AI Infrastructure
AI factory architecture starts with the workload.
Compute is only one part of the production system. Networking, storage, data movement, orchestration, serving, observability, security, facilities, and operating ownership determine whether the capacity delivers useful work.
Direct answer
An enterprise AI factory is a repeatable platform for developing and running AI workloads. Its architecture should be derived from model types, training and inference patterns, data volumes, latency, availability, security, location, utilization, and operating constraints before hardware and platform choices are finalized.
Design inputs
Define the workload before the bill of materials.
Training and tuning
Model scale, precision, checkpoint behavior, communication patterns, experiment concurrency, and expected iteration time shape the cluster.
Online inference
Latency, throughput, context size, availability, geography, batching, model mix, and traffic variability shape serving.
Agent workloads
Long-running state, tool calls, retrieval, concurrency, tracing, evaluations, and human waits add infrastructure beyond model execution.
Physical AI
Simulation, synthetic data, perception training, edge deployment, fleet telemetry, and feedback loops span centralized and operational environments.
Compute, networking, and storage are coupled
Accelerators can remain idle when data cannot arrive at the right rate, collectives are constrained, checkpoints compete with training traffic, or scheduling fragments capacity. The infrastructure should be modeled as a flow of data and work, not as independent equipment categories.
Network design must account for traffic patterns, topology, east-west bandwidth, congestion, isolation, and failure domains. Storage must account for ingestion, datasets, metadata, checkpoints, model artifacts, retrieval corpora, cache behavior, retention, and recovery. The correct architecture may combine several storage and network tiers rather than forcing every workload through one path.
Production serving is its own discipline
Inference requires model versioning, routing, batching, autoscaling, fallback, observability, security, and service objectives. Training infrastructure can inform serving choices, but it does not automatically provide a production inference platform.
Operations determine realized capacity
Utilization, queue time, job completion, latency, failures, capacity, energy, and unit economics should be visible. Teams also need ownership for scheduling, upgrades, model lifecycle, incidents, security, quotas, and demand planning. An AI factory without operating discipline is expensive capacity, not a production capability.
Design from workload to operation.
See AI infrastructure and AI factory consulting or email info@svc.group.