EXZAI Infrastructure
GPU servers sized for your workload.
The right server depends on model type, memory requirement, context length, quantization, simultaneous users, networking, storage, rack space, power, cooling and maintenance. We size against your workload, not a brochure.
Typical use cases
- Shared enterprise inference
- Model development and evaluation
- Retrieval and knowledge platforms
What we need to size a server
- Model type and size
- memory requirement
- context length
- quantization
- simultaneous users
- networking
- storage
- rack space
- power and cooling
- maintenance expectations
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.
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.