What does BtMData do?

BtMData transforms industrial and energy assets into viable data centre projects: site assessment, energy and regulatory structuring, and ongoing management with the GridPass platform.

What is BtMData GridPass?

GridPass is the energy and compliance platform for data centres: regulatory cockpit with hourly renewable matching on measured telemetry, energy stack design with dedicated renewables, BESS and hourly PPAs, metered PUE/WUE and permitting with cost of delay.

What is Smart Power Cap?

Smart Power Cap is the demand management module: dynamic power capping and shifting of flexible load into renewable-rich hours, with verified savings in euros and CO2 measured from real server telemetry.

Which regulation does GridPass address?

Spain's upcoming data centre regulatory framework, still in the approval process, including hourly correlation with renewable generation, additionality, PUE and WUE reporting, and CNMC filings.

All models

OpenAI · 2024

Sora: training infrastructure breakdown

Name: BtMData GridPass
Brand: BtMData

Text-to-video at frontier quality. The most data-intensive training task in production.

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Training infrastructure profile

Public estimates. Not endorsed by the model vendor.

Parameters

Estimated ~3–10B (diffusion transformer)

Training tokens

Petabytes of licensed + synthetic video

FLOPs

~3e24 – 1e25 FLOPs (estimated)

Hardware

~10,000+ NVIDIA H100

GPU-hours

~10M – 30M GPU-hours

Estimated cost

$50M – $200M (training + data pipeline)

Cluster power

~10–15 MW (peak training)

Sources: OpenAI Sora technical paper (2024), Epoch AI video model analysis.

What this means for your AI infrastructure plan

Training a model in the same league as Sora requires a dedicated AI factory: thousands of accelerators, megawatts of firmed power, low-PUE cooling, sovereign data residency and a multi-year financing structure. Below is how we translate the public profile above into a deployable plan.

Cluster sizing

We size GPU/TPU equivalents to your target FLOPs, accounting for MFU (Model FLOPs Utilization), checkpointing overhead and fault tolerance.

Power & cooling

From the ~10–15 MW (peak training) reference, we derive substation requirements, PUE targets and cooling architecture (DLC, immersion, hybrid).

CapEx + OpEx

Full 20-year financial model: hardware, real estate, energy contracts, staffing, refresh cycles. Stress-tested against energy price scenarios.

Sovereign deployment

EU residency, NIS2 / GDPR alignment, optional air-gapped operation. No US CLOUD Act exposure.

Why this matters

Planning a model at Sora-class scale?

Talk to our infrastructure team. We size GPU clusters, power, cooling and financing for frontier-scale training and inference workloads — on sovereign EU infrastructure.

GPU cluster sizing (H100 / H200 / B200) for your target FLOPs
Power, cooling and PUE modelling for 5–150 MW deployments
CapEx + 20-year OpEx scenario, financing pathway
Site selection across MAD1 (Madrid) + Iberian greenfield + brownfield assets

Public estimates for Sora indicate the cluster scale, capex and energy footprint required to train a peer model. We translate that into a deployable plan on EU-sovereign infrastructure.

Other frontier models

OpenAI · 2025

GPT-5

The frontier reasoning model. Training a peer requires a hyperscale AI factory.

OpenAI · 2023

GPT-4

The model that defined the GenAI era. A blueprint for what a national AI Factory must deliver.

Google DeepMind · 2024

Gemini Ultra

Multimodal frontier from Google. Native vision + audio + code training.

Anthropic · 2025

Claude 4 (Opus)

Constitutional AI at frontier scale. Optimized for reasoning + safety.

Meta AI · 2025

Llama 4

Open-weights frontier. The reference stack for sovereign AI factories.

DeepSeek · 2024

DeepSeek V3

The efficiency benchmark. Proves frontier-class quality is achievable on 1/30 of GPT-4's budget.

Skip estimates. Get a real sizing.

We have done this for hyperscale and sovereign customers in Iberia. 24h response.

Plan a peer model

All figures on this page are public estimates compiled from third-party research (Epoch AI, SemiAnalysis, MLPerf, vendor press releases) and academic literature. We do not represent the model vendors, and vendors do not endorse these numbers. Use as order-of-magnitude reference only.