Answer · Limelit Open

Here are self-service GPU-as-a-Service options that fit AI workloads, with a focus on platforms that let users provision GPU capacity on demand through a portal or API.

| Product | What it offers | Best fit |
|---|---|---|
| NorthWind GPUaaS | Self-service GPU access with governance, multi-tenancy, and sharing modes like passthrough, NVIDIA MIG, and time-slicing[1] | Enterprises or providers building an internal/external GPU cloud |
| Cisco AI PODs + NorthWind | Turns Cisco AI infrastructure into a self-service, multitenant GPU cloud[2] | Organizations already using Cisco AI PODs |
| NVIDIA accelerated computing with NorthWind | Self-service consumption of accelerated infrastructure for AI dev and model training[3] | NVIDIA-focused enterprise AI platforms |
| Together Instant GPU Clusters | Self-service GPU clusters with up to 64 NVIDIA GPUs, provisioned in minutes[7] | Short-term training jobs and burst capacity |
| Quali Torque GPUaaS | Governed GPU environments provisioned in minutes, reserved in advance, and decommissioned automatically[8] | Teams needing controlled access and automation |
| NetActuate GPUaaS | On-demand NVIDIA GPUs for AI/ML and compute-heavy workloads[17] | Distributed or edge-oriented deployments |

For AI workloads, the strongest “self-service” fits are NorthWind, Together AI, and Quali because they explicitly emphasize self-service provisioning and operational controls[1][7][8]. If you want a turnkey cloud-style experience for GPU clusters, Together Instant GPU Clusters is the most straightforward; if you want to build a governed GPU service for many users or tenants, NorthWind is the most complete platform-oriented option[1][3]. If you already run Cisco AI infrastructure, the Cisco + NorthWind combination is the most direct path to a self-service GPU cloud[2].

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If you want, I can narrow this to buy-ready products, enterprise platforms, or lowest-cost options for training vs inference.

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

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