> ## Documentation Index
> Fetch the complete documentation index at: https://docs.gcore.com/llms.txt
> Use this file to discover all available pages before exploring further.

# About GPU Cloud

> GPU Cloud offers dedicated GPU clusters for AI training, inference, and HPC with bare metal, spot, and virtual configurations.

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

## 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](https://mintcdn.com/gcore/W9hLxFUXyZVjy-WH/images/docs/edge-ai/ai-infrastructure/about-gpu-cloud/create-cluster-page.png?fit=max\&auto=format\&n=W9hLxFUXyZVjy-WH\&q=85\&s=a8c30ef2f07213f15e905ce5742c4858)

All nodes in a cluster share the same configuration: operating system image, network settings, and storage mounts. This ensures consistent behavior across the cluster.

## Cluster types

Gcore offers three types of GPU clusters:

| Type | Description | Best for |
| - | - | - |
| Bare Metal GPU | Dedicated physical servers with guaranteed resources. No virtualization overhead | Production workloads, long-running training jobs, and latency-sensitive inference |
| Spot Bare Metal GPU | Same hardware as Bare Metal, but at a reduced price (up to 50% discount). Instances can be preempted with a 24-hour notice when capacity is needed | Fault-tolerant training with checkpointing, batch processing, development, and testing |
| Virtual GPU | Virtualized GPU instances with flexible resource management. Supports flavor changes and cost optimization through shelving (powering off releases resources and stops billing) | Development environments, variable workloads, cost-sensitive projects |

Clusters can scale to hundreds of nodes. Production deployments with 250+ nodes in a single cluster are supported, limited only by regional stock availability.

## Available configurations

Select a configuration based on workload requirements:

| Configuration | GPUs | Interconnect | RAM | Storage | Use case |
| - | - | - | - | - | - |
| H200 with InfiniBand | 8x NVIDIA H200 141GB | 3.2 Tbit/s InfiniBand, 2x 200 Gbit/s Ethernet | 2TB | 6x 3.84TB NVMe | Distributed LLM training with the latest GPU generation |
| H100 with InfiniBand | 8x NVIDIA H100 80GB | 3.2 Tbit/s InfiniBand | 2TB | up to 8x 3.84TB NVMe | Distributed LLM training requiring high-speed inter-node communication |
| A100 with InfiniBand | 8x NVIDIA A100 80GB | 800 Gbit/s InfiniBand | 2TB | 8x 3.84TB NVMe | Multi-node ML training and HPC workloads |
| A100 without InfiniBand | 8x NVIDIA A100 80GB | 2x 100 Gbit/s Ethernet | 2TB | 8x 3.84TB NVMe | Single-node training, inference for large models requiring more than 48GB VRAM |
| L40S | 8x NVIDIA L40S | 2x 25 Gbit/s Ethernet | 2TB | 4x 7.68TB NVMe | Inference, fine-tuning small to medium models requiring less than 48GB VRAM |

Outbound data transfer (egress) from GPU clusters is free. Other costs are covered in [GPU Cloud billing](/edge-ai/billing).

Configurations with [InfiniBand](/edge-ai/networking/about-infiniband) are configured automatically at cluster creation — all nodes join the same InfiniBand domain with no manual setup required.

## Storage options

GPU clusters support two storage types:

| Storage type | Persistence | Performance | Use case |
| - | - | - | - |
| Local NVMe | Temporary (deleted with cluster) | Highest IOPS, lowest latency | Training data cache, checkpoints during training |
| File shares | Persistent (independent of cluster) | Network-attached, lower latency than object storage | Datasets, model weights, shared checkpoints |

## Cluster lifecycle

1. **Create**: Select region, GPU type, number of nodes, image, and network settings. Create a [Bare Metal GPU](/edge-ai/ai-infrastructure/create-a-bare-metal-gpu-cluster) cluster or a [Virtual GPU](/edge-ai/ai-infrastructure/create-a-virtual-gpu-cluster) 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.

## 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.