GPU infrastructure · Singapore
From spec and assembly to IaaS, PaaS and live model deployment. We deliver and operate a working private AI stack, not a pallet of loose cards.
# spec'd, racked, burned-in — 8× H200 node $ st provision --gpus 8xH200 --fabric nvlink ✓ driver 560 · CUDA 12.6 · fabric up ✓ k8s + gpu-operator · slurm queue ready # serve a private model, hosted in SG $ vllm serve Qwen2.5-72B \ --tensor-parallel-size 8 --port 8000 ✓ loaded in 41s · 128k ctx ✓ endpoint live · 2,140 tok/s # handed over, monitored, on call $ st status → healthy · 99.9% · region=sg $
What we do
Most vendors sell you one layer and leave the rest to you. We take a requirement and hand back a running system.
GPU server and cluster design, component selection, assembly, burn-in and rack-ready delivery, sized to your workload and budget.
Provisioning, networking, storage and multi-tenant isolation on bare metal, VMs or Kubernetes, tuned for GPU fabric.
Scheduling and serving platforms: Slurm or K8s + GPU Operator, inference with vLLM / Triton / TGI, and MLOps pipelines.
Open or custom models brought online: fine-tuning, serving, RAG and private LLMs, benchmarked for your latency and throughput.
Monitoring, scaling, patching and SLA-backed support once it's live, so your team ships AI instead of babysitting infrastructure.
Solution architecture
Each layer ships on its own or as the whole stack. Scheduling and lifecycle are automated with the GPU Operator, following NVIDIA's cloud-native reference design.
Deploy once and the whole GPU stack installs, reconciles and self-heals. Drivers, container runtime and telemetry are managed as one, per NVIDIA's official cloud-native design.
Who it's for
Dedicated capacity without a public-cloud bill that scales with every token.
On-prem clusters with shared scheduling and sane multi-user access.
Finance, health and public-sector work where data must stay in-country.
You bought the cards but can't stand up the platform. We make them run.
Mid-size firms moving an internal assistant off third-party APIs.
White-label build and deployment capacity behind your own delivery.
Why ScaleTensor
END TO END
One accountable team across hardware, platform and models, with no handoff gaps between a server vendor, a cloud and an ML contractor.
IN-COUNTRY
Private deployments that keep your data and models on infrastructure you control, inside Singapore.
COMPLIANT
Hardware sourced through traceable supply channels, with end-user and end-use checks and full record-keeping, built to pass due diligence.
Hardware we deploy
How we engage
Project
Build & deploy
Managed
Monthly retainer
Supply
Hardware + services
Get in touch
Fill in a short project brief and we'll reply within 1–2 business days with a spec, an approach and a quote. Or email us directly.
hello@scaletensor.fun