Deploying Production GPU Infrastructure for AI Startups
How we structure rapid onboarding for ML workloads — from prototyping blur to production-ready GPU capacity.
The Scenario
An AI startup at Series A stage typically hits a wedge:
- Models are trained or prototyped on shared developer tools (Colab, Lambda Labs, gaming rigs)
- Production signal arrives before production capacity
- Public cloud quotes arrive with multi-week lead times and sticker prices
- Training cost uncertainty prevents founders from committing to dedicated infrastructure
This is a pattern we see. Here is how we structure onboarding to GPU infrastructure that scales.
What Production Needs
A production GPU stack typically looks like:
- Compute: GPU instances sized by model — from adequate single-GPU inference to multi-GPU training families
- Storage: NVMe block storage for epochs, checkpoints, and training data
- Network: Low-latency intra-cluster communication for data-parallel training
- Platform: Kubernetes for ML workloads (Kubeflow, MLflow) or simpler TorchRun pipelines depending on how you operate
Fugoku runs Kubernetes on AWS EKS and supports GPU instance types via AWS — the same EC2-backed environment that we manage across our provisioning stack.
Sample Engagement Timeline (3-6 weeks typical)
| Phase | Days | Outcome |
|---|---|---|
| Discovery | 1-2 | Audit current sample metrics and workloads |
| Architecture | 3-5 | Target stack defined and priced |
| Pilot rollout | 5-10 | Ads-skipping testing of your models on representative infrastructure |
| Production | 10-14 | Cutover, ongoing operations, cost reporting |
Exact figures vary by model size and throughput. We provide written estimates before commencement — no open-ended commitments.
What We Typically Deliver
- GPU access via AWS p5 / g6 instance families (or comparable availability zones)
- Managed Kubernetes or managed virtual machines, depending on workload pattern
- Persistent NVMe-backed block storage for checkpoints
- Cost visibility from day one — no surprise egress charges
How We Think About Cloud Cost for GPUs
A typical GPU deployment saves material versus hourly public cloud spend. Exact figures depend on utilization profile; the economics move significantly once your models move from R&D into production hours. We quote based on your actual workload.
We also help you choose when to scale. Sometimes a reserved deployment wins; sometimes a shared on-demand model still fits better.
Talk to Fugoku about a structured GPU onboarding for your models. We'll estimate within a week.
Scope Note
This article describes standard engagement patterns, not a client case study. Specific timing and savings depend on model, workload, and prior architecture; your results will be evaluated during the discovery call.