NVIDIA H100 SXM5/HBM3 GPU infrastructure with 1 to 8 GPUs, up to 640 GB of VRAM, AMD Genoa or Intel Platinum CPUs and high-speed peer-to-peer interconnect for demanding AI workloads.
NVIDIA DGX H100 is built for AI training, fine-tuning, inference and HPC workloads that need powerful GPUs, large VRAM and multi-GPU scaling.
H100 GPU infrastructure for AI training, fine-tuning, inference, HPC and large-data workloads.
H100 80 GB VRAM options from a single GPU to a full 8-GPU node, covering a range of deployment sizes.
High-performance CPUs balance the data pipeline, preprocessing and parallel compute tasks.
The multi-GPU plans support high-speed peer-to-peer interconnect for distributed training and inference.
H100 SXM5 80 GB
H100 SXM5 80 GB
H100 SXM5 80 GB
H100 SXM5 80 GB
NVIDIA DGX H100 offers options from a single GPU to a full multi-GPU node, so you can scale with each stage of an AI product.
Suited to fine-tuning, inference, RAG, embeddings and large language model experimentation.
Choose 1, 2, 4 or 8 H100 GPUs to suit needs from development through to production.
Several HGX, HBM3 and DGX H100 Cloud product groups are available on different billing terms.
The 8-GPU configurations provide large total VRAM for big batches, large models and high-load inference.
The dedicated products suit environments that need steady performance and isolated resources.
HiTechCloud advises on configuration, runtime environment, drivers and the right AI framework.
Deploy the H100 for chatbots, RAG, embeddings, recommendations and production AI applications.
The 4–8 GPU plans support training, distributed training and large-scale AI pipelines.
Accelerate HPC, rendering, simulation, data analysis and demanding engineering applications.
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 an NVIDIA DGX H100 configuration at HiTechCloud.
Suited to AI training, fine-tuning, inference, HPC, simulation, rendering and any workload that needs high-performance H100 GPUs.
1 GPU suits experimentation, small fine-tuning runs and inference. 2, 4 or 8 GPUs suit training, high-load inference and large models.
Depending on the product, terms of 1 day, 1 week, 1 month, 3 months, 6 months, 12 months and longer are available.
Yes. The DGX H100 suits training, fine-tuning, LLM inference, generative AI, computer vision and HPC workloads that need high performance.
When the workload needs large VRAM, high bandwidth, multiple GPUs, shorter training times, and a stable data center environment.
HiTechCloud advises on CUDA, drivers, PyTorch, TensorFlow, containers, storage, and the runtime that fits your AI pipeline.
Yes. HiTechCloud helps you choose the GPU count, CPU cores, RAM, storage and rental term to match both technical goals and budget.
HiTechCloud helps you select the GPU count, VRAM, CPU cores, storage, runtime environment, and an operating plan that fits your budget.
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