S-01
Infrastructure design
Sizing the site and the cluster before anything is ordered.
- Power, cooling and floor-loading assessment
- Rack layout, thermal and power maps
- Fabric topology and cable plan
AI Infrastructure · Europe
We build and commission GPU clusters for AI
GTVA takes AI compute infrastructure from an empty rack to a production-ready cluster — site engineering, installation, InfiniBand fabric, validation and ongoing operation. Built in Europe, under European jurisdiction, for training and inference at scale.
up to5,040
GPUs per project
800 Gb/s
InfiniBand fabric class
25–35
Engineers on site at peak
96 h
Acceptance burn-in
01Services
From design to production
A single team across the full lifecycle of AI infrastructure — so responsibility for the result sits in one place, from the power plan to the running model.
S-02
Supply & installation
Getting the hardware racked and wired to plan.
- Goods-in inspection and serial reconciliation
- Rack and stack, structured cabling
- Per-link verification during installation
S-03
Commissioning & validation
Proving the cluster works before it goes into production.
- Firmware alignment to a compatibility matrix
- Fabric, node and collective-bandwidth testing
- Acceptance against measurable criteria
S-04
Operation & support
Keeping the cluster healthy over its life.
- 24/7 monitoring and alerting on contract
- Component replacement and RMA coordination
- Firmware and configuration management
- Monitoring for performance degradation, not only outages
S-05
Model training & optimisation
Turning raw compute into throughput.
- Distributed training setup and tuning
- GPU utilisation and scaling analysis
- Framework and pipeline optimisation
S-06
AI development
Applied models on top of the infrastructure.
- Model integration and deployment
- Inference serving at scale
- Custom AI solutions
02Process
How an engagement runs
A predictable path from first conversation to a cluster signed into production.
-
01
Assessment
We survey the site and the bill of materials, confirm power, cooling and delivery timelines, and agree the acceptance criteria in writing.
-
02
Installation
Goods-in, racking, power and structured cabling, with every link labelled and verified as it is laid.
-
03
Bring-up
Inventory, firmware alignment to the agreed baseline, automated OS and driver rollout, storage and scheduler.
-
04
Validation
Per-node diagnostics, fabric verification, collective-bandwidth scaling, 96-hour burn-in and a real training run.
-
05
Handover
Acceptance protocol against the agreed figures, health checks, monitoring and full documentation.
03Acceptance criteria
A cluster is done when the numbers say so
Acceptance is defined in figures before installation begins — so “working” is something both sides can measure, not argue about.
| Criterion | Metric | Target | Method |
|---|---|---|---|
| Node health | Diagnostics pass rate | ≥ agreed % | NVIDIA DCGM Diagnostics |
| Fabric integrity | Link errors after burn-in | none above threshold | ibdiagnet, mlxlink |
| Collective bandwidth | all-reduce at full scale | ≤ agreed degradation | NCCL tests, 2→full scale |
| GPU utilisation | MFU on reference run | ≥ agreed % | Megatron / NeMo run |
| Thermal stability | Throttling under load | none over 96 h | telemetry, per-rack |
| Fault recovery | Restart after node loss | ≤ agreed time | checkpoint restart test |
04Insights
Notes from the field
What we run into commissioning large GPU clusters — and how we deal with it. Written for the people who have to make the hardware work.
Commissioning
Lemon nodes: why a green health check doesn’t mean a GPU server is ready
A failure pattern that only shows up once you’re running real workloads at scale, and it’s more expensive than the dramatic ones. The costly node is the one that looks fine every time you check it and fails every time you actually use it.
Field notes
Common mistakes first-time AI builders make — and what they cost
A team raises money, orders a few hundred GPU servers, and pencils in a go-live date a week after the trucks arrive. The hardware almost never lets them down — the assumptions around it do.
Field notes
The failure that doesn’t crash anything: fail-slow in large GPU clusters
Everyone plans for the node that dies — it’s visible, it’s loud, and you replace the hardware and move on. The failure that actually eats your budget is quieter: the node that keeps working but works slowly.
Compliance
Your GPUs are American even when you aren’t: export control for AI clusters in Europe
There’s a comfortable assumption behind a lot of cross-border AI infrastructure: we’re not a US company, the cluster isn’t on US soil, so US export rules are someone else’s problem. When it’s wrong, it surfaces as a seized shipment rather than a polite letter.
05Team
Who does the work
A team drawn from data-centre operations, high-performance networking and EU compliance — the mix a project at this scale actually needs.
InfiniBand fabric engineers
Design and validation of large high-speed fabrics, up to 800 Gb/s class.
Commissioning engineers
Bring-up, diagnostics and burn-in across thousands of GPUs.
Project lead
Single point of ownership from assessment to signed handover.
Compliance & DPO
Export control, GDPR and NIS2 across the engagement.
06Europe
Built and hosted in Europe
The infrastructure sits in the EU, under European jurisdiction. For regulated and cross-border workloads that decides where the data lives, who can touch it, and how procurement is controlled.
- Jurisdiction European Union
- Data centre locations Czech Republic / EU
- Data residency EU only
- Compliance GDPR, NIS2
- Support European business hours + 24/7 on contract
Tell us about your cluster
Send the scope and constraints; we reply with an honest timeline and the questions that actually matter before anyone signs.