Hardware planning

Plan the hardware behind your AI workload.

TRcompute helps define an equipment brief for training and inference, then coordinates hardware sourcing. Start with your application, existing infrastructure and operational constraints.

Discuss your infrastructure GPU server supply

Give the procurement team a usable specification

You may know the model you need to run without having a final server configuration. We help identify the hardware requirements and the questions that need to be resolved before requesting supply options.

The planning scope can include a configuration brief, equipment options and a list of site and integration dependencies. We agree the required output at the start of the discussion.

Training and fine-tuning: Review model and dataset requirements, accelerator memory, data movement and checkpoint storage.
Inference: Define the serving workload: model size, precision, concurrency, latency targets and expected operating hours.
Existing environments: Account for available rack space, power, cooling, network interfaces, storage and the software stack you already use.

Define the scope of work

Responsibilities to establish before procurement
Workstream	Scope to establish
Hardware planning and sourcing	TRcompute reviews the equipment brief, identifies supply options and coordinates the agreed procurement scope.
Application and data	Your team defines model behaviour, data access requirements, software licences and application acceptance criteria.
Installation and operation	Agree who handles installation, software deployment, security, monitoring and ongoing administration.
Validation	Agree representative workloads, testing responsibilities and acceptance criteria before the equipment order.

Choose how to obtain the capacity

A defined equipment list can move into server procurement. For a limited project window, discuss rental requirements. If several nodes and shared storage are involved, include the cluster infrastructure in the same brief.