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

Here are self-service GPU-as-a-Service (GPUaaS) options for AI workloads, with a focus on platforms that let teams provision GPUs through a portal or API instead of manually managing infrastructure.

• NorthWind Enterprise GPUaaS Platform — A strong enterprise option for self-service, multitenant GPU access with governance, isolation, and usage tracking for AI/ML workloads.[2] Cisco also describes a Cisco AI PODs + NorthWind setup as a secure, self-service, consumption-driven AI cloud.[1]
• Quali Torque GPUaaS — Provides governed GPU environments that can be provisioned in minutes and decommissioned automatically after the job completes.[5]
• Northflank GPU PaaS — Lets users select a GPU type, deploy a container, and start training or serving models, which is useful for teams wanting a simpler self-service developer workflow.[9]
• Mirantis GPU Platform as a Service — Offers on-demand GPU access and lifecycle management plus a portal-based self-service model for tenants.[10]
• Red Hat GPU-as-a-Service — Positions GPUaaS as a way for ITOps to provide on-demand GPU access for rapid AI development and inference.[8]
• Spectro Cloud + MIG-based GPU slicing — Focuses on secure self-service AI infrastructure with GPU slicing so tenants get the right GPU resources on demand.[7]

If you want the most “productized” enterprise self-service offerings, NorthWind and Quali Torque are the clearest matches from the results.[2][5] If you want a more developer-friendly container platform with GPU support, Northflank is worth a look.[9]

Useful source URLs:

• northwind.co
• quali.com
• northflank.com
• mirantis.com
• redhat.com