Tensor OS Enterprise
End-to-end AI compute operating system built on GPU virtualization
A full-stack platform for GPU partitioning, pooling, scheduling, monitoring, and operations, reducing compute costs by 60–80%
Who is it for
When GPU clusters, team queues, and cost allocation become operational bottlenecks, Tensor OS turns compute into a managed resource pool.
- Inventory and pool existing GPU resources
- Give each team quotas and priority policies
- Track utilization, cost, and alerts continuously
The production loop
From inventory and pooling to quotas and alerts, teams use compute according to business priority.
- Audit resources and workloads
- Schedule shared capacity across teams
- Operate with monitoring and cost attribution
核心能力
全方位覆盖 AI 算力管理场景，开箱即用
GPU virtualization
Partition GPU capacity precisely with isolated virtual addresses and errors.
GPU pooling and scheduling
Pool GPU resources across nodes with embedded and remote vGPU modes.
Multi-tenancy
Manage quotas, QoS, billing, and permissions across teams.
Full-stack monitoring
Monitor compute, memory, temperature, network, and alerts end to end.
Zero-intrusion deployment
Run existing CUDA workloads across Kubernetes, VMs, and bare metal.
Security and compliance
Audit logs, access controls, and encryption support enterprise requirements.