Infrastructure
The physical layer AI runs on.
GPU servers don't plug into thin air. They need power, cooling, racks, fiber, and a room engineered to keep them running. We build that layer — from civil works to production cluster — and the software stack on top.
01 — Facility
The build, step by step.
Assessment
We inspect your facility. Power capacity, cooling capability, physical space, network infrastructure. We tell you what's ready, what needs work, and what it will cost.
Design
Power layout. Cooling architecture (air, liquid, or hybrid). Rack configuration. Network topology. Designed for your specific GPU hardware and workload. No generic templates.
Procurement
We source the hardware — GPU servers, networking gear, power equipment, cabling. NVIDIA H100, H200, B200. AMD MI300X. Dell, HPE, Supermicro. We buy it. You own it.
Build
Civil works. Electrical. Cooling installation. Structured cabling. Rack mounting. Server installation. GPU configuration. One crew, one schedule, one point of contact.
Handoff
Documentation, training, remote management setup. You have a running AI cluster. We can walk away — or stay and operate it through Managed Services.
Scope
What's included.
Power
AC and DC distribution, backup, surge protection, load balancing. Designed for GPU density (10–30 kW per rack).
Cooling
Air and liquid cooling. H100/H200/B200 clusters generate heat. We design for sustained load.
Racks & enclosures
Structured cabling, cable management, physical security.
Fiber & networking
Inside-plant fiber and 200/400 Gb/s links between servers — direct GPU-to-GPU networking, which AI training depends on.
Switches & routing
The network fabric that connects the cluster to your systems — and keeps it isolated from everything else.
Remote management
Remote-management hardware that lets us watch over and fix servers even when they're powered down or the operating system isn't running.
02 — Hardware
Hardware guide.
Procurement, installation, configuration — we source, you own.
| GPU | VRAM | Best for | Notes |
|---|---|---|---|
| NVIDIA H100 | 80 GB | Inference, fine-tuning | Industry standard. Most open-weight models run on 2–4× H100. |
| NVIDIA H200 | 141 GB | Large model inference | 1.8× more VRAM than H100 — fewer GPUs per model. |
| NVIDIA B200 | 192 GB | Frontier open-weight, training | Blackwell architecture. Maximum throughput. |
| AMD MI300X | 192 GB | Inference, cost efficiency | Best price-to-VRAM ratio. ROCm support maturing fast. |
A GPU server is more than its GPUs. CPU, RAM, NVMe storage, and network cards all gate performance — data has to reach the GPUs fast enough to keep them busy. We spec the whole machine, not just the accelerator.
| Model size | VRAM needed (approx.) | Typical configuration |
|---|---|---|
| 7–13B | 16–30 GB | 1× H100 — pilots can start on a workstation GPU |
| 70B | ~140 GB full precision · ~70 GB quantized | 2× H100, or 1× H200 |
| Frontier open-weight (MoE) | 300 GB+ | 4–8× H200 / B200 / MI300X |
Quantization (see the glossary) roughly halves VRAM needs with minimal quality loss — it's how a 70B model fits on a single H200.
03 — Software
The software layer.
GPUs you bought are not the same as GPUs that earn their keep. The stack between the hardware and your applications decides how much of what you paid for you actually use.
Inference engines
vLLM, SGLang, TensorRT-LLM — the serving software that batches requests, manages GPU memory, and keeps utilization high. A well-tuned engine is often several times the throughput of a naive setup on the same hardware.
Compute platform
CUDA for NVIDIA, ROCm for AMD. Your GPU choice sets the software path — we work with both, and we make sure it's a paved one.
Orchestration & monitoring
Model versioning, autoscaling, GPU health, latency and cost per request. The operational layer our Managed Services runs day to day.
We install, tune, and document the full stack. Open standards, no proprietary glue — it's part of the exit-ready promise.
Ownership
Own it. Or let us run it.
We build the infrastructure. You own it — the hardware, the facility, everything. If you want us to operate it after handoff — monitoring, updates, security, optimization — that's our Managed Services →. One team across both.
Grid monitoring that never leaves the building.
A utility company in Puerto Rico needs AI for grid monitoring. Data cannot leave the premises — regulators require in-jurisdiction processing. We design the power layout, install the GPU servers, configure the networking, and deploy the model. One contract, one team.
Contact
Does your facility have what AI hardware needs?
We'll assess your space, power, and cooling. Tell you what's missing. Tell you what it costs. No commitment.
Schedule an infrastructure assessment →