The Seven-Layer AI Factory Reference Architecture
A reference approach for teams building serious GPU platforms: workload-first design, opinionated component choices, and a delivery plan that maps to real capital and timelines.
Seven layers, one operating model.
Each engagement maps your workload onto the seven layers of an AI factory. Every layer has measurable budgets — watts, tokens, dollars, latency, risk — and a clear owner.
Seven structured workstreams.
Workload-first architecture
Define the workload — model sizes, batch shapes, throughput, latency SLOs, training cadence — before any vendor conversation. Architecture choices follow workload, not the other way round.
Hardware and topology
GPU class, NVLink / NVSwitch domains, NIC count and placement, local NVMe, rack and pod design. Sized to the workload and the facility envelope.
Cluster orchestration
Kubernetes with the GPU Operator, Slurm or Ray for batch and training, Kueue / Volcano for fair-share scheduling, and policy-driven multi-tenancy.
Inference & training platform
vLLM, SGLang, Triton, TensorRT-LLM, and NIM-style services for inference. PyTorch DDP / FSDP / DiLoCo for training. Shared model registry, checkpoints, and evaluation harnesses.
Security & tenancy
Tenant isolation, secrets, audit logs, agent policy gates, supply-chain controls, and SOC 2 / ISO 27001 / GDPR-aligned mapping.
Observability & operating model
DCGM, Prometheus, Grafana, Datadog, OpenSearch. GPU utilisation, queue depth, latency percentiles, tokens/sec, cost/token. SLOs, runbooks, incident response.
Delivery roadmap
Phased delivery plan — assessment, design, build, harden, scale — with measurable acceptance criteria for each milestone.
Bring the architecture into focus.
An infrastructure review captures the workload, facility, vendor, and operating assumptions, and produces a concrete reference architecture and delivery plan.