GPU infrastructure consulting
Hands-on help with GPU infrastructure on Kubernetes, from a team that runs it in production
We build and operate a GPU cloud platform for a living: multi-tenant clusters, distributed training, usage metering, and day-2 operations on NVIDIA hardware. The same engineers work directly on your stack. Engagements are vendor-neutral. We focus on the software layer, and we leave you with infrastructure your team can operate.
Who this is for
Two kinds of teams hire us
Enterprise platform teams running GPU infrastructure for their own AI workloads, who need it built correctly or need an existing cluster fixed. And GPU cloud operators building the self-service platform layer their tenants expect. Different goals, same underlying problems: schedulers, network fabric, driver lifecycle, and keeping expensive hardware busy.
Enterprise infra and platform teams
You have GPUs, and you need to give your ML and research teams a reliable, self-service way to use them on Kubernetes. We help you stand that up, or fix the one you have: distributed training, fair scheduling, cost visibility, and a cluster your on-call rotation can own.
GPU cloud operators and neoclouds
You sell GPU capacity. Your tenants expect self-service workspaces, multi-tenant isolation, usage billing, and a platform that does not require a support ticket for every task. We have built that layer and can build it on your infrastructure too.
Service lines
What we do
Six focused engagements, each scoped to a problem we have solved in production. We work on your stack, alongside your team, and document everything so you are not dependent on us afterward.
Cluster buildout and architecture →
A working GPU cluster from bare nodes or a fresh managed control plane: NVIDIA GPU Operator, device plugin, time-slicing or MIG, storage, and a scheduler that behaves under contention.
Multi-node training →
Distributed training that runs on first launch. InfiniBand or RoCE, MOFED, NCCL topology, and torchrun / DeepSpeed wired into the scheduler so a multi-node job is a config change.
Day-2 operations and SRE →
Upgrades, driver and operator lifecycle, GPU health and node draining, DCGM observability, and runbooks your on-call can follow without escalating to a specialist.
Cost control and chargeback →
Utilization from DCGM and Prometheus, idle detection, quota and fair-share scheduling, and per-user, per-project chargeback records finance will accept.
Platform migration →
Off SageMaker, off bespoke scripts, or onto your own hardware. We plan and run the move to Kubernetes without a multi-quarter freeze.
Tenant platform for GPU cloud operators →
The self-service layer above the Kubernetes control plane: tenant isolation, workspace provisioning, per-tenant metering, and a portal tenants use instead of filing tickets.
Why us
We run this stack ourselves
The recommendations come from production, not slide decks
Our guidance on the GPU operator, the network fabric, or the scheduler is what we built for our own platform. When something breaks in your cluster, we have likely debugged the same failure in ours.
Vendor-neutral
We work on vanilla Kubernetes, EKS, GKE, AKS, and on operator stacks including k0rdent, Rancher with OpenNebula, and Spectro Cloud Palette. The Saturn Cloud product is one option if you want a managed platform afterward. It is not a prerequisite for any engagement.
Software layer only
Power, cooling, cabling, and the physical network fabric are your data center's or cloud provider's responsibility. We start at the OS and Kubernetes layer: drivers, operators, the scheduler, network configuration, and everything your workloads touch above that.
You keep the infrastructure
Every engagement delivers infrastructure-as-code, runbooks, and documented architecture. The goal is infrastructure your team owns and can operate without us.
How an engagement works
Scoped to a concrete outcome
| Phase | What happens |
|---|---|
| Assessment | A paid review of your current cluster, hardware, and workloads. You get a written architecture and a prioritized list of what to fix, whether or not you continue with us. |
| Scope | We agree on a concrete outcome before work starts: "multi-node training runs correctly on 4 nodes," "per-project GPU chargeback exports monthly to your billing system." Fixed deliverables, not open-ended hours. |
| Build | We work in your environment alongside your team, committing infrastructure-as-code as we go. No handoff of a black box at the end. |
| Handoff | Runbooks, architecture docs, and a walkthrough with whoever owns it. Optionally a retainer for ongoing support. |