The Zoneits Lab.
Our own proving ground – GPU rigs running real AI workloads, a hardware bench where network, wireless, and VoIP gear gets stress-tested, and a software team building the tools we ship to clients. We validate before we recommend, benchmark before we deploy, and de-risk every rollout on infrastructure we actually own. Built, not just bought.
Prove it before production.
A recommendation is only as good as the evidence behind it. So we keep a working proving ground – the same idea as the test tracks and validation grounds that serious builders use before anything ships. New silicon, a different fabric, a rival wireless radio, an AI model we are considering fine-tuning: it runs here first, on real infrastructure, measured against a baseline. When we tell a client something works, it is because we watched it work. That is how you de-risk a deployment instead of hoping.
- ✓Real hardware, real load. GPU rigs pushed to full utilisation, not spec-sheet promises.
- ✓Multi-vendor bake-offs. We put competing gear side by side and let the numbers decide.
- ✓Evidence you can see. Benchmarks, validation runs, and proof-of-concept results before you commit.
Illustrative utilisation curve for a benchmark training run on the Lab cluster. Figures are representative.
| Layer | What we run | Why it matters |
|---|---|---|
| Accelerators | NVIDIA H100 & B200 | Current-generation silicon for training and inference workloads |
| Scale-up domain | NVLink | Binds GPUs into a single high-bandwidth memory domain |
| Scale-out fabric | InfiniBand NDR | Low-latency 400 Gb/s interconnect for multi-node jobs |
| Cooling | Direct-to-chip liquid | Keeps dense racks at full clocks without throttling |
Representative Lab configuration. Exact hardware rotates as we validate new gear.
A working proving ground.
Where ideas earn their place.
Compute, hardware, and software under one roof – so every recommendation we make has already been proven on our own infrastructure.
AI proving ground
Our own GPU cluster is where we fine-tune, benchmark, and validate AI models before they touch a client workload. We measure throughput, utilisation, and cost per token on real hardware, then build the agents and pipelines that put those models to work with proper governance. When we recommend an approach, it has already run here.
Hardware bench
Switches, routers, wireless radios, and VoIP gear get racked, loaded, and measured before we spec them into a client design. We test real throughput, range, and failover – not the datasheet.
In-house software products
A dedicated software team builds the monitoring, automation, and integration tools we deploy for clients – dogfooded internally before they ship.
Proof-of-concept as a service
Not sure a design will hold up? We will build a working proof of concept on Lab infrastructure – a scaled model of your deployment, benchmarked and documented – so you make the call on evidence instead of a vendor pitch. It is the fastest way to de-risk a large commitment.
Benchmarking & validation
Repeatable, documented tests against a baseline – so performance claims are numbers you can check, not adjectives.
Multi-vendor bake-offs
We run competing products head to head on identical workloads and pick the winner on merit – no default vendor, no assumptions.
From idea to production.
Frame the question
A client goal, a new technology, or a design we want to pressure-test becomes a clear hypothesis with a measurable target.
Build it for real
We stand up a working model on Lab hardware – GPU rigs, bench gear, or code – configured the way it would actually run.
Measure against a baseline
Repeatable tests capture throughput, utilisation, latency, and cost, with competing options run side by side.
Ship what wins
The proven configuration graduates into a client deployment or a shipped product, documented and de-risked.
The Lab, answered.
What actually is the Zoneits Lab?
Why does an IT services company run its own lab?
Can you build a proof of concept for our project?
How do multi-vendor bake-offs work?
What runs on the Lab GPU cluster?
Bring us the hard part.
We will prove it out.
Whether it is an AI model to benchmark, competing hardware to compare, or a deployment you need de-risked, the Lab turns the question into evidence. Tell us what you are trying to prove.