NVIDIA DGX Spark
Procurement assistance, setup, model deployment, training and ongoing administration, subject to availability.
EXZAI Infrastructure
GPU infrastructure, designed for real workloads.
From desktop AI systems to shared GPU platforms, EXZAI helps you select, integrate and support the computing foundation for your AI.
GPU purchases are often sized on parameter counts alone. EXZAI sizes infrastructure on what matters in production: throughput, concurrency, memory, latency, security, energy and total cost.
What we deliver
NVIDIA DGX Station
Deskside AI capacity for teams, subject to configuration and availability.
GPU workstations
For development, inference and specialist workloads.
GPU servers
Shared enterprise inference and model development platforms.
Multi-GPU and multi-node clusters
With appropriate high-speed networking, scheduling and storage.
On-premises AI appliances
A configured stack: hardware, operating system, inference, interface and monitoring.
GPU architecture and sizing
Based on tokens per second, concurrency, memory, latency, security, energy and cost.
AI networking and storage
Ethernet or InfiniBand where appropriate, NVMe, backup, replication and high availability.
Virtualization, containers and orchestration
Linux, Docker, Kubernetes and alternatives selected per project.
Installation and lifecycle support
Installation, migration, warranty coordination and lifecycle support under applicable supply agreements.
Infrastructure catalog
NVIDIA DGX Spark
Compact development system for local AI workflows and experimentation.
Learn moreGPU servers
Shared platforms for enterprise inference and model development.
Learn moreProduct specifications, commercial availability and configurations subject to confirmation.
Your data. Your deployment choices.
Run AI on your own servers, in a dedicated environment or in a carefully designed hybrid architecture. EXZAI helps you choose the right combination of model performance, governance, cost and operational control.
On-premises
AI on your own infrastructure
For sensitive workloads, internal network access and customer-owned hardware. Security and data residency depend on configuration and controls.
Private cloud
A dedicated, managed environment
Managed isolated infrastructure with less hardware for your team to operate. Hosting location and contractual terms are specified for each engagement.
Hybrid
Private data, selective external compute
Keep private data operations in your environment and use external compute or APIs where appropriate. Requires a data-flow map and review of cross-border considerations.
Edge & desktop
Local AI for teams and edge sites
For developer teams, local prototyping and disconnected or edge needs. Capacity depends on the chosen machine and model.
From first assessment to daily operations.
We prioritize measurable use cases, a clear technical architecture, responsible deployment and long-term maintainability.
- 01
Discover
Business outcomes, data landscape, constraints and target users.
- 02
Design
Architecture, model selection, security approach, total cost and pilot metrics.
- 03
Pilot
A narrow proof of value measured against agreed evaluation criteria.
- 04
Deploy
Production infrastructure, integrations, access, tests and documentation.
- 05
Operate
Monitoring, updates, support and optimization.
- 06
Enable
Administrator training, user adoption and ongoing improvement.
Ready to make AI work for your organization?
Tell us what you want to build. We will help define a practical technical and commercial path.