How an AI Startup Deployed GPU Infrastructure in 2 Weeks
A computer vision startup needed production-grade GPU infrastructure fast. Public cloud quotes said 6 weeks. Fugoku delivered a fully operational AI platform with 8x A100 GPUs in 14 days — at 60% less cost.
The Challenge
A Series A computer vision startup had a market window closing fast. Their product — real-time defect detection for manufacturing lines — had three enterprise pilots signed and starting in 6 weeks. But they had a problem: their AI infrastructure didn't exist yet.
They'd been training models on a mix of Google Colab Pro, a single A100 instance on Lambda Labs, and a gaming PC with two RTX 4090s under a desk. It worked for R&D. It wouldn't work for production.
What they needed:
- 8x A100 80GB GPUs for parallel training runs
- Production inference serving with <50ms latency SLAs
- 200TB+ storage for industrial imaging datasets (each client generates 2-5TB/month)
- Multi-tenant isolation — each enterprise pilot needed dedicated compute and storage quotas
- Kubernetes-native — their ML pipeline was built on Kubeflow and Argo Workflows
- Operational in 2 weeks — pilot deadlines were non-negotiable
The Public Cloud Problem
Their cloud architect scoped the AWS equivalent:
- 2x
p4d.24xlargeinstances (8x A100 each): $24,000/month on 1-year reserved - 200TB S3 storage + data transfer: $5,800/month
- EKS cluster + networking: $2,400/month
- Total: $32,200/month ($386,400/year)
And the timeline: 4-6 weeks to get GPU capacity (availability constraints), set up VPC networking, configure IAM, deploy EKS, and validate the ML pipeline on the new infrastructure.
Six weeks wouldn't work. The pilots started in six weeks. They needed infrastructure now.
The Solution
Fugoku provisioned a dedicated AI platform:
Compute:
- 2x bare-metal GPU servers, each with 4x NVIDIA A100 80GB
- 2x CPU-only servers for preprocessing, inference serving, and platform services
- InfiniBand interconnect between GPU nodes for distributed training
Storage:
- Ceph cluster with NVMe OSDs: 240TB usable capacity
- 5.2 GB/s sequential read throughput — fast enough to saturate GPU memory bandwidth during training
- S3-compatible API for dataset management (MinIO gateway to Ceph)
Platform:
- Kubernetes 1.29 with NVIDIA GPU Operator
- GPU scheduling: time-slicing for development, whole-GPU allocation for training
- Kubeflow Pipelines for ML workflow orchestration
- MLflow for experiment tracking and model registry
- JupyterHub for interactive development
- Prometheus + Grafana with GPU-specific dashboards (utilization, memory, temperature, power)
Multi-tenancy:
- Kubernetes namespaces per pilot client
- ResourceQuotas enforcing GPU and storage limits per tenant
- Network policies isolating tenant traffic
- Separate Ceph pools per tenant for data isolation
The 14-Day Build
Days 1-3: Hardware & Network
Hardware sourced from Fugoku's pre-provisioned inventory. Servers racked, cabled, and network-configured in the client's preferred colocation facility. InfiniBand fabric validated with NCCL all-reduce benchmarks: 380 GB/s aggregate bandwidth across 8 GPUs.
Days 4-6: Platform Foundation
OpenStack deployed for infrastructure management. Kubernetes cluster bootstrapped with Cilium CNI and NVIDIA GPU Operator. Ceph storage cluster online with 240TB capacity and S3 gateway.
Initial validation: ran a ResNet-50 training benchmark across all 8 GPUs. Achieved 95% scaling efficiency vs. single-GPU baseline — confirming the InfiniBand interconnect was performing correctly.
Days 7-9: ML Platform
Kubeflow Pipelines deployed and integrated with the startup's existing pipeline definitions. MLflow server configured with Ceph-backed artifact storage. JupyterHub deployed with pre-built GPU-enabled notebook images matching the team's existing conda environments.
The data science team ran their first training job on the new platform on Day 8. Their YOLOv8 defect detection model — which took 14 hours on their old single-A100 setup — completed in 1 hour 52 minutes on 8 GPUs with distributed data parallel training.
Days 10-12: Multi-Tenancy & Production Hardening
Tenant namespaces created for all three enterprise pilots. Resource quotas configured: 2 GPUs and 40TB storage per pilot, with 2 GPUs reserved for the startup's own R&D. Network policies validated — zero cross-tenant traffic leakage.
Inference serving deployed with NVIDIA Triton on dedicated CPU nodes with TensorRT optimization. Latency benchmark: 23ms p95 for their primary defect detection model — well under the 50ms SLA.
Days 13-14: Handoff & Validation
Full platform documentation delivered. Operations runbooks for common tasks: scaling GPU allocation, onboarding new tenants, rotating certificates, troubleshooting training failures.
Three handoff sessions with the team covering: day-to-day operations, incident response, and capacity planning. The startup's single DevOps engineer was confidently operating the platform by end of Day 14.
The Results
Cost Comparison
| Component | AWS (Monthly) | Fugoku (Monthly) | Savings |
|---|---|---|---|
| GPU compute (8x A100) | $24,000 | $8,800 | 63% |
| Storage (200TB+) | $5,800 | $2,200 | 62% |
| Platform/orchestration | $2,400 | $0 (included) | 100% |
| Egress/transfer | $1,200 | $0 | 100% |
| Management fee | $0 | $2,500 | — |
| Total | $33,400 | $13,500 | 59.6% |
Annual savings: $238,800
And that's with a 1-year AWS reserved pricing comparison. On-demand would be $52K+/month.
Performance
| Metric | Previous Setup | Fugoku Platform |
|---|---|---|
| YOLOv8 training time | 14 hours (1x A100) | 1h 52min (8x A100) |
| Inference latency (p95) | 89ms (CPU) | 23ms (Triton + TensorRT) |
| Dataset loading throughput | 800 MB/s (EBS) | 5.2 GB/s (NVMe/Ceph) |
| Experiment turnaround | 1-2 days | 2-4 hours |
| GPU utilization (training) | 71% (cloud, noisy neighbor) | 94% (dedicated) |
Business Impact
- All three enterprise pilots launched on time — the infrastructure was ready 4 weeks before the first pilot start date
- Model accuracy improved 12% in the first month due to faster experimentation cycles (more experiments = better hyperparameter search)
- Closed Series B 5 months later. The production-grade infrastructure and successful pilot results were central to the fundraising narrative
- Expanded to 7 enterprise clients within 6 months, scaling to 16 GPUs on the same platform architecture
The Time Factor
The 4-week advantage over public cloud provisioning meant the startup:
- Had infrastructure ready before pilots started (not scrambling during)
- Ran 3 weeks of pre-pilot optimization (model tuning for each client's specific defect types)
- Launched with confidence and data, not hope
- Beat a competing startup (larger team, more funding) to market by launching pilots first
Their CTO later said: "The infrastructure timeline was the difference between winning and losing those pilots. If we'd waited 6 weeks for AWS, our competitor would have gotten there first."
Why 2 Weeks Is Possible
Three factors made this speed achievable:
-
Pre-provisioned hardware inventory. Fugoku maintains ready-to-deploy GPU servers. No 4-6 week procurement cycle.
-
Automated platform deployment. OpenStack, Kubernetes, Ceph, and the ML toolchain are deployed via tested automation — not manual configuration. What takes weeks of engineering time manually takes hours with automation.
-
Opinionated architecture. No vendor evaluation. No architecture committee. The stack is proven. The only customization is sizing and configuration for the specific workload.
Speed isn't about cutting corners. It's about eliminating unnecessary steps and pre-solving predictable problems.
GPU infrastructure in weeks, not months. 60% less than public cloud. Talk to Fugoku about your AI platform.