Rent a cloud GPU. Any model. Under 30 seconds.

GPU Pods are dedicated GPU instances you can spin up on Runpod. Unlike abstracted serverless GPUs, Pods give you full control over the underlying VM, drivers, and environment. You get a persistent instance (or ephemeral, if you prefer) with direct access to powerful GPUs, letting you run training, inference, or other workloads exactly how you want.

We offer 30+ GPU models, from entry-level inference cards to top-tier training accelerators. Examples include A100, H100, RTX 6000 Ada, L4/L40 series, and many more—over 30 options in total. You can pick any supported GPU when you launch a Pod, and new models roll out as soon as they’re live on the platform. For the latest availability, check the dashboard or query the API.

Pricing is shown as an hourly rate but billed by the millisecond. You only pay for the exact time your Pod runs—if you start and stop a Pod in one minute, you’re charged just that minute. Storage volumes may incur minimal fees when attached, but compute costs are metered by the millisecond.

Yes. GPU Pods support custom Docker images. You can build an image with your preferred libraries and push it to a registry (Docker Hub, ECR, etc.), then reference it when you launch the Pod. That way you control the OS, drivers, and dependencies.

Any framework that runs on Linux and supports GPUs: PyTorch, TensorFlow, JAX, ONNX, CUDA toolkits, etc. Since you control the container, you can install whatever versions or additional tools you need (e.g., NCCL, Horovod). We provide base images with common ML stacks to speed up setup.

We offer spot instances where GPU capacity is available at a discount, but with the risk of eviction when demand spikes. You can use them for fault-tolerant or batch workloads. The UI/API will indicate current spot availability and pricing.

Sign up, go to [Pods → Deploy], select your GPU model (H100, A100, RTX 4090, and 30+ others), choose a region, attach a container image, and click Deploy. Your GPU instance is live in under 30 seconds. Billing starts the moment it's running and stops the moment you terminate it — billed by the second, never by the hour.

RTX A5000s start at $0.27/hr and L4s at $0.39/hr — both solid choices for inference, testing, and development. If you need more VRAM, A40s are $0.44/hr and RTX 3090s are $0.46/hr. All GPU rentals are billed by the second, so a short job costs a fraction of an hour's rate.

Yes. H100 PCIe instances are available on-demand from $2.89/hr and A100 PCIe from $1.39/hr, with no contracts or minimums. If you need guaranteed capacity at lower rates, ask about our reserved pricing. Either way, you're billed by the second — not by the hour or day.