GPU Compute
Accelerate AI training, inference, HPC, rendering and large-scale data processing on a GPU platform.
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.
- HiTechCloud’s role
- Partner CSP · 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 is a Partner CSP (Cloud Service Provider) of NVIDIA, and also holds the Data Center Partner and Advanced Technology Partner roles in the NVIDIA ecosystem.
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.
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
-
01
Workload assessment
Define the AI problem, data, model, compute, network, security and budget requirements.
-
02
Architecture design
Recommendations on the right NVIDIA GPU, DGX, cloud, storage, networking and software stack.
-
03
Platform deployment
Configuration of infrastructure, software environment, security, monitoring and application integration.
-
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.
