Runpod Clusters Expansion: Scale your Clusters without recreating them

Runpod Cluster Expansion allows users to add pods to an existing multi-node GPU cluster without recreating it, preserving the same GPU type, pod template, network storage, and private network while increasing available GPUs, VRAM, and compute capacity. The feature is intended for changing distributed training or inference requirements, such as larger models, batch sizes, or Slurm demand, although it is unavailable for reserved, contracted, or private-pool hardware through the self-service flow. Users can scale from the Runpod console, review the resulting resources and hourly cost, and provision additional pods subject to GPU inventory availability. New pods automatically join the cluster network and inherit its configuration, but most distributed frameworks, including PyTorch DDP and DeepSpeed, require jobs to be restarted or resubmitted because world size and topology are typically fixed at startup. Networking can be verified through hostname connectivity, while Runpod preconfigures NCCL settings for inter-node GPU communication; operationally, users should scale between runs unless using elastic training and can downsize by terminating selected pods.

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