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

Here's a breakdown of the leading self-service GPU-as-a-Service options, split into "raw/instant GPU rental" platforms and "GPUaaS platform software" for enterprises building internal clouds:

Instant, self-service GPU rental (no waitlist, pay-as-you-go)

• RunPod –

self-serve instant GPU clusters with Slurm/K8s and expert support, also provides managed fine-tuning/inference APIs
, splitting pricing across Pods and Serverless for warm vs. event-driven inference. (runpod.io)

• Lambda Labs –

offers on-demand B200s at $3.49/GPU/hr with SSH access and pre-installed ML frameworks
; strong for reserved multi-node training clusters with InfiniBand.

• GMI Cloud –

offers on-demand NVIDIA H200 and H100 GPUs for high-performance, scalable AI training and inference at low cost
, with a
purpose-built AI/ML Ops "Cluster Engine" offering container-as-a-service, bare-metal, and managed Kubernetes/Slurm
. (gmicloud.ai)

• CoreWeave –

GPU-native Kubernetes infrastructure with reserved pricing for sustained workloads
, purpose-built for large-scale training and enterprise inference.

• Vast.ai –

behaves like a live marketplace, with the upside being lower rates, though pricing and availability move with supply and demand
. (blog.runc.ai)

Enterprise GPUaaS platform software (turn your own/cloud GPUs into a self-service internal cloud)

• NorthWind GPU PaaS –

delivers on-demand access to GPU compute through APIs or self-service portals, similar to how cloud platforms deliver CPU-based infrastructure, enabling on-demand, self-service access to GPU resources
. (northwind.co)

• NVIDIA Run:ai –

cemented GPU orchestration as a critical infrastructure layer, creating virtual GPU pools enabling dynamic, policy-based scheduling across Kubernetes clusters
, with fractional GPU sharing that
increases effective cluster capacity by 2-3x for mixed workloads
. (introl.com)

• Red Hat OpenShift AI –

a Kubernetes-based platform that can be used to implement a multi-user GPUaaS solution, achieving true GPUaaS through dynamic allocation based on workload
. (redhat.com)

Recommendation: For quick self-service compute without owning hardware, start with RunPod or Lambda Labs. For enterprises with existing GPU investments needing an internal self-service portal, NorthWind or Run:ai on Kubernetes are the leading choices.