AI compute, operated by us.

GPU clusters in Singapore, Malaysia, Indonesia and Taiwan — built by our engineers, and run by the same ones.

Talk to our team
A long row of GPU server cabinets in service in a bright white data hall, doors closed and status lights running the length of the row.

The build you don't have to do

You don't have to build it to run on it.

Standing up a GPU cluster means racks, power, fabric, cooling — and someone watching it at three in the morning. Take the capacity instead of the project, and skip the chain of rack integrators, cabling contractors and monitoring vendors where each one owns a slice and nobody owns the outcome.

Backed by contract

Someone is watching it at 3am.

Capacity comes with a contractual SLA, not best effort: one hour to respond on P1, four hours on P2, next business day on P3. Behind that sit a seven-stage incident process and end-to-end RMA coordination, run by local L1 and L2 engineers stationed in Indonesia, Singapore and Malaysia — not a ticket queue in another timezone.

See how we operate
A bright data centre operations room at night, an operator seen from behind at a desk of monitoring screens.

What runs on it

Training, tuning, inference — and the jobs in between.

  • Multi-node

    Large-model training

    Multi-node runs across an InfiniBand or RoCEv2 fabric. The interconnect is sized before the first rack ships, because it is the part you cannot retrofit.

  • Rack-scale

    Frontier-scale clusters

    Rack-scale systems, deployed in-region. The first 64-node rack-scale cluster in Asia-Pacific was ours, delivered for a Tier-1 data centre operator in 2025.

  • Per node

    Fine-tuning and evaluation

    Dedicated nodes for teams that need a whole GPU rather than a shared slice — and predictable numbers when they benchmark it twice.

  • In-market

    Production inference

    Capacity in the market your users are already in, so the round trip stays short and the data does not leave the region to get answered.

  • Kubernetes

    Containerised pipelines

    Bring your own images, schedulers and CI. You get a namespace and role-based access; we keep the nodes, drivers and fabric underneath healthy.

  • Named facility

    Workloads that have to stay put

    Pinned to a named facility in a named market, with local engineers holding the keys. Day 2 operations are live in Indonesia, Singapore and Malaysia.

Notes from the field

NEWS

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What you can take

4 cloud services. One operator underneath.

A lit aisle of GPU compute racks in a white data hall, cabinet doors closed and status lights running the length of the row.

Compute

AI Compute

Dedicated GPU nodes on infrastructure we operate.

Explore compute
A monitoring room with a wall of dashboards — time-series charts and gauges across three large displays above an operator desk.

Observability

Observability & Monitoring

RCS watches every layer, from GPU to facility power.

Explore monitoring

In numbers

We did not buy this capacity. We built it.

Scale

1,700+

GPU servers deployed across multiple platform generations — by our own engineers, not bought as finished capacity from someone else.

Coverage

5

Singapore, Malaysia, Indonesia and Taiwan, with the United States expanding.

Operations

24×7

Day 2 operations, with resident on-site teams in Singapore, Malaysia and Indonesia.

SLA

1 hr

One hour to respond on P1, four hours on P2, next business day on P3 — written into the contract.