AI Factory Setup
You're building a GPU cluster from scratch — on-prem, colo, or cloud. You want to get compute, networking, storage, orchestration, and monitoring right the first time without spending months figuring out what NVIDIA's docs don't tell you.
What We Do
- Compute layer — GPU server selection (DGX, HGX, custom builds), H100/H200/B200 sizing, NVLink/NVSwitch topology, power and cooling planning
- Network layer — RDMA fabric design (InfiniBand or RoCE), leaf-spine topology, compute/storage network separation, GPUDirect RDMA
- Storage layer — Parallel filesystem selection (Lustre, WekaFS, GPFS), checkpoint storage, data staging, GPUDirect Storage
- Orchestration — Kubernetes with GPU Operator + KAI Scheduler, or Slurm with Pyxis/Enroot. Multi-tenancy, quotas, job scheduling
- Operations — DCGM monitoring, XID error detection, automated fault recovery, capacity planning, runbooks
- Cost planning — TCO analysis across hardware, facility, and ops. Build-vs-buy comparison for your workload
Proof
We've built GPU clusters from zero for multiple companies — from 3-node bare-metal setups with RTX 5000 Ada to multi-rack H100 deployments with full RDMA fabric. We built the GPUaaS platform at Aarna Networks from day one through its acquisition by Armada.
Read case study: 8.5x Faster Training with RDMA →
How We Work
Scope
Understand your workload, scale, budget, and timeline.
Design
Architecture document covering all five layers with hardware BOM.
Build
Rack, cable, configure, test. We do the implementation.
Handoff
Runbooks, dashboards, and knowledge transfer.
Technologies
Related
Frequently Asked Questions
What is an AI factory?
A full-stack GPU compute environment purpose-built for AI training and inference — compute, high-speed networking, storage, orchestration, observability, and tenancy — operated as a product for internal or external AI teams.
How long does it take to stand up a production GPU cluster?
For a well-scoped deployment on dedicated hardware, a functional training-ready GPU cluster is typically weeks, not months. Full production hardening — multi-tenancy, self-service, cost allocation, SLOs — is usually a follow-on phase.
Should I build on-prem, in a colo, or in the cloud?
Cloud is fastest to start and best for bursty workloads. Colo hits a lower $/GPU-hour once utilization is above 50-60%. On-prem makes sense for the largest sustained fleets and regulated environments. We help model the tradeoff with your real numbers.
What storage architecture do I need?
Training I/O is dominated by large-file sequential reads and checkpoint writes. Most clusters combine a parallel filesystem (Lustre, WEKA, VAST) or high-throughput object store for datasets, plus local NVMe for checkpoints and scratch.
How do you size the network fabric?
We size inter-node bandwidth from the model's gradient volume and target AllReduce-to-compute ratio, then pick NICs and switch radix accordingly. GPU-to-GPU fabric uses non-blocking or 2:1 CLOS topologies with RoCE v2 or InfiniBand.
Do you operate the cluster after it is built?
Both. We lead greenfield builds end-to-end and can hand off to your SRE/platform team with documentation and runbooks, or stay on as a co-operating partner for a defined period while they ramp up.
Planning a GPU Cluster Build?
We've done this before. Let's talk about what you're building and where we can help.
Schedule a Call