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OFFICIAL // CTG NATIONAL // PRACTICE 02

AI Infrastructure.

From a single H100 box to a 256-GPU cluster — designed, racked, and benchmarked before it lands in your datacenter.

01 // OUTCOMES

What you get.

  • 01 A GPU footprint sized to the models you actually run.
  • 02 Storage and network design tuned to the workload it has to carry.
  • 03 Validated stack: drivers, CUDA, NCCL, schedulers — burnt in before delivery.
  • 04 Day-2 documented: monitoring, capacity planning, and refresh on a contract.
02 // CAPABILITIES

How we deliver it.

Capabilities listed are the ones we ship under fixed scope. Custom work outside this list is welcomed — and scoped explicitly.

CAP / 01

Cluster design

H100, H200, B200, Blackwell — sized against your models and their tokenomics: the per-token inference cost that owning the hardware on-prem drives down at scale.

CAP / 02

Storage for AI

All-flash tiers, parallel filesystems, object backends. NetApp, VAST, Pure, and reference-architecture validated.

CAP / 03

Network fabric

400G / 800G InfiniBand and Ethernet fabrics. RoCE, spine-leaf, and tested under workload before sign-off.

CAP / 04

Validation & burn-in

Every node passes a documented validation suite in our lab — NCCL all-reduce, sustained tensor throughput, thermal soak — before you see it.

CAP / 05

High-performance computing

Parallel compute for CFD, FEA, MODSIM, and scientific workloads — batch scheduling, high-speed interconnects, and high-performance storage. Practice led by a former NASA Advanced Supercomputing CTO.

CAP / 06

Rapid deployment

AI-driven, vendor-agnostic provisioning (PXE, Redfish, Ansible) takes a GPU cluster from unboxing to production in days, not weeks — with zero configuration drift.

03 // ECOSYSTEM

OEMs we engineer with for ai infrastructure.

We carry authorizations across each. Status changes — call us if a vendor matters to you and isn't listed.

NVIDIA
Supermicro
Dell Technologies
HPE
VAST Data
Pure Storage
NetApp
04 // PROOF

We build it before we recommend it.

Every reference architecture in this practice has been racked, configured, validated, or prototyped in our lab. That's where every recommendation we make comes from.

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LAB · AI INFRASTRUCTURE
ENGAGE

Scope a ai infrastructure engagement.

Start with a 30-minute call. Bring the workload, the architecture, or the question. We'll bring the engineer.