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GPU Cloud provides dedicated compute infrastructure for machine learning workloads. Use GPU clusters to train models, run inference, and process large-scale AI tasks.

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GPU clusters

A GPU cluster is a group of interconnected servers, each equipped with multiple high-performance GPUs. Clusters run workloads that require massive parallel processing: training large language models (LLMs), fine-tuning foundation models, running inference at scale, and high-performance computing (HPC).
GPU Cloud create cluster page showing region selection, cluster type, and GPU configuration options
All nodes in a cluster share the same configuration: operating system image, network settings, and storage mounts. This ensures consistent behavior across the cluster.

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Cluster types

Gcore offers three types of GPU clusters: Clusters can scale to hundreds of nodes. Production deployments with 250+ nodes in a single cluster are supported, limited only by regional stock availability.

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Available configurations

Select a configuration based on workload requirements: Outbound data transfer (egress) from GPU clusters is free. Other costs are covered in GPU Cloud billing. Configurations with InfiniBand are configured automatically at cluster creation — all nodes join the same InfiniBand domain with no manual setup required.

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Storage options

GPU clusters support two storage types:

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Cluster lifecycle

  1. Create: Select region, GPU type, number of nodes, image, and network settings. Create a Bare Metal GPU cluster or a Virtual GPU cluster.
  2. Configure: Connect via SSH to each node, install required dependencies, and mount file shares to prepare the environment for workloads.
  3. Run workloads: Execute training jobs, run inference services, process data.
  4. Resize: Add or remove nodes on demand — new nodes inherit the cluster configuration.
  5. Delete: Remove the cluster when no longer needed. Local storage is erased; file shares and network disks can be preserved.

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GPU cluster characteristics

  • Provisioning takes 15–40 minutes
  • The configuration (image, network, and storage) is fixed at creation and cannot be changed afterward
  • Local NVMe storage is temporary — store critical data in persistent file shares
  • Spot clusters can be interrupted with a 24-hour notice
  • Available regional capacity determines cluster size
  • Servers equipped with BlueField network cards support hardware firewalls
  • Use a single GPU server for development or smaller workloads; use a cluster for large models, distributed training, or high-scale inference
After connecting to a node via SSH, run nvidia-smi to confirm GPU availability and ibstat to verify InfiniBand connectivity.