THE TRAINING CLUSTER

We train on hardware we own.

No metered bill decides when an experiment stops — the machine is ours, so the iteration is too.

ON-PREMISE 400+ CORES 2 TB+ RAM 24/7 TRAINING

THE ACTUAL MACHINE — NOT A STOCK PHOTO CPU LOOP + ECC BANKS · NODE BUILD, IN-HOUSE

MID-CONSTRUCTION · 1 TB RAM AT THIS STAGE Later expanded to 8 slots of 8-channel RAM — 192 logical cores running linear-regression and gradient-boosting workloads.

WHY OWN THE IRON

ITERATION

Experiments run until they're done.

When compute is metered, teams stop exploring early. Ours runs overnight, over weekends, over and over — hyperparameter sweeps included.

DATA CUSTODY

Your data stays in-house.

Sensitive datasets train on machines we physically control — not scattered across a multi-tenant cloud. A simpler answer for your security review.

ECONOMICS

Fixed cost, not a taxi meter.

Owned hardware means training cost doesn't scale with curiosity — savings that show up in your fixed bid, not our margin.

THE MACHINE

INSIDE A NODE · FAN WALL + CPU LOOP

400+

COMPUTE CORES

2 TB+

SYSTEM MEMORY

24/7

TRAINING UPTIME

GPU

ACCELERATED NODES · [CONFIRM SPECS]

WHAT RUNS HERE Vision model training & hyperparameter sweeps · custom embedding models · dataset preprocessing at scale · evaluation harnesses · the workloads behind Firmatek, Numin, Pilotly, and RecoveryTrek

UNDER LOAD · LIVE TELEMETRY

Watch it work.

The cluster's load monitor mid-run — CPU saturation across the cores, memory and swap, network throughput, temperatures, and disk. This is what a training night looks like.

CLUSTER LOAD MONITOR

CPU · MEMORY · THROUGHPUT · SWAP · TEMPERATURES · DISK — TELEMETRY ACROSS THE NODES

BUILT BY HAND, IN-HOUSE REAL BUILD PHOTOS · NO STOCK

Cluster parts delivered — DELIVERY DAY — NODES ARRIVE AS PARTS

Motherboard PCIe lanes and cooling — ON THE BOARD — PCIE LANES, COPPER, COOLING

NVMe scratch array — THE SCRATCH ARRAY — 8 TB NVME, BY THE HANDFUL

AND WHEN A JOB OUTGROWS THE ROOM

The cluster trains. The cloud bursts.

Owned iron isn't dogma — it's the default. When a workload needs more than the room holds, we scale out the way we did for Numin in 2019: hundreds of Google Cloud machines, spun up on demand, torn down when done. Your project gets whichever economics win.

THE SPLIT