ETERNApod Modular AI infrastructure · In development
ETERNA+PARTNERSETERNApod+MODULAR AI INFRASTRUCTUREHANSconstruction

DISTRIBUTED GPU INFRASTRUCTURE

Beyond a building.
A network.

Bringing AI compute to where power and fibre already exist. Small pods. One shared network. Growth, one proven step at a time.

Explore the vision

In development · First milestone: prototype validation

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MODULARITY / CONCEPT VISUALConceptual illustration of the distributed pod architecture.
01 / THE INITIATIVEPOWER × FIBRE × AI

01 / VISION

Compute power.
Built where it matters.

Built to grow across locations.

ETERNApod aims to use existing power and fibre at suitable residential, industrial and educational sites through modular GPU pods. The proposed architecture connects pods across locations into one centrally managed service network.

START SMALL. VALIDATE. SCALE.From a prototype to a pod fleet, then modular facilities and a long-term campus vision.

02 / OUR APPROACH

The parts behind
the whole.

One location. One pod. A shared network.
Explore the three components of the model.

PROPOSED SERVICE MODEL

GPU compute

Model serving, batch processing, rendering and suitable fine-tuning workloads for AI teams. Planned options include hourly GPU usage, reserved capacity, AI usage credits and storage.

Services are part of the development roadmap.

03 / HOW THE NETWORK WORKS

Distributed locations.
One coordinated system.

01

Workload

Compute requirements from AI teams and enterprise users.

02

Portal + API

A planned shared access layer for capacity requests, usage tracking and billing.

03

Orchestration

Software designed to route workloads to suitable pods and manage capacity and continuity.

04

Pod fleet

Modular GPU units across locations, operating under shared management.

This is the proposed architecture. Network and service performance will be validated through prototype and fleet testing.

04 / THE FIRST POD

Compact footprint.
Focused capacity.

Target configuration for Phase 1.
An engineering design to validate through testing.

4GPU

RTX PRO 6000 Blackwell

384GB

Combined GPU memory

4.9kW

Target peak power draw

24U

Steel cabinet form factor

96 TB raw storage + NVMe cacheDX cooling design≤50 dB noise target

Target values. Final hardware specifications depend on procurement and prototype testing. Raw storage differs from usable capacity.

05 / SCALING ROADMAP

From one pod
to what comes next.

Each stage validates the foundations for the next. The figures below represent phased development targets, not operational capacity.

FIRST VALIDATION MILESTONE

1POD

Prototype & field validation

Validate the first pod through a 72-hour load test, power consumption and noise measurements, installation checks and pilot use cases.

Priority: a repeatable, measurable first unit.

06 / WHO IT IS FOR

AI builders.
Space providers.

AI teams & enterprises

Flexible capacity for model serving, inference, rendering, batch processing and suitable fine-tuning applications.

Site owners

A fixed-rent hosting model for sites with suitable power, fibre and physical access. The operator is expected to cover power and internet costs.

Strategic partners & investors

Opportunities to participate in hardware, energy, locations and infrastructure as the development programme moves from prototype to scale.

07 / YOUR QUESTIONS

A clear perspective.

What stage is ETERNApod at today?

The initiative is in development. The first objective is prototype production and technical validation. Capacity, production timelines and growth figures on this site are targets, not an operational fleet or service guarantees.

Can a pod be installed at any location?

No. Power capacity, fibre connectivity, physical access, cooling conditions and required permissions must be assessed for each site. Hosting arrangements follow this suitability review.

How long do production and installation take?

The target is a 45-day standard cycle, or 30 days with all components in stock. These timelines depend on component availability and supply agreements; they are not guaranteed delivery times. Critical components that are out of stock can significantly extend the schedule.

Is the distributed network suitable for every AI workload?

The focus is inference, model serving, batch jobs and suitable fine-tuning applications. Due to latency between locations, large model training requiring tightly synchronised operation across thousands of GPUs is outside the initial distributed model’s focus.

Who is behind the initiative?

ETERNApod is being developed with the support of HANSconstruction and ETERNA+ Partners. HANSconstruction brings construction and delivery experience, while ETERNA+ Partners contributes its architectural and design approach to this infrastructure vision.

Veli Tunahan Bay, Owner and CEO of HANSconstruction and ETERNA+ Partners
ISTANBUL · LONDON

08 / A NOTE FROM OUR CEO

Thinking ahead.
Building responsibly.

“A bold vision earns trust by proving itself in small steps. With ETERNApod, our ambition is to bring engineering, design and sound management together to develop scalable AI infrastructure. Validate first, then grow. For me, that is where lasting value begins.”

Veli Tunahan Bay

Owner & CEO
HANSconstruction · ETERNA+ Partners

Bringing together a background in civil engineering, architecture and construction management, Veli Tunahan Bay has led HANSconstruction and ETERNA+ Partners since 2019.

09 / CONTACT

Let’s take the next step
together.

Discuss a pilot, a host partnership or an investment opportunity.
Istanbul / Maslak · London / Mayfair, W1J 6BD