GPU Cluster Systems
High-density GPU compute, sized, racked, and configured for training, fine-tuning, and inference workloads.
InfrastructurePrivate AI, properly engineered.
Lucrion designs and operates on-premise AI infrastructure for European organisations that need performance, privacy, and control, without the public-cloud bill or the data-sovereignty risk.
What makes Lucrion defensible
A five-way intersection few providers occupy.
Plenty of firms do one of these. Lucrion is built where all five meet, and that intersection is where on-premise AI becomes genuinely worth owning.
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The air-gap
Systems that can run with no external network path. Inference, weights, and logs never leave the perimeter you control.
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Austrian domiciliation + EU AI Act fluency
An EU-domiciled engagement that designs around the AI Act, GDPR, DORA, and NIS2 from the first architecture sketch, not retrofitted later.
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Mid-market pricing
Engagements scoped between €35k and €120k. Serious on-premise capability without hyperscaler-tier consulting fees.
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Operations continuity
We hand over a system your team owns, with runbooks, monitoring, and a clear backup-and-restore path. No lock-in to keep it running.
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Open-source, open-weights
Built on open infrastructure and open-weights models: vLLM, Qdrant, Keycloak, Linux. No proprietary black boxes, no vendor lock-in.
What we build
Systems designed for on-premise AI operations.
Private Inference
Isolated inference stacks that run on your hardware. No external APIs, no shared compute, no data leaving your network.
PrivacySecure Architecture
Network segmentation, identity, encrypted model storage, and audit logging applied at every layer.
SecurityModel Deployment
Open-weights models served with vLLM or SGLang, installed, benchmarked, and handed over with documentation.
OperationsEnterprise Integration
Integration with your existing infrastructure, identity providers, and operational workflows.
IntegrationOperations Support
Monitoring, model updates, capacity planning, and infrastructure support as your AI operations scale.
SupportWhere to start
Three concrete ways to begin.
Most engagements start with an assessment, then a first production system. Indicative price bands and durations are published up front, with no discovery call required to learn them.
Readiness Assessment
A fixed-scope engagement to decide whether on-premise AI makes sense for you: reference architecture, hardware BoM, compliance map, and a 3-year TCO against public-cloud LLMs.
- Use-case matrix + data inventory
- Hardware BoM with three price points
- Go / no-go recommendation
Private AI Starter
One hardened on-premise GPU server with identity, ingress, an open-weights LLM served via vLLM or SGLang, an internal chat UI, monitoring, backup, and a one-day training session.
- Open-weights LLM on your hardware
- Identity, monitoring, and backup
- Witnessed restore + runbook handover
Enterprise RAG
Retrieval-augmented generation over your private documents, with access control, citation enforcement, audit logging, and a measured precision benchmark agreed up front.
- Citations enforced on every answer
- Access control + audit logging
- ≥80% precision on an agreed benchmark
Need something outside these three? See all services →
Why private
Why private AI infrastructure
is no longer optional.
Control is not a feature. It's the foundation.
Every inference, every model weight, every log stays inside your perimeter. No shared cloud, no third-party processing, no data-residency uncertainty.
Designed around the regulations you answer to.
GDPR, the EU AI Act, DORA, and NIS2, designed for from day one, with audit trails and access controls at every layer. We document the posture; we do not claim certifications we do not hold.
No shared resources. No throttling.
On-premise GPU clusters deliver predictable, deterministic response times, with no shared compute contention and no unexplained cloud throttling.
How we work
From requirements to production infrastructure.
- Discover
- Design
- Deploy
- Operate
Reference deployment
The rig that proved the playbook.
Our first reference deployment runs a financial-grade workload, portfolio analytics for an associated venture, on a hardened on-premise rig: FreeIPA and Keycloak identity, HAProxy/Keepalived high availability, NetBox inventory, MLflow, NVIDIA GPU with DCGM monitoring, ZFS on TrueNAS, and encrypted offsite backup. It is engineered to financial-grade standards, and it is where the Lucrion playbook was first proven end to end.
It is a reference deployment running an associated venture's workload, not a customer reference. We frame it honestly because the engineering is what matters.
Trust & compliance
Built for environments where
trust has to be earned in the design.
Air-Gap Capable
Systems designed to operate with no external network access, fully isolated from the public internet.
Encryption In Transit & At Rest
Data encrypted across the stack: storage volumes, inference endpoints, and model weights.
Identity At Every Boundary
Keycloak and FreeIPA identity with authentication and authorization between services. No implicit trust.
Audit-By-Design
Audit logging and access records built in from the architecture phase, designed for EU regulated review.
Get Started
Your data. Your infrastructure. Your control.
Start with a Readiness Assessment, a fixed-scope engagement to decide whether on-premise AI makes sense for your organisation, before any hardware is procured.