GPU AI Servers With NVIDIA Power You Own
The horsepower behind private AI is the GPU. We build NVIDIA-based servers — single card to multi-GPU and DGX-class — sized to the models you actually run, then install and support them here in Texas. You own the silicon, so there are no rental clocks and no per-hour meters.
Sized to your models
We match GPU memory and count to the models you plan to run — no guessing, no overspending on cards you will not use.
Built for uptime
Server-grade power, cooling and ECC memory so inference keeps running through the workday, not a desktop pushed past its limits.
Owned, not rented
Cloud GPUs bill by the hour forever. Your server is a one-time number, and then the compute is simply yours.
GPU server questions
How many GPUs does my business actually need?+
It depends on the model size and how many people use it at once. We size the GPU count to your real workload — one card is plenty for many teams; heavier inference or larger models call for multi-GPU.
Which NVIDIA GPUs do you build with?+
We spec the right card for the job — workstation/pro GPUs through data-center class — based on the models you want to run and your power and budget, not a fixed catalog tier.
Can you add more GPUs later?+
Yes. We build with headroom — power, cooling and slots — so you can add GPUs as your usage grows instead of buying a whole new machine.
Is the RTX PRO 6000 Blackwell really competitive with an H100?+
For single-GPU inference, yes — a 96GB RTX PRO 6000 runs a 70B-class model on one card and competes well at a fraction of the price. Where the H100 pulls ahead is multi-GPU work that uses NVLink for tensor parallelism on very large models or training, which is rare for a small business. See the full GPU comparison for the numbers.
Back to AI Servers · compare with an AI workstation · see cost vs. cloud.
What tokens/sec actually means for your team
Tokens per second is just how fast the model produces text — higher feels snappier. The thing most people get wrong is what limits it: inference speed is mostly bound by GPU memory bandwidth, not raw clock speed. That is why VRAM and the card's bandwidth matter more than a headline core count.
Realistic ranges help set expectations. On a current card an 8B model can reach into the triple digits in tokens/sec, while a 70B model at Q4 runs slower but stays very usable for chat and drafting. Treat every number as a range — actual speed depends on the model, quantization, context length, and how many requests are batched together. We tune the build so the models you actually run feel responsive on your network.
NVLink vs PCIe for multi-GPU
When you run more than one GPU, the cards have to talk to each other, and there are two ways they do it. NVLink is NVIDIA's high-speed GPU-to-GPU link, and it matters most when you split one very large model across several data-center cards with tensor parallelism — the GPUs are constantly exchanging data, so the fast interconnect pays off.
For most business builds, that case never arrives. If you are serving several independent models or one model that fits on a single card, PCIe 5.0 is plenty — the GPUs work largely on their own and do not need the NVLink firehose between them. We are honest about which one your workload calls for; see the full GPU comparison for where each card fits.
GPU options for an AI server
A condensed look at the cards we build with. For full price ranges, power draw, and the new-vs-used tradeoffs, see the complete GPU comparison.
| GPU | VRAM | Best for |
|---|---|---|
| RTX PRO 6000 Blackwell | 96GB | One-card 70B inference; the SMB sweet spot |
| RTX 5090 | 32GB | Smaller 8B–32B models, tight budgets |
| RTX 4090 | 24GB | Entry builds, 8B–13B-class models |
| A100 80GB (used) | 80GB | Multi-GPU/NVLink work if condition checks out |
| H100 80GB | 80GB | Large-model training, dense multi-GPU |
VRAM is the spec that sets the largest model a card can run. Used-card availability and pricing are volatile — verify at quote.
Let’s spec the right GPU server
Tell us the models you want to run and how many people will use them — we’ll size a GPU server you own outright.