AI infrastructure

Compute, ready when you are

GPU capacity, deployment and day-two operations for teams training and serving models in Southeast Asia.

Compute / GPU infrastructure

From a single GPU server to a working cluster

Compute is a supply chain problem, a facilities problem and an operations problem at once. We take all three.

GPU server and cluster sourcing — specified against your models and workloads, not a vendor catalogue
On-prem, colocation or cloud, compared honestly on cost per GPU-hour, control and time to first job
Data-center readiness: power budget, rack density, cooling and floor loading assessed before anything is ordered
Managed operations — provisioning, scheduling, monitoring and hardware replacement once it is live

Capabilities

The work between signing a purchase order and running a training job — handled by one team.

GPU sourcing and supply
Hardware shortlisting, quotations and lead-time management across GPU servers, networking and storage.
Deployment models
On-prem, colocation or cloud — sized and costed side by side so the decision is made on numbers.
Data-center readiness
Power, cooling, rack density and floor loading reviewed against the kit before it ships.
Cluster networking and storage
High-throughput interconnect and storage sized to keep expensive GPUs fed rather than waiting.
Managed operations
Monitoring, scheduling, patching and hardware replacement, with a response path when a node drops.
Capacity planning
A roadmap from pilot capacity to production scale, so the next expansion is not a rebuild.

Tell us the models, the workloads and the site. We will come back with a configuration, a deployment option and a realistic lead time.