Find self-service GPU-as-a-Service solutions for AI workloads.

Here are self-service GPU-as-a-Service (GPUaaS) options suitable for AI workloads, especially if you want on-demand provisioning, tenant isolation, and governed access rather than manually managing GPU infrastructure.

• NorthWind Enterprise GPUaaS Platform — A platform layer for enterprises/providers to deliver self-service GPU access for AI/ML workloads, with APIs or portals, governance, usage tracking, and support for passthrough, NVIDIA MIG, and time-slicing.[2]
• Cisco AI PODs + NorthWind — A validated architecture that turns Cisco AI POD infrastructure into a secure, multitenant, self-service GPU cloud for consumption-driven AI workloads.[1]
• Together Instant GPU Clusters — Lets users self-serve 8/16/32/64-GPU clusters in minutes, with H100s, Kubernetes or Slurm, and flexible 3–90 day rentals.[7]
• Quali Torque GPUaaS — Offers governed GPU environments that can be provisioned in minutes, reserved in advance, scaled dynamically, and decommissioned automatically when jobs finish.[5]
• Northflank GPU PaaS — Lets you select a GPU type, deploy a container, and start training or serving models, making it a simpler self-service path for GPU-backed apps.[9]
• Mirantis GPU Platform as a Service — Focuses on self-service GPU provisioning with browsing, pricing visibility, instant provisioning, and consumption tracking through a portal.[10]

If you want the most directly relevant choices for enterprise AI infrastructure, start with NorthWind, Cisco AI PODs + NorthWind, and Quali Torque.[1][2][5] If you want fast, temporary cluster access for model training, Together Instant GPU Clusters is the clearest fit.[7]

Source URLs:

• cisco.com
• northwind.co
• quali.com
• together.ai
• northflank.com
• mirantis.com