Modular Infrastructure
Container-based compute facilities that integrate racks, power and cooling around site-specific requirements.
Explore infrastructureAguasto / Infrastructure-first AI computeAI Compute.
AI Compute.
Built From the
Ground Up.
Aguasto is developing a distributed network of modular AI data centers designed for high-density GPU computing, dedicated enterprise infrastructure and high-performance AI inference.
U.S. focused · Platform in development
Original conceptual visualization. No operating assets represented.
01 / The businessThe physical foundation.
The physical foundation.
The computing service.
Aguasto develops infrastructure designed to deliver high-performance AI compute. Our intended operating model brings the facility, the hardware and the software together.
Facilities and services are in development. No GPU capacity, reservation or operating inference endpoint is available.
02 / One operating ideaBuild the foundation.
01 02 03
Build the foundation.
Deliver useful compute.
Infrastructure
Authorized power, cooling and service access support the physical environment.
Local compute
Qualified GPU systems, storage and local fabric support a defined workload.
Model service
Licensed weights, runtime evaluation and service policy connect compute to applications.
Clusters remain local. Independent sites could host compatible model replicas; service routing does not join distant GPUs into one training fabric.
Explore the electrical-to-API architecture03 / Modular by designRepeatable engineering.
Repeatable engineering.
Independent facilities.
Power constraints, local approvals and complex construction shape the path to usable AI capacity. Factory-prepared systems can suit projects where physical constraints and integration requirements are clear.
Read the modular development thesisParallel preparation
Coordinate equipment integration with site work while respecting the actual dependencies.
Independent clusters
Develop each facility around its own engineering, local capacity and operating responsibilities.
Shared service policy
Compatible model replicas may support routing when versions, spare capacity and data policy permit.
Modularity does not remove utility, permitting, financing or operational requirements, or guarantee a faster delivery date.
04 / Planned commercial offeringsTwo ways to use
01 / In development 02 / In development
Two ways to use
a stronger foundation.
Dedicated GPU Compute
Planned GPU rental, reserved capacity and dedicated enterprise clusters, with a defined configuration, tenancy boundary and service scope.
Discuss GPU Requirements Explore planned computeManaged Inference
Planned model hosting and private enterprise APIs, with workload evaluation and transparent token accounting before commercial availability.
Inference Early Access Explore planned inferenceNonbinding inquiries. No capacity purchase, reservation, API key, token price or live service is offered.
Architecture matters.
So does the whole system.
Relevant NVIDIA and AMD architectures inform hardware evaluation. Rack power, liquid-cooling interfaces, local fabric and software compatibility must be assessed together.
Explore the GPU LibraryNVIDIA / Manufacturer reference
NVIDIA Vera Rubin NVL72
Vera CPU / Rubin GPU
Published reference · Explore specifications NVIDIA / Manufacturer referenceNVIDIA GB300 NVL72
Grace Blackwell Ultra
Published reference · Explore specifications AMD / Manufacturer referenceAMD Helios
MI455X / EPYC Venice / Pensando
Published reference · Explore specificationsManufacturer references are research, not Aguasto inventory, OEM partnership or procurement commitments.
05 / Engineering disciplineDesign the interfaces.
Design the interfaces.
Measure the useful work.
Infrastructure quality is coordinated execution across electrical, thermal, mechanical, software and operating responsibilities.
Engineering and resource efficiencyPower readiness
Installed equipment does not establish usable capacity or utility authorization.
Thermal interfaces
Evaluate equipment heat capture and site heat rejection as separate engineering questions.
Useful output
Define the measurement boundary before comparing energy, water or inference efficiency.
06 / The evidence behind the engineering
Research behind the infrastructure.
01 / Research
Explore Aguasto Research Liquid Cooling for High-Density GPU Systems: Follow the Interfaces
Read research 02 / ResearchFrom Power to Inference—Building a Distributed AI Compute Network
Read research 03 / ResearchHow Modular AI Data Centers Are Built and Commissioned
Read research07 / Discuss your requirementsA clear workload.
A clear workload.
A considered next step.
For enterprise compute, future inference, technical collaboration and institutional capital. Prepare an unsent inquiry or open your email app, review the message and send it yourself.
Platform in development. Website actions do not send an email or establish a reservation or service commitment.