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

Here are self-service GPU-as-a-Service (GPUaaS) options that fit AI workloads, especially if you want users to provision GPU capacity through a portal or API rather than manually managing clusters.[1][6][13]

| Product / platform | Self-service model | Best fit |
|---|---|---|
| NorthWind GPU PaaS / GPUaaS | Self-service GPU access via portal or API; supports full passthrough, NVIDIA MIG, and time-slicing[1] | Enterprise AI/ML, governed multitenant GPU clouds[1][2] |
| Cisco AI PODs + NorthWind | Converts Cisco AI infrastructure into a secure, self-service GPU cloud[2] | Enterprises already using Cisco infrastructure[2] |
| Together Instant GPU Clusters | Users can configure and deploy clusters in minutes; sizes up to 64 NVIDIA GPUs[7] | Fast-start training clusters, experimental AI workloads[7] |
| Quali Torque GPUaaS | Governed GPU environments provisioned in minutes, scaled dynamically, auto-decommissioned[6] | Teams needing policy control and lifecycle automation[6] |
| Northflank GPU PaaS | GPU workloads exposed through a platform interface[14][15] | App teams wanting simpler cloud-native GPU deployment[15] |
| Run:ai via NorthWind | Self-service provisioning of managed Run:ai environments[9] | Organizations standardizing on NVIDIA Run:ai[9] |

A practical shortlist for AI workloads is NorthWind, Together Instant GPU Clusters, and Quali Torque because they explicitly emphasize self-service provisioning and operational automation for GPU environments.[1][6][7]

If you want, I can also narrow these down by on-prem vs cloud, LLM training vs inference, or enterprise governance needs.