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ORBITRONOPS
AI Infrastructure10 min read

Building Production-Ready AI Infrastructure

Training and inference place very different demands on infrastructure. A practical view of what production-ready means.

AI infrastructure is often assembled under pressure: a few GPU nodes, a serving container, and a dashboard. Production-ready infrastructure needs considerably more structure.

Separate training and inference concerns

Training is throughput-oriented, bursty and tolerant of queuing. Inference is latency-sensitive and user-facing. They benefit from different scheduling, scaling and capacity strategies.

Make GPU capacity a shared, governed resource

  • Queue-based admission with tools such as Kueue or Volcano
  • Quotas and fair-share across teams
  • Sharing strategies such as MIG or time-slicing where appropriate

Observe what matters

Beyond CPU and memory, track GPU utilization, queue wait time, inference latency percentiles, throughput and cost per model.

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