On-Prem GPU & Model Hosting
“Your Models, Your Hardware, Your Data”
Sometimes the right place for your models is your own building. We design, source, rack, and tune local GPU infrastructure, from a single Mac Studio to a rack of NVIDIA accelerators, so you can run open models in-house with full data residency, predictable cost, and no per-token cloud bill. We right-size the hardware to your models and stand up an OpenAI-compatible inference stack your apps can talk to unchanged.
When the per-token meter never stops.
Per-token cloud pricing is convenient until it isn't. For steady, high-volume inference the meter never stops, and a workload that made sense as a pilot can quietly become one of your largest recurring bills. Meanwhile your most sensitive data is leaving the building to be processed on someone else's hardware.
For a lot of teams the answer is to bring inference in-house, but the hardware question is genuinely hard. Which accelerators, how much memory, what throughput, how much power and cooling, which software stack? Get it wrong and you have either overspent on idle silicon or built something that can't keep up. This is as much a data-center problem as an AI one.
Owned capacity, sized to your workload.
We right-size to your actual workload first. Starting from your models, context sizes, throughput, and concurrency targets, we work out exactly what hardware you need, whether that is a Mac Studio M3 Ultra on a desk, a unified-memory cluster, or a rack of H100s, so you buy capacity you will use rather than specs that look impressive.
Then we handle the parts most AI teams have never had to: power, cooling, airflow, and rack layout for an office or colocation, and an inference stack, Ollama, vLLM, or TGI, behind OpenAI-compatible APIs so your existing apps point at it without code changes. Where you need it, we build air-gapped with compliance-grade audit and full data residency.
Everything you need, engineered to production standards.
We design, source, rack, and tune local GPU infrastructure so you can run open models in-house, with full data residency, predictable cost, and no per-token cloud bill. From a single Mac Studio to a rack of NVIDIA accelerators.
- Apple Silicon builds: Mac Studio M3 Ultra and unified-memory clustering (EXO) for large-context open models
- NVIDIA workstation & rackmount servers: RTX 5090, L40S, H100/H200 in 1U to 5U chassis (BIZON / Premio-class)
- Right-sizing to your models: VRAM / unified memory, throughput (tokens/s), and concurrency targets
- Power, cooling, airflow, and short-depth rack layout for office or colocation
- On-prem inference stack: Ollama, vLLM, and TGI behind OpenAI-compatible APIs
- Air-gapped options with compliance-grade audit, access control, and data residency
“Private, owned AI capacity at a fraction of recurring cloud spend”
We engineer for production from day one, then transfer ownership so the capability stays with your team.
Built for the problems you're actually facing.
- 01 Private inference for sensitive or regulated data
- 02 Replacing a growing per-token cloud bill with owned capacity
- 03 Air-gapped deployments with full data residency
- 04 Right-sizing hardware to your models' throughput and concurrency
The stack we reach for.
Battle-tested tools, chosen to fit your team and constraints, never technology for its own sake.
- Mac Studio M3 Ultra
- NVIDIA H100 / H200
- RTX 5090 / L40S
- Ollama
- vLLM
- TGI
- EXO
Why teams pick AzeniQ for this.
Right-sized to your models, not a catalog
We size hardware from your real throughput and concurrency numbers, so you don't overspend on idle silicon or under-build and choke under load.
The full stack, silicon to API
Power, cooling, racking, and an OpenAI-compatible inference layer, we handle the data-center parts most AI teams have never had to touch.
Drop-in for your existing apps
OpenAI-compatible endpoints mean your code points at your own hardware with a change of URL, no rewrite required to leave the cloud.
Engagements designed to leave you stronger.
Every service follows the same disciplined path: de-risk fast, engineer for production, then transfer ownership.
Frame & de-risk
We pressure-test the goal, define measurable outcomes, and ship a focused proof-of-concept fast.
Engineer to production
Hardened, observable, cost-aware systems built on AWS/Azure/GCP with security by default.
Transfer & scale
We embed the practices and mentor your team so the capability stays in-house.
Answers before you ask.
When does on-prem actually beat the cloud on cost?
When inference is steady and high-volume. For bursty or low-volume workloads the cloud often wins; for a constant load, owned hardware usually pays back quickly. We model your specific usage before recommending either.
How do I know what hardware I need?
We size it from your models and targets, VRAM or unified memory, tokens per second, and how many concurrent users, so the build matches the workload rather than a spec sheet.
Will our existing applications work with it?
Yes. We serve models behind OpenAI-compatible APIs, so apps written against the OpenAI SDK point at your hardware with a change of URL, no rewrite needed.
Can it be fully air-gapped?
Yes. For regulated or sensitive workloads we build air-gapped deployments with full data residency, access control, and compliance-grade audit logging.
More of what we do.
Ready to Build The Future Together?
Tell us where you're headed. We'll map the fastest secure path from idea to production.