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BAYWAWData Center

Solutions by sector

Sovereign AI infrastructure, on your own ground.

From national AI programs to telecom edge sites and remote industrial assets, AI compute increasingly has to run where the data, the users or the power are. The BAYWAW AI Container brings 1.2 MW of on-premise AI infrastructure to your site in a standard 40ft container, operational in as little as four months.

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The common platform

One platform, six missions.

Every deployment starts from the same AI data center: 624 NVIDIA B300 GPUs in six single-phase immersion tanks, pre-integrated, tested and validated in the UAE, then shipped as a standard ISO container. What changes from one sector to the next is the site, the operating model and the workloads it runs.

NVIDIA B300
624GPUs
Total GPU memory (HBM3e)
~179TB
Total facility power
1.2MW
Power usage effectiveness
1.05PUE

01Governments & public sector

AI infrastructure for government, under national jurisdiction.

National AI strategies depend on compute the state actually controls. Sovereign AI infrastructure means the hardware, the data and the models stay on national soil, under national law.

Sovereign deployment schematicData and models flow only into the BAYWAW AI Container, which sits inside the national jurisdiction boundary. The path to an offshore cloud is closed.National jurisdictionDataModelsBAYWAW AI ContainerOffshorecloud
Fig. 01 — Data and models stay inside the national boundary

The challenge

Public-sector AI runs on sensitive data: citizen records, health, security, critical infrastructure. Sending it to offshore cloud regions creates legal exposure and strategic dependency, yet building a national AI data center from scratch is typically a multi‑year project.

How the AI Container answers

  1. Data and models stay in-country

    The container is installed on a site you choose, under your jurisdiction. Training data, model weights and inference traffic never need to leave national territory.

  2. Clear control over access

    Next-generation firewalls, a separate out-of-band management network and a dedicated network segment for cameras and sensors give your security teams clear boundaries to enforce.

  3. National capacity in months, not years

    Built, integrated and tested in the UAE, the container can be operational in as little as four months, then extended with additional units as the program grows.

Typical workloads

  • National-language LLMs
  • Citizen-service assistants
  • Document and records analysis
  • Health and public-research data
  • Public-safety and smart-city analytics
Discuss a sovereign deploymentRelated readingInside the AI Container: compute, network and security

02Enterprises & AI labs

On-premise AI infrastructure for your proprietary data.

For enterprises and AI labs, the model is the product and the data is the moat. On-premise AI infrastructure keeps both inside your own perimeter, with capacity you plan rather than queue for.

Private AI deployment schematicProprietary data feeds the AI Container inside your private network, where training, fine-tuning and inference run on dedicated capacity of 624 GPUs.Your private networkProprietarydataAI ContainerTrainingFine-tuningInferenceDedicated capacity · 624 GPUs
Fig. 02 — Training, fine-tuning and inference inside your perimeter

The challenge

Shared cloud GPU capacity can be hard to reserve at scale, difficult to forecast in cost and located outside your security perimeter. For teams training and serving models on proprietary data, that combination slows roadmaps and complicates compliance.

How the AI Container answers

  1. Dedicated capacity you can plan around

    624 NVIDIA B300 GPUs with 288 GB of HBM3e each, reserved for your teams alone. Capacity is known in advance, so training schedules and product launches can be planned with confidence.

  2. Your data stays inside your perimeter

    Training, fine-tuning and inference run on your site, behind your firewalls. Proprietary datasets and model weights remain under your control.

  3. Built for large-scale training

    A rail-optimized, non-blocking GPU fabric with eight 400G east-west links per server keeps multi-node training jobs supplied with data.

Typical workloads

  • Foundation-model training
  • Fine-tuning on proprietary data
  • Retrieval-augmented generation (RAG)
  • High-throughput inference
  • Agentic and multimodal workloads
Request a proposal for private AI capacityRelated readingHow immersion cooling reaches a PUE of 1.05

03Telecom & edge operators

An edge AI data center, close to your users.

Real-time AI services depend on latency as much as on raw compute. An edge AI data center places inference next to subscribers and devices, at the network sites you already operate.

Edge deployment schematicThree AI Containers at edge sites serve nearby users and devices with low-latency inference, and connect to the network core through backhaul links.Network coreEdge siteLow-latency inferenceUsers and devicesBackhaul to the core
Fig. 03 — Inference at the network edge, backhaul to the core

The challenge

Centralized AI regions can sit far from where traffic originates. Every round trip to a distant data center adds latency and backhaul load, and conventional facilities are rarely an option at the edge of a network.

How the AI Container answers

  1. Deploys at existing network sites

    As a standard 40ft ISO container, it travels by road or sea and is built to withstand transport and harsh environments, so it can be placed at aggregation sites or other network locations with the space and power to host it.

  2. Redundant, multi‑site connectivity

    Two 400G north-south links per server, a front-end spine of six Cisco Nexus 9364 switches and redundant edge routers running BGP for high availability and multi-site operation.

  3. Operated remotely, around the clock

    With BAYWAW managed services, distributed sites are monitored 24/7/365, with follow-the-sun support and an initial response in under 15 minutes.

Typical workloads

  • Real-time inference for subscribers
  • Network optimization and AIOps
  • Video analytics at the edge
  • Voice and language services
  • GPU services for business customers
Book an edge deployment briefingRelated readingHow we monitor and maintain distributed sites

04Energy, mining & industry

Put AI compute where the power is.

In many markets, grid connections have become a bottleneck for new data centers. Energy, mining and industrial operators often face the opposite situation: power and land on site, with no AI infrastructure to put them to work.

Power path schematicAn optional solar farm charges a battery energy storage system, which feeds two power modules, one active and one on standby, supplying the 1.2 MW AI Container. The grid or on-site generation can feed the power modules instead.Grid or on-site generationActiveStandbySolar farmBESSPower modulesAI ContainerN+2 · <10 ms1.2 MWOptional solar + BESS1.2 MW continuous ≈ 10.5 GWh per year
Fig. 04 — From solar farm to silicon

The challenge

Remote assets generate large volumes of operational data from sensors, cameras and drones, far from any data center. Moving that data out is slow and costly, and conventional data centers are rarely built where the energy is produced.

How the AI Container answers

  1. Energy-agnostic by design

    The container connects to the grid, to on-site generation or to a dedicated solar farm with battery energy storage (BESS), through two power modules: one active at full capacity, one on standby.

  2. A resilient power path

    N+2 redundancy and failover in under 10 ms protect running workloads. Total facility power is 1.2 MW, about 10.5 GWh a year at continuous load.

  3. Built for harsh environments

    Servers run immersed in dielectric liquid, with roof-mounted heat-rejection units, inside a container built to withstand transport and harsh environments.

Typical workloads

  • Predictive maintenance
  • Seismic and subsurface interpretation
  • Drone and camera analytics
  • Process optimization
  • Safety monitoring
Plan an off-grid deploymentRelated readingDesign a solar-powered AI data center

05Universities & research

Shared GPU capacity for research programs.

Research groups need serious compute for a few weeks, then something else entirely. One container on a campus or at a national laboratory can serve many programs at once.

Shared capacity floor planTop view of the container: six immersion tanks of 104 GPUs, arranged three and three on either side of a central service aisle, with an illustrative allocation to three research programs.Top view · illustrative allocation104 GPUs104 GPUs104 GPUs104 GPUs104 GPUs104 GPUsService aisleProgram A · 3 tanksProgram B · 2 tanksProgram C · 1 tank1 tank = 13 servers8 GPUs per server
Fig. 05 — Six tanks shared across programs (illustrative)

The challenge

Access to large GPU clusters is scarce and competitive. Researchers wait in queues, spread work across external allocations or scale experiments down, while sensitive datasets in health or national research cannot always leave the institution.

How the AI Container answers

  1. Capacity shared across programs

    The 624 GPUs, spread across six tanks of 104 GPUs each, can be allocated by group, project or semester, and reallocated as priorities change.

  2. Memory for large models

    288 GB of HBM3e per GPU, about 179 TB in total, leaves room for large models and long-context experiments.

  3. Operations off the researchers’ desks

    With BAYWAW managed services, monitoring, maintenance, patching and capacity reporting are handled for you, so research staff stay focused on research.

Typical workloads

  • Large-model research
  • Scientific machine learning
  • Genomics and medical imaging
  • Climate and Earth-system modeling
  • Teaching and student projects
Discuss a research deploymentRelated readingImmersion cooling for AI GPUs, explained

06Rapid capacity

AI capacity before a building could be ready.

Demand for AI compute moves faster than construction. For operators, cloud providers and integrators who need capacity now, the AI Container replaces a construction project with a delivery.

Time to capacity and scalingA conventional data center build typically takes years, while the AI Container can be operational in as little as four months. Capacity then grows in 1.2 MW steps by adding containers.Time to capacityConventional buildtypically yearsBAYWAW AI Containeras little as 4 monthsScale by adding containers1.2 MW1.2 MW1.2 MW1.2 MW
Fig. 06 — Time to capacity, then growth in 1.2 MW steps (not to scale)

The challenge

Conventional data center projects are typically measured in years from planning to commissioning: permits, construction, grid connection, cooling plant. Meanwhile, customers, models and contracts wait.

How the AI Container answers

  1. Operational in as little as four months

    The container is built, integrated and tested in the UAE while your site is prepared, then shipped by road or sea ready to run.

  2. Scale one container at a time

    Each unit adds 1.2 MW of immersion-cooled capacity. Start with one container and add more on the same site or across sites as demand grows.

  3. Validated before it ships

    The network is tested end to end before shipment, with AI-driven diagnostics, topology mapping, configuration compliance checks and automated tests.

Typical workloads

  • Bridge capacity during a data center build
  • Burst capacity for model launches
  • New GPU cloud regions
  • Capacity for colocation customers
  • Pilot sites ahead of larger rollouts
Request a proposal and timelineRelated readingContainerized data centers: the 2026 buyer’s guide

07Ownership and operation

Your container, your operating model.

Both models start the same way: a container built, integrated and tested in the UAE, delivered ready to run with every system configured and optimized. The difference is who runs it day to day.

Model A

You own it, you operate it

Your infrastructure team takes over a pre-integrated, validated system and runs it under your own procedures and security policies.

Ownership
Your organization
Day-to-day operation
Your infrastructure team
Monitoring
Your tools and processes
Maintenance, upgrades and patching
Your team, under your procedures
Support
Scope agreed with you in the proposal
Best suited to
Organizations with in-house data center operations and strict rules on who handles the hardware
See what ships with the container

Model B

You own it, BAYWAW operates it

BAYWAW runs the container end to end: monitoring, maintenance, upgrades, security patching, component replacement, warranty management and reporting, with a dedicated account manager.

Ownership
Your organization
Day-to-day operation
BAYWAW, end to end
Monitoring
Real-time monitoring and alerting, 24/7/365
Maintenance, upgrades and patching
Preventive maintenance, lifecycle upgrades, OS and firmware patching, component replacement
Support
Follow-the-sun, initial response in under 15 minutes
Best suited to
Teams that want AI compute without running a facility
See the ten managed services

Every proposal is sized to your workloads, your site and the operating model you choose.

Request a proposal

08Questions on fit

Fit, scale and sovereignty.

Short answers on scale, sites, connectivity and data sovereignty. For anything specific to your project, book a technical briefing.

What is the minimum deployment size?

One BAYWAW AI Container: 624 NVIDIA B300 GPUs in 78 servers and 1.2 MW of total facility power, delivered in a single 40ft High Cube ISO container. It is a complete data center on its own, with compute, immersion cooling and networking pre-integrated.

Can we scale beyond one container?

Yes. Capacity grows by adding containers, each bringing another 1.2 MW and 624 GPUs, on the same site or across several sites. The edge routing layer supports BGP, high availability and multi-site operation, and we design the interconnect between containers with you.

What does our site need to provide?

In general terms: a level, accessible area with a foundation suited to a loaded 40ft container (maximum gross mass 30,480 kg), a power supply matched to a 1.2 MW facility load from the grid, on-site generation or solar with battery storage, network connectivity, and clear access for delivery, heat rejection and maintenance. We review the specifics of your site during the technical briefing.

What connectivity is required?

Inside the container, each server has eight 400G links into the GPU fabric and two 400G north-south links into a front-end spine of six 64-port 800G switches, backed by redundant edge routers. The upstream capacity your site needs depends on your workloads: serving inference to many users calls for more external bandwidth than training on data stored on site. We size it with you.

How does the AI Container support data sovereignty?

The container is installed on the site and in the jurisdiction you choose, so data, model weights and inference traffic can stay entirely in-country. Next-generation firewalls, a separate out-of-band management network and access control are part of the design. Under the managed model, data and access security (network security, access control, vulnerability management and compliance) is part of the BAYWAW scope.

Next step

Sovereign AI compute on your site in as little as four months.

Tell us about your workload, site and timeline. Our engineers typically reply within one business day with a first sizing and the next steps.

Request a proposalBook a technical briefing