Bare Metal GPU
Dedicated physical GPU servers with full control for workloads that cannot share hardware or that need predictable, exclusive performance.
GPU as a Service
Enterprise GPU as a Service
Provision the level of control and scale your workload requires — from dedicated physical GPU servers to multi-node clusters — inside Dimension AI Factory environments.
Overview
What is GPU as a Service?
GPU as a Service (GPUaaS) gives organisations access to accelerated compute without having to finance, deploy and operate every layer of a GPU platform themselves. Dimension AI delivers GPUaaS as part of its AI Factory model, so capacity is paired with fabric, storage, orchestration and operations.
Workloads differ. Some need exclusive physical servers. Others need fractional or virtualised access. Large training and inference jobs need multi-node clusters. Dimension AI offers all three consumption models rather than forcing one shape of GPU onto every use case.
Capabilities
Three ways to consume GPU infrastructure
GPU Instances
Virtualised or fractional GPU access for flexible workloads, development cycles and capacity that should scale with demand.
GPU Clusters
Multi-node GPU clusters for training and large-scale inference, connected over high-speed fabric so the cluster behaves as one system.
Capabilities
Workloads
Training
Keep accelerators utilised with cluster-scale compute, interconnect and storage designed for long-running training jobs.
Fine-tuning
Run customisation jobs on dedicated or flexible GPU capacity without building a separate stack for each model iteration.
Inference
Serve models from GPU infrastructure that can also be exposed as managed inference APIs and model endpoints.
Capabilities
Availability, control and regions
Enterprise control
GPUaaS sits inside Dimension AI Factory operations — monitoring, capacity planning and governance — rather than as unmanaged raw servers.
Regional capacity
GPU infrastructure is delivered from Dimension AI environments in Singapore, Indonesia and Thailand, aligned to where customers need to operate.
Path to a private factory
Start with GPU consumption and expand into a private or sovereign AI Factory when isolation, residency or dedicated operations become the requirement.
FAQ
Questions teams ask
How is Dimension AI GPUaaS different from renting GPUs?
Capacity is delivered inside an AI Factory architecture: compute plus fabric, storage, orchestration and managed operations. That is what makes the GPU usable for production training and inference, not only for short experiments.
Should I choose Bare Metal GPU, instances or clusters?
Choose Bare Metal GPU for exclusive physical control, GPU instances for flexible or fractional access, and GPU clusters when training or inference needs multiple nodes working as one system.
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