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

For an enterprise looking for a multi-tenant GPU-as-a-Service (GPUaaS) layer rather than simply renting GPU VMs, I’d shortlist these:

• NVIDIA Run:ai — Best overall fit. It provides Kubernetes-native GPU orchestration, quotas, scheduling, workload prioritization, fractional GPU allocation, and multi-cluster management. NVIDIA specifically positions it for improving utilization across AI teams and projects, and it can operate on-premises, cloud, or hybrid infrastructure. NVIDIA also offers a dedicated multi-tenant architecture. (nvidia.com)
• NVIDIA Run:ai on DGX Cloud — Better when you want the GPU infrastructure delivered as a managed cloud service rather than building the GPU cluster yourself. The subscription combines dedicated GPU capacity, Run:ai orchestration, and NVIDIA AI Enterprise software. (docs.nvidia.com)
• Red Hat OpenShift AI + NVIDIA GPU Operator — Strong alternative for organizations standardized on OpenShift/Kubernetes. It supports GPU sharing mechanisms including NVIDIA MIG and time-slicing, making it possible to build an internal GPUaaS platform with tenant/project quotas. (docs.redhat.com)
• Nutanix — Worth evaluating for private/sovereign cloud and service-provider environments. Nutanix has announced GPUaaS alongside a multi-tenant service portal, Kubernetes-as-a-Service, and its enterprise AI platform. (nutanix.com)

For your requirement, NVIDIA Run:ai is the strongest starting point. Its differentiator isn't merely access to GPUs: it dynamically schedules scarce GPU capacity among multiple enterprise teams and allows smaller workloads to consume fractional GPUs, potentially improving utilization considerably. NVIDIA AI Enterprise also packages Run:ai alongside vGPU, MIG, GPU operators, and other infrastructure tooling. (docs.nvidia.com)

One caveat: Run:ai's multi-tenant control plane provides logical/access isolation, while underlying Kubernetes cluster, networking, compute, and storage isolation remains an infrastructure responsibility. (run-ai-docs.nvidia.com)

Sources:
nvidia.com
run-ai-docs.nvidia.com
docs.nvidia.com
docs.nvidia.com
nutanix.com