVIDIA, Huawei Ascend, Cambricon, Iluvatar, and other mainstream and domestic GPUs — eliminating hardware silos entirely.

• A single console manages multiple GPU types without separate tooling per vendor
• Real-time per-card metrics for utilization, VRAM, temperature, and power consumption
• Online node addition for fast compute capacity expansion
• Built-in vGPU virtualization for fine-grained GPU slicing and multi-task sharing

Thousand-Card Distributed Scheduling

Capable of scheduling at thousand-GPU scale, with built-in priority job queues and resource reservation policies to ensure stable execution of large-scale AI training workloads.

• Distributed training job scheduling for PyTorch, TensorFlow, and other major frameworks
• Job queues with priority preemption and resource reservation to prevent critical task starvation
• Elastic scaling policies that dynamically allocate compute based on workload demand
• GPU node health checks and automatic fault isolation for training continuity

Powerful Full-Stack Observability

Second-level GPU/CPU monitoring across all dimensions, paired with flexible alerting policies, enables ops teams to detect cluster anomalies immediately.

• Multi-level monitoring: cluster, node, pod, and container
• Dedicated GPU resource monitoring view: utilization, VRAM, temperature, power
• Custom alerting rules and thresholds with notification channels including email, WeCom, DingTalk, and Slack
• Multi-tenant log isolation with centralized collection and search for fast troubleshooting

Fine-grained Multi-tenant Access Control

Built-in Platform → Workspace → Project three-tier permission isolation model with LDAP/AD integration, meeting the fine-grained access control needs of large organizations.

• Different teams and departments work independently in isolated namespaces without resource interference
• Custom roles and permission sets for fine-grained authorization
• SSO single sign-on support to reduce authentication overhead for enterprise users

Transparent and Controllable Compute Costs

Built-in metering module tracks compute usage by tenant, department, and project, generating exportable usage reports to support IT budget planning and cost accounting.

• Real-time tracking of GPU/CPU resource consumption across all dimensions
• Multi-dimensional billing statistics and export for cost allocation
• Quota management prevents resource contention and ensures fair compute distribution

Modular and Pluggable Architecture

All feature modules are optional and can be enabled on demand. The loosely coupled architecture supports flexible integration with third-party schedulers, storage systems, and monitoring stacks.

• Runs on any compatible Kubernetes cluster (bare metal, private cloud, public cloud)
• Supports both online and air-gapped installation
• Multiple storage backends: S3, NFS, Ceph, LocalPV
• Multiple network plugins: Calico, Flannel, and others

For more information, see Features and Scenarios.