Three ways to consume compute.

Multi-node training, minus the operations tax.

Dedicated clusters on a rail-optimized, non-blocking fabric — provisioned, burned in, and health-checked by our engineers before your first epoch. Sized in nodes, delivered as a working system.

Rail-optimized, non-blocking interconnect sized so collective operations never become the bottleneck.

Every node and every link passes stress and thermal validation before it counts toward your cluster.

Slurm or Kubernetes, tuned by us — long runs survive maintenance windows and node swaps.

Parallel file systems on a flash tier, sized to keep every accelerator fed at full stride.

Single-tenant compute and fabric partitions, private networking, and dedicated interconnect options.

A team that knows your topology and your training job — on call 24/7, in your timezone.

Serve Asia at millisecond latency.

Autoscaling endpoints on capacity tuned for serving, in the four metros that matter. p50 and p99 measured at the regional edge — and held under real traffic, not lab conditions.

Set replica bounds per endpoint; every scale event is logged and auditable in the console.

Serve from the metro your users are in — and keep data in-region where residency requires it.

Route a slice of traffic to a new model revision before you commit the fleet to it.

Retrain the moment the data lands.

Elastic headroom for refreshing models on new campaign data. Reserve a window in advance or spin up in hours — pay for the burst, hand it back when the run completes.

Book capacity against a campaign calendar — it's waiting when the data drops.

Burst allocations provision from standing headroom — no procurement cycle in the loop.

When the run completes, capacity releases and consumption stops — visible in the console.