Local hardware. Your data. No cloud surveillance.
We design, build, and host private AI servers for businesses that can't put their customer data into someone else's cloud. Healthcare, legal, finance, and any operator who'd rather own the box than rent it by the minute.
There's no one right AI server. The right one depends on what you're running, who's allowed to see the data, and how much you need to spend. Here's the lineup we deploy from.
Datacenter inference at scale
The H200 is what you put in a rack when "AI" stops being a side project and becomes how the business runs. 141 GB of HBM3e memory means you can host a 70-billion-parameter model with headroom to spare. Throughput is high enough to serve a whole department from one box.
Best for: production inference for 50+ concurrent users, large-model fine-tunes, RAG over millions of documents.
We deploy these for businesses that have outgrown cloud GPU bills.
Active development
Two RTX 5090s in one workstation gives you 64 GB of fast GDDR7 memory. That's enough for medium-sized model training, comfortable RAG inference for a small team, and the kind of "let's try something" experimentation that's painful to do in the cloud.
Best for: development teams, model fine-tuning, multi-user inference for under 20 people, on-prem RAG.
This is our most-recommended workstation for Texas SMBs starting their first private-AI deployment.
Production inference + light fine-tune
The RTX 6000 family (Ada / Blackwell generations) trades raw consumer speed for ECC memory and rock-solid reliability. 96 GB on a single card means most useful open models load in one piece, and the workstation runs quietly enough for an office.
Best for: inference under sustained load, light fine-tune work, 24/7 uptime in a non-datacenter environment.
We deploy these for businesses that want one box to handle everything reliably.
Quiet office inference
Mac Studios with M-series Ultra chips share unified memory across CPU and GPU — up to 512 GB on the top tier. Cluster two or three of them and you have a silent, low-power AI server that fits under a desk and never warms up the room.
Best for: small teams, sub-30-billion-parameter models, offices without server-room HVAC, businesses prioritizing power draw and noise.
Surprisingly capable for the price. Not the right pick if you need raw GPU throughput.
Most days, your local server handles every query. On the days it doesn't — month-end batch jobs, a marketing campaign that ten-x's traffic, an unexpected spike — we configure your stack to burst into rented GPU capacity over an encrypted link and pull back to local when load drops.
You pay cloud only when you actually need it. Your customer data never touches the cloud unless you explicitly route a workload there. The local box is the default; cloud is the safety valve.
Three reasons we hear, in order:
The case for cloud AI: low upfront cost, fast experimentation, no operations team needed. We sell hardware to businesses that have already passed the experiment stage and want what they've built to last.
The hardware alone is six figures. Total project cost depends on integration, model selection, and ongoing support. We quote per-engagement after a discovery call.
In your building, in a colocation facility, or in our Houston operations space — your call. We've deployed all three configurations.
Usually yes. The hard part is matching model behavior, not the migration itself. We start with a paid pilot to verify equivalence before recommending the move.
No. The Mac Studio cluster is our preferred quiet-office build, and we're tracking AMD MI-series for clients with specific compatibility requirements.
Tell us a little about what you're trying to do. We'll respond within one business day with whether private AI fits — and if it doesn't, we'll say so.