Enterprise AI inference infrastructure
A phased reference architecture for private knowledge assistants, document processing and enterprise AI services, with workload validation before configuration selection.
Architecture and delivery scope
Workload brief — capture model versions, precision, context length, concurrency and latency or batch targets before calculating capacity.
Compute layer — evaluate catalog accelerators and server platforms as candidates. Exact GPU quantities, slot layout, firmware and driver support require separate compatibility checks.
Data and network layer — separate document ingestion, model storage and serving traffic; size storage access and uplinks from measured demand.
Site survey — confirm rack units, power feeds, populated-node power, cooling capacity and available cabling at the deployment location.
Service controls — define user access, data boundaries, monitoring, backup responsibilities and the procedure for recovering a failed node.
Phased delivery — use a representative workload in a pilot, then agree a bill of materials, licenses, installation scope and expansion triggers.
Acceptance checklist — measure service response times, batch completion, stability and recovery against agreed targets. This reference design does not guarantee workload performance.