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Rows of GPU server racks with dramatic blue ambient lighting in modern AI data center
Artificial intelligence

AI Infrastructure

If your data must not leave the building, the model has to run on your own servers. That is not a software job; it is procurement, data centre installation and ongoing maintenance.

Layer structure
  • ApplicationL6
  • Model servingL5
  • RuntimeL4
  • StorageL3
  • GPU serverL2
  • Power and coolingL1

Bottom to top: from physical infrastructure to application. Each layer has its own delivery document.

In brief

AI infrastructure is the GPU server, storage, cooling and model-serving layer required to run models on an organisation's own hardware. Mipo handles this end to end: sourcing the GPU server abroad, running the import and customs process, installing it in the data centre and maintaining it afterwards. Where data cannot leave the organisation, this is the only realistic route.

Layers

GPU server infrastructure from power to model, layer by layer.

Infrastructure is not a single job — it is layers stacked on top of each other. An installation that skips a layer makes everything above it fragile.

L6

Application

Internal applications and interfaces that use the model

L5

Model serving

The layer models are served from, with version and resource management

L4

Runtime

Drivers, libraries and GPU resource sharing

L3

Storage

High-speed storage for model files and the vector database

L2

GPU server

GPU, memory and redundant power sized to the model

L1

Power and cooling

Rack capacity, airflow and electrical calculation

AI infrastructure developer with headset reviewing neural network model on screen
Site survey

For AI infrastructure we measure power and cooling on site.

Measurements are taken before installation. Capacity, coverage and downtime window are not determined by estimation.

Load profile

Which model, how many concurrent users, how much memory — the hardware decision follows this measurement.

Power capacity

The existing rack's electrical capacity is measured. GPU servers draw markedly more than standard servers.

Cooling calculation

Heat load is calculated; if existing cooling is insufficient it is resolved before installation.

Data path

Where the data the model reaches is held, and how it travels across the network.

AI infrastructure team in front of large neural network screen
Post-installation measurement is reported against pre-installation measurement.
Technical specifications

Which hardware and software we build GPU servers with.

GPU
Selected against model memory requirement, written with rationale
Storage
High-speed local disk · vector database
Power
Redundant power supply · separate circuit
Cooling
Heat load calculated before installation
Access
Segmented network · operates without external connectivity
Monitoring
GPU utilisation · temperature · memory · capacity trend

Installation timeline depends on procurement lead time

Delivery documents

When the AI infrastructure is handed over, what you hold.

Infrastructure without documentation is infrastructure dependent on whoever built it. The delivery file is prepared so another team can take over.

  • Load profile and hardware rationale report
  • Rack layout and power distribution diagram
  • Cooling and heat load calculation
  • Model serving layer configuration document
  • Access and authorisation matrix
  • Acceptance test record
Frequently asked

Questions about ai infrastructure.

Three reasons: it is mandatory when data cannot leave the organisation; beyond a certain level of sustained use, subscription cost exceeds the hardware investment; and you are not exposed to a third party's pricing or policy changes. For low, intermittent use cloud is the better answer — we run that comparison with you.

Yes. Power draw and heat output are markedly higher than standard servers. Installing without calculating existing rack and cooling capacity shortens hardware life and affects neighbouring systems. The power and cooling calculation is therefore part of the scope.

The memory requirement of the model and the number of concurrent users are decisive. An oversized GPU is wasted cost; an undersized one means the model does not run at all. The decision is made against a measured load profile and given in writing with its rationale.

No. Sourcing, import and customs for the GPU hardware are handled by us. Your involvement begins at delivery and commissioning.

If you wish, yes. Having infrastructure and application under one company means accountability is not split when performance issues arise. Infrastructure installation alone can also be purchased.

Next step

We don't quote ai infrastructure without seeing the site.

Book a site visit and we'll start with a measurement of existing infrastructure. The report stays with you even if you don't work with us.