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Private inference · European infrastructure

Private AI inference on dedicated European infrastructure.

Deploy open-source models behind a private API, on physically dedicated GPU capacity, with a known operator, controlled data location and no dependency on shared model APIs.

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TRL 5Current laboratory prototype
INFERENCE PATHCONTROLLED
01Enterprise systems
02Private API
03Dedicated inference environment
04Dedicated GPU infrastructure
05Named European data centre
AUTHENTICATEDSINGLE TENANTKNOWN LOCATION
01 · Infrastructure choices

Enterprise AI should not require surrendering control.

Every infrastructure model makes a different trade-off. INFERENC is being developed for organisations that need a practical route to dedicated capacity without building a GPU operations team.

01

Shared model APIs

  • External processing
  • Limited infrastructure visibility
  • Shared service model
  • Changing pricing and platform dependency
02

Hyperscale dedicated infrastructure

  • Mature controls
  • High cost
  • Complex configuration
  • Significant vendor lock-in
03

Internal GPU cluster

  • Full control
  • High CAPEX
  • Specialist staffing
  • Utilisation and maintenance risk
04

INFERENC

  • Dedicated and managed
  • Known operator
  • Open-source models
  • Private API
  • European infrastructure roadmap
Current laboratory prototype

A working foundation for private, multi-GPU inference.

The current laboratory system validates the core technical path. It is not presented as a production data centre or an enterprise SLA.

TRL5
GPU configuration2× NVIDIA RTX PRO 6000 Blackwell
Combined GPU memory≈192 GB
Validated model≈284B parameter MoE
Validated today
✓Multi-GPU inference
✓Private local API
✓Locally controlled inference server
✓No external model API required
02 · Operational control

Designed for control

A deployment model that makes the operational chain visible and reviewable.

01

Dedicated GPU capacity

02

Private model endpoint

03

Known infrastructure operator

04

Customer-defined retention

05

Controlled administrative access

06

Open-source model portability

07

Predictable capacity

08

Enterprise evidence roadmap

03 · R&D

Research-driven efficiency

We are investigating quantisation, continuous batching, KV-cache management, multi-GPU partitioning, long-context serving, speculative decoding and energy-aware optimisation.

Research→
The objective is not to run a model at any cost. The objective is to identify the lowest-cost configuration that satisfies the customer’s requirements for quality, latency, context length and security.
Private deployment

Planning a private AI deployment?

Tell us about the model, workload, data sensitivity and expected traffic. We will assess the required GPU configuration and deployment model.

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