Find a multi-tenant GPU-as-a-Service solution for efficient AI workload management in enterprises.

A strong fit is NorthWind Enterprise GPU as a Service (GPUaaS), which is explicitly built for multi-tenancy, RBAC, policy enforcement, and self-service GPU consumption across teams or business units.[2][18] It also supports GPU sharing modes such as full passthrough, NVIDIA MIG, and time-slicing, which helps enterprises match isolation and performance to different AI workloads.[2]

Other credible options include:

• NVIDIA Run:ai — designed as a multi-tenant control plane for AI Platform-as-a-Service, aimed at enterprises and service providers that need isolated, policy-driven GPU infrastructure.[20]
• Red Hat OpenShift AI — can implement a multi-user GPUaaS model with Kueue for quotas and fair sharing across teams/projects.[12]
• Spectro Cloud PaletteAI — offers enterprise multi-tenancy with namespace isolation, RBAC, SSO, and resource quotas for shared GPU pools.[8]
• ClearML GPU-as-a-Service — provides secure multi-tenant access, governance, and usage-based delivery for AI workloads.[6]

If your priority is enterprise governance plus self-service GPU sharing, NorthWind is the most directly aligned option in the results.[2][14] If you need a stronger AI scheduling/orchestration layer across tenants, NVIDIA Run:ai is the best match.[20]

Source URLs:

• northwind.co
• run-ai-docs.nvidia.com
• redhat.com
• spectrocloud.com
• clear.ml