NVIDIA DGX H200 for AI training, inference and HPC needing large VRAM
Deploy GPU Cloud with NVIDIA H200 SXM5 141 GB, AMD Genoa CPUs, 6,000 GB of storage capacity, and options from 1 to 8 GPUs.
H200 GPU Cloud for AI models that need large memory
NVIDIA DGX H200 is designed for AI workloads that need large VRAM, consistent throughput and multi-GPU scaling for training, fine-tuning or large-model inference.
NVIDIA DGX H200
H200 SXM5 141 GB GPU infrastructure for AI training, fine-tuning, inference and HPC workloads that need large VRAM.
H200 SXM5 141 GB
Configurations from 1 to 8 GPUs, with VRAM scaling from 141 GB to 2,048 GB depending on the plan.
AMD Genoa CPU
High-core-count AMD Genoa CPUs accelerate data pipelines, preprocessing and parallel compute workloads.
NVLink/P2P
P2P or 900 GB/s NVLink options improve data exchange between GPUs in multi-GPU workloads.
Choose an H200 configuration based on AI workload
| Service plans | Price/month | GPU | CPU | Core | RAM | VRAM | Actions |
|---|---|---|---|---|---|---|---|
| 1H200.141S.44V | 109,981,800 VND/month | 1 GPU | AMD Genoa | 44 Core | 240 GB RAM | 141 GB VRAM | Sign up |
| 2H200.141S.88V | 205,500,240 VND/month | 2 GPU | AMD Genoa | 88 Core | 370 GB RAM | 282 GB VRAM | Sign up |
| 4H200.141S.176V | 361,584,000 VND/month | 4 GPU | AMD Genoa | 176 Core | 740 GB RAM | 564 GB VRAM | Sign up |
| 8H200.141S.176V | 662,904,000 VND/month | 8 GPU | AMD Genoa | 176 Core | 1450 GB RAM | 2048 GB VRAM | Sign up |
| Service plans | GPU | CPU | Core | RAM | VRAM | NVLink/P2P | Disk | Notes | Price/month | Actions |
|---|---|---|---|---|---|---|---|---|---|---|
| 1H200.141S.44V | 1 GPU | AMD Genoa | 44 Core | 240 GB RAM | 141 GB VRAM | P2P: No | 6000 GB | Visible product • Single GPU AI compute | 109,981,800 VND/month | Sign up |
| 2H200.141S.88V | 2 GPU | AMD Genoa | 88 Core | 370 GB RAM | 282 GB VRAM | NVLink Supported | 6000 GB | Visible product • Multi-GPU ready | 205,500,240 VND/month | Sign up |
| 4H200.141S.176V | 4 GPU | AMD Genoa | 176 Core | 740 GB RAM | 564 GB VRAM | NVLink 900 GB/s | 6000 GB | Visible product • Training scale | 361,584,000 VND/month | Sign up |
| 8H200.141S.176V | 8 GPU | AMD Genoa | 176 Core | 1450 GB RAM | 2048 GB VRAM | NVLink 900 GB/s | 6000 GB | Visible product • Full-node AI compute | 662,904,000 VND/month | Sign up |
Applies to all plans
- H200 SXM5 141 GB
- AMD Genoa
- 6000 GB
Optimized for GenAI, HPC, and enterprise AI platforms
H200 configurations scale GPU capacity across every stage: experimentation, fine-tuning, training, and production inference.
Large VRAM for AI
The H200 SXM5 with 141 GB suits LLMs, multimodal AI, computer vision and large batch inference.
Scale with your workload
Choose 1, 2, 4 or 8 GPUs to balance cost, performance and your project's scaling needs.
Storage 6000 GB
Every H200 configuration comes with 6,000 GB of disk for datasets, model checkpoints and artifacts.
Multiple billing cycles
Available on 1, 3, 6, 12, 24, 36, 48, and 60-month terms.
AI stack support
HiTechCloud advises on drivers, CUDA, runtime, frameworks and cloud GPU deployment configuration.
Production ready
Suitable for training, fine-tuning, RAG, embedding, inference, and enterprise AI platform deployments.
Scenarios suited to the NVIDIA DGX H200
Use DGX H200 for fine-tuning, instruction tuning, model evaluation and enterprise GenAI pipelines.
Serve AI models with 141 GB of VRAM per GPU, suited to batch inference and memory-heavy workloads.
Accelerate simulation, data analysis, rendering, scientific research, and GPU-intensive workloads.
High-performance GPU instances for a wide range of workloads
Powerful capacity, optimized to accelerate AI/ML and high-performance workloads at any scale.
A broad range of infrastructure options
Training, inference, or fine-tuning - HiTechCloud offers a range of GPUs to match your needs, with transparent pricing and on-demand deployment environments.
Built to NVIDIA reference architecture
HiTechCloud GPU instances combine NVLink/PCIe, InfiniBand (RDMA), and RAIL topology to optimize AI/HPC performance.
Three core connectivity layers for high-performance GPU clusters
A network and GPU fabric designed so AI workloads scale reliably, easing bandwidth bottlenecks and holding performance at scale.
NVLink / PCIe Switch
High-speed GPU-to-GPU connectivity within and between nodes, reducing bottlenecks during model training.
InfiniBand (RDMA)
Low-latency connectivity optimized for distributed training and reduced processing load on the host.
RAIL Topology
A parallel network architecture delivering higher bandwidth, redundancy, and consistent performance at any scale.
GPU auto scaling and utilization optimization
From a single GPU to large clusters, HiTechCloud provisions resources ahead of demand and gets the most out of every instance.
Auto scaling with Kubernetes
Automatically scale GPU resources from small scale to thousands of GPUs, with forecasting and Provisioning capability before you need it.
Security for every access connection
Access over SSH, TCP, and HTTP with built-in protection layers that keep enterprise data safe and access fully controlled.
Optimize GPU utilization with MIG
Partition a single GPU into multiple independent instances to run AI workloads in parallel, improving utilization and lowering infrastructure cost.
Transparent pricing, no hidden fees
From large-scale Training to real-time inference — pay only when you actually run, with a GPU Cloud platform built to serve every AI workload and high performance requirement.
Ready to run your first GPU instance?
From register to GPU instance launch in under 5 minutes — no complex setup, no resource reservation, no idle fees. Just deploy, run, and pay for what you use.
More than just Compute, you can manage, scale and develop entirely within a simple cloud ecosystem.
AgentBase
A complete management platform for deploying and operating AI agents securely at scale on enterprise-grade infrastructure.
Explore AgentBaseAI Platform
A unified platform for training, fine-tuning, and deploying AI models at any scale.
Explore AI PlatformVector Database
Supports fast search, real-time analytics, log and large-scale event data, and vector databases for RAG.
Explore Vector DatabaseKubernetes
Managed Kubernetes for container orchestration, AI services, and GPU cloud workloads.
Explore KubernetesScale with confidence across Southeast Asia
Deploy systems and applications closer to your users, reducing latency and meeting local regulatory requirements.
Bangkok
BKK-01
Ho Chi Minh
HCM-01 · HCM-02 · HCM-03
Ha Noi
HAN-01 · HAN-02
A partner for your digital transformation journey
Large enterprises and fast-growing startups choose HiTechCloud for secure, high-performance AI cloud solutions that let them innovate and scale.
Does your enterprise have a specific challenge? HiTechCloud is ready to support.
The HiTechCloud team advises on GPU architecture, networking, security, and an operating model matched to your real workloads.
FAQ
Quick facts before choosing the NVIDIA DGX H200 at HiTechCloud.
Which workloads is the NVIDIA DGX H200 suited to?
Suited to AI training, fine-tuning, inference, HPC, simulation, data science, and workloads that need large VRAM.
How much disk do the H200 plans include?
All plans in the data are configured with 6,000 GB of disk.
How is DGX H200 different from H100?
DGX H200 is the better fit for workloads that need large GPU memory, heavy datasets and AI models with high VRAM requirements.
Is the DGX H200 suitable for LLM inference?
Yes. The DGX H200 suits LLM inference, fine-tuning, model serving, RAG pipelines and memory-intensive AI tasks.
Can I choose the number of H200 GPUs at HiTechCloud?
Yes. HiTechCloud advises on GPU count, CPU, RAM, storage, runtime and framework based on the scale of your workload.
When should you choose DGX H200?
Choose it when your model, batch size or dataset exceeds what general-purpose GPUs can handle and you need a stable data center platform.
What billing cycles are available?
Plans are available on 1, 3, 6, 12, 24, 36, 48, and 60-month terms.
Need a consultation on NVIDIA DGX H200 for your AI workload?
HiTechCloud helps you select the GPU count, VRAM, CPU cores, storage, runtime, and a GPU cloud operating model that fits.




























































































































