Dedicated GPU servers
Maximum performance and isolation for LLM training, HPC simulation, large-scale analytics, and deterministic production workloads.
On-demand NVIDIA GPUs in Indonesia
Access H100 and L40S through bare metal, passthrough, vGPU, or Kubernetes — sized for training, inference, RAG, VDI, rendering, and HPC, with Indonesian data residency.
Four ways to consume GPU capacity
Choose the isolation, density, and operations model that matches the workload — without buying and running scarce GPU hardware yourself.
Near-native GPU in a VM
Physical GPUs assigned directly to virtual machines for teams that need VM lifecycle control with dedicated GPU performance.
Efficient sharing
GPU profiles for VDI, rendering, development, visualization, and moderate inference workloads.
Cloud-native AI platform
GPU scheduling, namespaces, RBAC, NVIDIA GPU Operator, autoscaling inference, and MLOps integration.
NVIDIA GPUs sized for the job, not a generic SKU list
| GPU | Architecture | Memory | Strength | Recommended workloads |
|---|---|---|---|---|
| NVIDIA H100 SXM | Hopper | 80 GB HBM3 | Flagship AI training and high-bandwidth compute | LLM training, fine-tuning, HPC, large-scale analytics, production inference |
| NVIDIA L40S | Ada Lovelace | 48 GB GDDR6 ECC | Universal AI, rendering, visualization, and VDI GPU | Inference, GPU VDI, professional graphics, rendering, development |
| NVIDIA GB200 | Blackwell with Grace CPU | Up to 384 GB HBM3e | Next-generation hyperscale generative AI platform | National AI platforms, frontier-scale LLM training, multimodal AI, AI supercomputing |
Indonesia-hosted, security-first GPU infrastructure
AI Sovereign Cloud
Keep data, models, prompts, embeddings, and workloads inside Indonesian infrastructure and governance boundaries.
NVIDIA Cloud Partner ecosystem
Use NVIDIA GPU technology, CUDA, CUDA-X, NVIDIA AI Enterprise, Triton, vLLM, NeMo, RAPIDS, and GPU Operator patterns.
Security by design
Support encryption, IAM, RBAC, audit logging, tenant isolation, API security, and regulated enterprise deployment models.
Frequently asked questions
Which GPUaaS model should we choose?
Use bare metal for maximum isolation and performance, passthrough VMs for VM lifecycle control, vGPU for shared visual or VDI workloads, and Kubernetes GPUaaS for cloud-native AI platforms and model serving.
Can GPUaaS support sensitive or regulated workloads?
Yes, subject to final architecture. Data flows, administrator access, network boundaries, encryption, logging, backup, identity, and support responsibilities should be documented before production.
Is the calculator a final quotation?
No. The calculator is an indicative planning tool. Final pricing depends on capacity, contract duration, support model, availability, service scope, and commercial approval.