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.
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.
H200 SXM5 141 GB GPU infrastructure for AI training, fine-tuning, inference and HPC workloads that need large VRAM.
Configurations from 1 to 8 GPUs, with VRAM scaling from 141 GB to 2,048 GB depending on the plan.
High-core-count AMD Genoa CPUs accelerate data pipelines, preprocessing and parallel compute workloads.
P2P or 900 GB/s NVLink options improve data exchange between GPUs in multi-GPU workloads.
H200 SXM5 141 GB
H200 SXM5 141 GB
H200 SXM5 141 GB
H200 SXM5 141 GB
H200 configurations scale GPU capacity across every stage: experimentation, fine-tuning, training, and production inference.
The H200 SXM5 with 141 GB suits LLMs, multimodal AI, computer vision and large batch inference.
Choose 1, 2, 4 or 8 GPUs to balance cost, performance and your project's scaling needs.
Every H200 configuration comes with 6,000 GB of disk for datasets, model checkpoints and artifacts.
Available on 1, 3, 6, 12, 24, 36, 48, and 60-month terms.
HiTechCloud advises on drivers, CUDA, runtime, frameworks and cloud GPU deployment configuration.
Suitable for training, fine-tuning, RAG, embedding, inference, and enterprise AI platform deployments.
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.
Powerful capacity, optimized to accelerate AI/ML and high-performance workloads at any scale.
Training, inference, or fine-tuning - HiTechCloud offers a range of GPUs to match your needs, with transparent pricing and on-demand deployment environments.
HiTechCloud GPU instances combine NVLink/PCIe, InfiniBand (RDMA), and RAIL topology to optimize AI/HPC performance.
A network and GPU fabric designed so AI workloads scale reliably, easing bandwidth bottlenecks and holding performance at scale.
High-speed GPU-to-GPU connectivity within and between nodes, reducing bottlenecks during model training.
Low-latency connectivity optimized for distributed training and reduced processing load on the host.
A parallel network architecture delivering higher bandwidth, redundancy, and consistent performance at any scale.
From a single GPU to large clusters, HiTechCloud provisions resources ahead of demand and gets the most out of every instance.
Scale GPU resources automatically from a handful to thousands of GPUs, with forecasting and provisioning ahead of demand.
Access over SSH, TCP, and HTTP with built-in protection layers that keep enterprise data safe and access fully controlled.
Partition a single GPU into multiple independent instances to run AI workloads in parallel, improving utilization and lowering infrastructure cost.
From large-scale training to real-time inference - pay only when you actually run, on a GPU cloud platform built for every AI and high-performance workload.
From sign-up to a running GPU instance in under 5 minutes - no complex setup, no resource reservations, no charges while idle. Just deploy, run, and pay only for what you use.
A complete management platform for deploying and operating AI agents securely at scale on enterprise-grade infrastructure.
Explore AgentBaseA unified platform for training, fine-tuning, and deploying AI models at any scale.
Explore AI PlatformSupports fast search, real-time analytics, log and large-scale event data, and vector databases for RAG.
Explore Vector DatabaseManaged Kubernetes for container orchestration, AI services, and GPU cloud workloads.
Explore KubernetesDeploy systems and applications closer to your users, reducing latency and meeting local regulatory requirements.
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Large enterprises and fast-growing startups choose HiTechCloud for secure, high-performance AI cloud solutions that let them innovate and scale.
The HiTechCloud team advises on GPU architecture, networking, security, and an operating model matched to your real workloads.
Quick facts before choosing the NVIDIA DGX H200 at HiTechCloud.
Suited to AI training, fine-tuning, inference, HPC, simulation, data science, and workloads that need large VRAM.
All plans in the data are configured with 6,000 GB of disk.
DGX H200 is the better fit for workloads that need large GPU memory, heavy datasets and AI models with high VRAM requirements.
Yes. The DGX H200 suits LLM inference, fine-tuning, model serving, RAG pipelines and memory-intensive AI tasks.
Yes. HiTechCloud advises on GPU count, CPU, RAM, storage, runtime and framework based on the scale of your workload.
Choose it when your model, batch size or dataset exceeds what general-purpose GPUs can handle and you need a stable data center platform.
Plans are available on 1, 3, 6, 12, 24, 36, 48, and 60-month terms.
HiTechCloud helps you select the GPU count, VRAM, CPU cores, storage, runtime, and a GPU cloud operating model that fits.
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