AI Infrastructure & Private AI

Run selected AI workloads on infrastructure you control.

DSI helps organizations assess, size, deploy, integrate, and support private AI environments around their data, workloads, users, and operating requirements.

You do not need to know the model, GPU, or final architecture before starting the conversation.

Where Private AI Fits

Some workloads require a different level of control.

Public AI services remain useful for many workloads. Private or dedicated infrastructure becomes relevant when data location, intellectual property, access, predictable capacity, latency, or dependence on external APIs changes the decision.

DSI helps determine which workloads belong inside an organization-controlled boundary and which can remain in cloud or hybrid environments.

Keep control of sensitive work

Choose where selected models run and where proprietary, customer, operational, or regulated information is processed.

Match capacity to the workload

Plan dedicated compute around expected models, context, concurrency, latency, and growth instead of buying from a specification alone.

Build for real operations

Connect AI to identity, data, applications, security, monitoring, backup, and support from the beginning.

What DSI Can Deliver

An infrastructure-led path from use case to an operable private AI environment.

Engagements are scoped to the requirement. These capabilities can be delivered together or applied to the part of the environment that needs support.

Assessment & sizing

Define the use case, data boundary, model needs, users, performance targets, deployment location, and operational constraints.

GPU infrastructure

Design and source compute with the supporting system memory, storage, networking, power, cooling, and expansion path.

Deployment & model serving

Prepare the environment for model inference, access, resource management, monitoring, and controlled updates.

RAG & integration

Connect approved data sources, retrieval workflows, applications, APIs, and business systems where the use case requires them.

Security & access

Design identity, permissions, network boundaries, logging, patching, and data handling around the organization’s policies and obligations.

Support & lifecycle

Document the environment, hand it over clearly, and define support for infrastructure, software components, changes, and capacity growth.

Implementation Path

Make the important decisions before committing to the hardware.

1
Define the workload
Clarify the business task, users, data, model class, integrations, and target experience.
2
Choose the boundary
Decide what should run on-premises, in dedicated infrastructure, at the edge, or as part of a hybrid architecture.
3
Design and validate
Translate workload requirements into compute, memory, storage, network, power, security, and software decisions.
4
Deploy and operate
Implement the environment, connect approved systems, document ownership, and establish a support path.

Workload-First Sizing

GPU memory matters. It is not the whole sizing decision.

Model size and VRAM provide a starting point, but useful sizing also depends on precision and quantization, context length, KV cache, concurrent users, throughput, latency, framework overhead, workload type, and GPU topology.

The surrounding environment matters too: system memory, storage, networking, power, cooling, deployment location, availability, and future expansion can change the design.

Inference, fine-tuning, or mixed Model & context requirements Concurrent users & throughput Data, RAG & multimodal needs Software compatibility Power, cooling & expansion

Control With Clear Boundaries

Private infrastructure supports your controls. It does not replace them.

A private deployment can help keep selected workloads within security and data-location boundaries your organization controls. Security still depends on sound architecture, configuration, identity, access, patching, monitoring, network design, and operating practices.

DSI can design infrastructure around your security, privacy, and data-location requirements. The resulting environment can support your compliance architecture, but no server or deployment model makes an organization compliant by itself.

Connected Capabilities

Start With the Workload

Tell us what you need the environment to do.

Share the use case, data constraints, expected users, preferred deployment location, or an existing bill of materials. We can help turn it into a practical infrastructure and implementation path.