HiTechCloud x NVIDIA

GPU Cloud, DGX and AI Factory for business

HiTechCloud builds AI infrastructure on the NVIDIA ecosystem — from GPU compute and DGX to enterprise software, networking and visualization.

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HiTechCloud's role
Cloud Partner · Data Center Partner · Advanced Technology Partner
Capability groups
Compute · Software · Network · Visualization
Deployment scale
From development environments to AI Factory and SuperPOD
Scope of support
Design, deployment and long-term operations
About the partnership

From GPU cloud to AI Factory and SuperPOD

HiTechCloud NVIDIA

How HiTechCloud positions its capabilities in the ecosystem NVIDIA in the roles of Cloud Partner, Data Center Partner and Advanced Technology Partner.

HiTechCloud helps businesses design, deploy and operate NVIDIA infrastructure for AI training, inference, visualization, virtual desktop and high-performance computing workloads.

The approach centers on real-world performance, scalability, data security, cost efficiency and long-term operability, rather than simply supplying hardware.

Solutions

Core NVIDIA capabilities

The catalog is organized by solution group so you can pick the right deployment path.

GPU Compute

Accelerate AI training, inference, HPC, rendering and large-scale data processing on a GPU platform.

DGX AI Compute Systems

Advice on and deployment of DGX systems for businesses that need dedicated AI compute capability.

NVIDIA AI Enterprise

Make use of an enterprise-grade AI software stack, runtime and operating tools.

Virtual Desktop

Virtual desktops, graphics applications, remote workstations and high-performance work environments.

Networking

High-performance network design for GPU clusters, data centers, storage and distributed AI workloads.

Visualization

Graphics, simulation, digital twins, 3D rendering and visualization workflows.

Why choose HiTechCloud

How HiTechCloud deploys it

  • Start from the workload, not the hardware Define the AI problem, data and model first, then choose the matching GPU, DGX or cloud option.
  • Scale in phases From development to production, scaling up as your data and compute needs genuinely grow.
  • With operational capability included Performance monitoring, cost optimization and technical support once the system is live.
Process

Four-step deployment roadmap

  1. 01

    Workload assessment

    Define the AI problem, data, model, compute, network, security and budget requirements.

  2. 02

    Architecture design

    Recommendations on the right NVIDIA GPU, DGX, cloud, storage, networking and software stack.

  3. 03

    Platform deployment

    Configuration of infrastructure, software environment, security, monitoring and application integration.

  4. 04

    Operations and optimization

    Performance monitoring, cost optimization, resource scaling and long-term technical support.

Use cases

Common enterprise scenarios

Model training and fine-tuning

GPU, storage and network infrastructure for training, fine-tuning and data pipelines.

Inference platform

Deploy inference that scales, with monitoring and running cost optimization.

Virtual workstations

Virtual desktops for engineers, designers, architects and remote teams.

Modernize the data center

Upgrade compute, networking, software and automation on your existing infrastructure.

Building an AI platform for business

Send us your AI problem and your current infrastructure, and HiTechCloud will propose a GPU configuration and rollout plan.

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