Training at scale
Distributed training jobs that need high-bandwidth, low-latency links between nodes to keep accelerators utilised.
GPU Clusters
GPU Clusters Built for Large-Scale AI
Multi-node GPU clusters for training and large-scale inference — connected so the fabric, storage and schedulers keep the whole system moving.
Overview
What is an AI GPU cluster?
An AI GPU cluster is a multi-node GPU environment designed to train and serve models that no longer fit on a single server. The value is not only more GPUs — it is the interconnect, storage and orchestration that allow those GPUs to work as one system.
Dimension AI builds GPU clusters inside AI Factories, including high-speed InfiniBand and RoCE fabric, parallel storage and schedulers such as Kubernetes and SLURM.
Capabilities
Built for cluster-scale work
Large-scale inference
Serving large models or high-concurrency inference where capacity must span more than one GPU server.
HPC-style coupling
Workloads that behave like tightly coupled systems, not a collection of unrelated virtual machines.
Capabilities
What makes the cluster usable
Fabric
InfiniBand and RoCE connectivity so gradient exchange, parameter updates and inference traffic do not stall on a slow network.
Storage
Parallel filesystems and high-performance storage for datasets, checkpoints and model artefacts at cluster scale.
Orchestration
Scheduling, isolation and observability so multiple jobs can share a cluster without losing operational control.
Related proof
Programmes and locations
FAQ
Questions teams ask
Do I need a GPU cluster or Bare Metal GPU?
Choose a cluster when the job must span multiple nodes. Choose Bare Metal GPU when a dedicated physical server — or a small set of dedicated servers — is enough and exclusive control is the priority.
Can cluster capacity be consumed as inference?
Yes. Cluster infrastructure can run training and can also back Inference as a Service and model endpoints when you want to consume serving rather than operate it.
