Resource contention
Understand which resources must be isolated and how performance should be assessed for the actual workload.
Private & dedicated compute
Give demanding work
the right environment.
Some applications need closer control over resources, data or performance. Begin with those requirements and build the operating model around them.
The challenges
Control has to extend beyond the hardware.
Sensitive data
Data location, access and retention requirements shape the environment and the way it is operated.
Specialist workloads
Private AI and other demanding applications bring their own needs for memory, storage, networking and software support.
Our approach
Turn requirements
into decisions.
Define the workload
Specify the software, data and performance objective. For AI workloads, include model characteristics, inference or training needs and expected concurrency.
Evaluate the full environment
Assess compute, memory, storage and connectivity as a system. Hardware selection alone cannot establish application performance.
Make the ownership model clear
Agree who deploys, patches, monitors and maintains each layer, and what happens when a component needs replacement.
Common questions
Is dedicated infrastructure always the better choice?
It depends on the workload, utilisation, control requirements and operating resources. Compare the full cost and constraints of the available options.
Can we discuss a private AI environment?
Yes. Share the workload and requirements so suitability, hardware availability and the delivery scope can be assessed.
Does isolation establish compliance?
Isolation can support a control requirement. The applicable obligations, configuration, operating process and evidence still need their own assessment.
The next conversation
Let’s look at
your environment.
Share what runs today and what you would like to improve. The next step is a scoped discussion with the relevant team.
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