Technology

Infrastructure for on-premise AI: compute, storage and network

Bringing artificial intelligence within the organisation’s perimeter is first of all an infrastructure project: GPUs, storage and network must be sized for real use cases.

Size for the use cases

Number of concurrent users, model size, volume of documents to index and expected response times determine the compute capacity needed. Oversizing is costly; undersizing undermines adoption.

Not just GPUs

Fast storage for indexes and documents, low-latency networking between nodes, adequate power and cooling, backup and monitoring. And security designed for new risks: access to models, protection of data used for retrieval, interaction logging.

A phased path

  1. Selection and prioritisation of use cases.
  2. Pilot on infrastructure sized for the first phase.
  3. Progressive scaling based on usage metrics.
  4. Operations and 24/7 security monitoring.

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