Runpod

What it is

Quick answers

What GPU infrastructure does Runpod offer for AI workloads?

Runpod offers three primary infrastructure products: Serverless (autoscaling GPU endpoints that scale to zero when idle), Pods (GPU instances for persistent compute and development – available as Reserved or Spot), and Clusters (multi-GPU distributed compute for training and large-batch inference). All run on the same GPU catalog including H100 80GB HBM3, H100 NVL, A100, L40S, and more, accessible on-demand with no contracts or minimum commitments.

Is Runpod suitable for production AI infrastructure?

Yes. Runpod is SOC 2 Type II certified and HIPAA and GDPR compliant, with SOC 2 reports, BAAs, and DPAs available for security review. Secure Cloud adds network isolation for workloads with stricter compliance needs. Enterprise agreements add a 99.99% uptime SLA, dedicated capacity, and tailored terms.

What is the difference between a GPU pod and a Cluster?

A GPU pod is a single instance with one or more GPUs within the same node. A Cluster consists of multiple nodes interconnected with high-speed networking, allowing for workloads that span across multiple machines. Clusters are ideal for large model inference and distributed training that exceeds the capacity of a single node.

Are there minimum lease terms or contract requirements for Clusters?

No. For Clusters, there are no minimum lease terms. You have complete flexibility to deploy and terminate clusters as needed to support your workloads, with no long-term commitments or contract obligations.