GPU compute, data platforms and the protection layer underneath — the physical foundation of an AI program, quoted with the compliance discipline government AI demands.
AI & Data
AI programs run on hardware someone has to get right.
Behind every model is a bill of materials: GPU nodes, fast storage, lossless networking, and the power to feed it all. We quote AI infrastructure the way programs actually buy it — validated configurations, honest lead times on constrained parts, and compliance documentation that lets public-sector AI move.
GPU supply is volatile, configurations are unforgiving, and a mis-specced interconnect can idle a seven-figure cluster. We validate platform compatibility line-by-line — GPUs to chassis, NICs to fabric, power to rack — and tell you the real lead time before you commit, not after.
For federal and defense AI, the compliance layer is built in: TAA confirmation, §889 screening and documented chain of custody on every order.
Validated GPU server and workstation configurations
Honest lead-time reporting on constrained parts
Power and cooling quoted with the compute that needs it
From a single fine-tuning workstation to multi-node GPU clusters: we quote accelerated compute as validated configurations — GPU, chassis, CPU, memory, NIC and fabric checked against each other — so what arrives boots as a system, not a compatibility experiment.
Where the build needs it, the OEM-integration lane racks, cables and burns in nodes before delivery.
GPU servers and workstations (NVIDIA ecosystem, Supermicro platforms)
High-bandwidth fabrics and NICs validated per topology
Rack power and thermal sized for accelerated loads
Integration, imaging and burn-in available per order
Training throughput dies at the storage layer first. We spec NVMe tiers, parallel-friendly arrays and capacity economics against your data pipeline — ingest, staging, training, archive — and quote the memory and drive BOMs that make existing platforms keep up.
NVMe and high-throughput storage for training workloads
Capacity tiers for lakes, staging and archive
Memory and drive upgrade BOMs for installed fleets (Micron, Axiom ecosystems)
Models are rebuildable; training data often isn't. Immutable backup targets, air-gap-capable storage and the recovery licensing to match — quoted against the value of the datasets, with the governance questions (where data lives, who can touch it) reflected in the hardware architecture.
Immutable and air-gap-capable backup targets
Recovery licensing with term clarity (Veeam ecosystem)
Inference increasingly happens where the sensor is: vehicles, field sites, facilities. We source compact and ruggedized inference hardware, the connectivity to reach it, and per-site kitting through the logistics lane so an edge-AI rollout ships as repeatable, labeled packages.
Compact GPU and accelerator hardware for inference
Ruggedized enclosures for harsh environments
Per-site kitting and staged rollouts
UAS and sensor-platform sourcing through the mission lanes
Outcomes
What AI programs get from buying here
Configs that boot
Platform compatibility validated line-by-line before the order — not on the datacenter floor.
Lead-time honesty
Constrained parts flagged with real dates, so program plans survive contact with supply.
Compliance built in
TAA, §889 and chain-of-custody on the same paperwork as any federal order.
One accountable stack
Compute, storage, network, power and protection on one quote.
Most AI hardware budgets are wrong in the same direction: sized for training when the workload is inference, or for a cluster when a workstation would prove the case. We size against the actual job — model class, precision, batch profile, dataset shape — and match it to the smallest tier that does the work: a workstation for fine-tuning and evaluation, a single node for departmental inference, a cluster only when the arithmetic demands one.
Sequencing matters as much as sizing. A pilot tier that ships this quarter produces the utilization data that justifies — or kills — the next tier, so capital follows evidence instead of forecasts. Because platform compatibility is validated up front, the pilot's chassis, fabric and storage choices carry into the scaled build rather than being replaced by it, and constrained parts get real lead-time dates at every step of the growth plan.
Workload-first sizing: inference, fine-tuning and training tiered separately
Pilot configurations chosen so they scale into the production build
VRAM, memory and storage throughput sized to the dataset, not the datasheet
Utilization checkpoints between tiers so the next purchase is evidence-based
Yes — send the target workload or a reference architecture. We validate the configuration (GPUs, fabric, storage, power), flag constrained lines with realistic lead times, and return a firm line-item quote.
GPU availability is volatile. How do you handle it?
With honesty: live stock where we have it, real lead times where we don't, and compatible alternatives flagged when a different SKU delivers the same capability sooner.
Do you supply AI software and model licensing?
We quote the platform layer — OS, orchestration, data and protection licensing. For model and framework questions, we scope through the R&D lane alongside your technical team.
Can inference hardware be deployed to field sites?
Yes — compact and ruggedized inference builds can be kitted per site and staged through managed logistics, with chain-of-custody documentation to the door.
What hardware do I need to run AI models on premises?
It depends on the job. Inference on small-to-mid models runs on a single GPU workstation or server; fine-tuning typically needs one node with high-VRAM GPUs, fast NVMe storage and enough system memory to stage the dataset; full-scale training is a cluster problem — multiple GPU nodes on a high-bandwidth fabric with parallel storage. Size from the model class and dataset you actually have, and buy the smallest tier that does the work; a validated platform can scale later.
Do AI servers need special power and cooling?
Usually, yes. A dense GPU server can draw 3–10 kW or more — more than an entire rack of ordinary servers — while many facilities budget only 5–8 kW per rack. Before ordering, check three numbers: per-rack power budget, cooling capacity in kW per rack, and the circuit type available (208V three-phase is common for GPU nodes). If the room can't feed the density, plan fewer nodes per rack, upgraded PDUs and containment — and quote that power work with the compute.
Put real hardware under your AI program.
From one workstation to a cluster — validated configurations, honest lead times, compliant paperwork.
