A production-grade blueprint for standing up multi-tenant GPU infrastructure — from ingress and model serving down to NVLink-connected compute and high-throughput storage, built for real training and inference workloads.
Most teams stand up GPU clusters by extending patterns built for stateless web applications — and it shows up as idle accelerators, saturated links, and storage that can't keep pace with data loaders.
Without scheduling built for accelerators — MIG, time-slicing, priority classes — clusters run at 30–40% utilization while invoices assume 100%.
Distributed training moves gradients between GPUs constantly. Route that over generic VPC networking instead of NVLink/InfiniBand, and compute sits waiting on the network.
Object storage alone starves multi-GPU training jobs. Without a tiered storage layer, expensive accelerators idle waiting on I/O.
Shared clusters without namespace quotas and cost attribution turn into a free-for-all the moment a second team joins.
Presentation, platform, and infrastructure are kept as distinct tiers with clearly owned east-west paths — so scaling the GPU layer never means re-architecting the platform above it.
In order — the sequence architects actually work through when standing this up.
Management, monitoring, and orchestration traffic never shares a path with training or inference data — a noisy dashboard should never stall a training job.
NVLink / NVSwitch for intra-node, InfiniBand or RoCE for inter-node. Routing gradient sync over standard VPC networking is the single most common cause of idle accelerators.
NVMe for hot checkpoints, a parallel file system for training data, object storage for archives. One tier of storage cannot serve all three access patterns well.
Model registry, feature store, and experiment tracking are platform services with their own SLAs — not an afterthought bolted onto the compute layer.
Namespace quotas, MIG / time-slicing, and priority classes — retrofitting isolation onto a shared cluster after the second team joins is far more expensive.
GPU utilization, network fabric saturation, and job queue depth — not just CPU and memory, which are the wrong signals for accelerator-bound workloads.
Illustrative — final selection depends on existing platform investments, team skillset, and cloud vs. on-prem constraints.
We haven't published a client case study for this pattern yet — these are the outcomes this architecture is engineered to hit, based on established infrastructure practice.
We design and stand up infrastructure like this for enterprise and government teams. Bring us your constraints — cloud, on-prem, or hybrid — and we'll work through the architecture with you.