--- title: "AI Compute. Built From the Ground Up. | Aguasto" description: "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." canonical: "https://www.aguasto.com/" last-updated: "2026-10-14" --- # AI Compute. Built From the Ground Up. | Aguasto Canonical HTML: [Home](https://www.aguasto.com/) > 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. Aguasto / Infrastructure-first AI 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. [Explore Our Infrastructure](https://www.aguasto.com/infrastructure) [Discuss Compute Requirements](https://www.aguasto.com/contact?type=Enterprise%20compute&intent=gpu-capacity) U.S. focused · Platform in development Modular Compute Network Original conceptual visualization. No operating assets represented. 01 / The business ### 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. 01 #### Modular Infrastructure Container-based compute facilities that integrate racks, power and cooling around site-specific requirements. [Explore infrastructure](https://www.aguasto.com/infrastructure) 02 #### Dedicated GPU Compute [Explore planned compute](https://www.aguasto.com/gpu-cloud) 03 #### Managed Inference [Explore planned inference](https://www.aguasto.com/ai-inference) Facilities and services are in development. No GPU capacity, reservation or operating inference endpoint is available. 02 / One operating idea ### Build the foundation. Deliver useful compute. 01 #### Infrastructure Authorized power, cooling and service access support the physical environment. 02 #### Local compute Qualified GPU systems, storage and local fabric support a defined workload. 03 #### 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 architecture](https://www.aguasto.com/learn/from-power-to-inference) 03 / Modular by design ### 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 thesis](https://www.aguasto.com/infrastructure/why-modular) #### Parallel 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 offerings ### Two ways to use a stronger foundation. 01 / In development #### Dedicated GPU Compute Planned GPU rental, reserved capacity and dedicated enterprise clusters, with a defined configuration, tenancy boundary and service scope. [Discuss GPU Requirements](https://www.aguasto.com/contact?type=Enterprise%20compute&intent=gpu-capacity) [Explore planned compute](https://www.aguasto.com/gpu-cloud) 02 / In development #### Managed Inference Planned model hosting and private enterprise APIs, with workload evaluation and transparent token accounting before commercial availability. [Inference Early Access](https://www.aguasto.com/contact?type=Inference%20%26%20model%20hosting&intent=inference-early-access) [Explore planned inference](https://www.aguasto.com/ai-inference) Nonbinding 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 Library](https://www.aguasto.com/gpus) NVIDIA / Manufacturer reference #### NVIDIA Vera Rubin NVL72 Vera CPU / Rubin GPU Published reference · Explore specifications [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) NVIDIA / Manufacturer reference #### NVIDIA GB300 NVL72 Grace Blackwell Ultra Published reference · Explore specifications [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) AMD / Manufacturer reference #### AMD Helios MI455X / EPYC Venice / Pensando Published reference · Explore specifications [AMD Helios](https://www.aguasto.com/gpus/helios) Manufacturer references are research, not Aguasto inventory, OEM partnership or procurement commitments. 05 / Engineering discipline ### Design the interfaces. Measure the useful work. Infrastructure quality is coordinated execution across electrical, thermal, mechanical, software and operating responsibilities. [Engineering and resource efficiency](https://www.aguasto.com/infrastructure/cooling-resource-efficiency) #### Power 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 #### Liquid Cooling for High-Density GPU Systems: Follow the Interfaces Read research [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces) 02 / Research #### From Power to Inference—Building a Distributed AI Compute Network Read research [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) 03 / Research #### How Modular AI Data Centers Are Built and Commissioned Read research [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) [Explore Aguasto Research](https://www.aguasto.com/research) 07 / Discuss your requirements ### A clear workload. A considered next step. [Discuss Compute Requirements](https://www.aguasto.com/contact?type=Enterprise%20compute&intent=gpu-capacity) [Technical and strategic collaboration](https://www.aguasto.com/contact?type=Equipment%20%26%20infrastructure) 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. ## Company navigation - [Home](https://www.aguasto.com/) - [Infrastructure](https://www.aguasto.com/infrastructure) - [GPU Cloud](https://www.aguasto.com/gpu-cloud) - [AI Inference](https://www.aguasto.com/ai-inference) - [Development & Investment](https://www.aguasto.com/development) - [Company](https://www.aguasto.com/company) - [Contact](https://www.aguasto.com/contact) - [Privacy](https://www.aguasto.com/privacy) - [Research topics](https://www.aguasto.com/topics) - [GPU systems & local clusters](https://www.aguasto.com/topics/gpu-systems) - [Model serving & inference](https://www.aguasto.com/topics/model-serving) - [Modular infrastructure & resources](https://www.aguasto.com/topics/modular-infrastructure) - [Compute economics & evidence](https://www.aguasto.com/topics/compute-economics) - [Glossary](https://www.aguasto.com/glossary) - [Methodologies](https://www.aguasto.com/methodologies) - [Resources](https://www.aguasto.com/resources) - [GPU Library](https://www.aguasto.com/gpus) - [Model Index](https://www.aguasto.com/models) - [Inference Stack](https://www.aguasto.com/inference-stack) - [Learn](https://www.aguasto.com/learn) - [Insights](https://www.aguasto.com/insights) - [Research](https://www.aguasto.com/research) - [Industry cases](https://www.aguasto.com/research/cases) - [Why Modular](https://www.aguasto.com/infrastructure/why-modular) - [Cooling & Resource Efficiency](https://www.aguasto.com/infrastructure/cooling-resource-efficiency) - [Partners](https://www.aguasto.com/partners) - [GPU comparison](https://www.aguasto.com/gpus/compare) - [Model comparison](https://www.aguasto.com/models/compare) - [Engineering tools](https://www.aguasto.com/tools) - [Development friction](https://www.aguasto.com/research/hyperscale-development-friction) - [NVIDIA H100 SXM](https://www.aguasto.com/gpus/h100) - [NVIDIA H200 SXM](https://www.aguasto.com/gpus/h200) - [NVIDIA B200 SXM](https://www.aguasto.com/gpus/b200) - [NVIDIA B300](https://www.aguasto.com/gpus/b300) - [NVIDIA GB200 NVL72](https://www.aguasto.com/gpus/gb200-nvl72) - [NVIDIA GB300 NVL72](https://www.aguasto.com/gpus/gb300-nvl72) - [NVIDIA Vera Rubin NVL72](https://www.aguasto.com/gpus/vera-rubin-nvl72) - [AMD Instinct MI300X](https://www.aguasto.com/gpus/mi300x) - [AMD Instinct MI325X](https://www.aguasto.com/gpus/mi325x) - [AMD Instinct MI355X](https://www.aguasto.com/gpus/mi355x) - [AMD Instinct MI455X](https://www.aguasto.com/gpus/mi455x) - [AMD Helios](https://www.aguasto.com/gpus/helios) - [GLM-4.5](https://www.aguasto.com/models/glm-4-5) - [Qwen3-235B-A22B](https://www.aguasto.com/models/qwen3-235b-a22b) - [DeepSeek-R1](https://www.aguasto.com/models/deepseek-r1) - [Llama 3.3 70B Instruct](https://www.aguasto.com/models/llama-3-3-70b-instruct) - [Gemma 3 27B IT](https://www.aguasto.com/models/gemma-3-27b-it) - [gpt-oss-120b](https://www.aguasto.com/models/gpt-oss-120b) - [Kimi-K2-Instruct](https://www.aguasto.com/models/kimi-k2-instruct) - [GLM-5.3](https://www.aguasto.com/models/glm-5-3) - [Qwen3.8-27B](https://www.aguasto.com/models/qwen3-8-27b) - [DeepSeek-V4.1-Flash](https://www.aguasto.com/models/deepseek-v4-1-flash) - [Llama 4 Scout 17B-16E Instruct](https://www.aguasto.com/models/llama-4-scout-17b-16e-instruct) - [Gemma 4 31B IT](https://www.aguasto.com/models/gemma-4-31b-it) - [Kimi K3](https://www.aguasto.com/models/kimi-k3) - [vLLM](https://www.aguasto.com/inference-stack/vllm) - [SGLang](https://www.aguasto.com/inference-stack/sglang) - [TensorRT-LLM](https://www.aguasto.com/inference-stack/tensorrt-llm) - [NVIDIA Dynamo](https://www.aguasto.com/inference-stack/dynamo) - [NVIDIA Triton Inference Server](https://www.aguasto.com/inference-stack/triton) - [AMD ROCm](https://www.aguasto.com/inference-stack/rocm) - [Ray Serve](https://www.aguasto.com/inference-stack/ray-serve) - [Kubernetes GPU orchestration — NVIDIA device plugin](https://www.aguasto.com/inference-stack/kubernetes) - [Model weights are only the beginning of the memory budget](https://www.aguasto.com/learn/model-weights-and-runtime-memory) - [Why tokens per second is not enough to benchmark inference](https://www.aguasto.com/research/inference-benchmark-methodology) - [Prefill, decode and the cost of separating them](https://www.aguasto.com/learn/prefill-decode-and-disaggregated-serving) - [Choosing between vLLM, SGLang and TensorRT-LLM](https://www.aguasto.com/learn/choosing-an-inference-serving-framework) - [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) - [Dedicated compute and shared inference: compare the whole workload](https://www.aguasto.com/insights/dedicated-and-shared-inference-economics) - [Distributed inference reliability begins with the failure boundary](https://www.aguasto.com/learn/distributed-inference-reliability) - [Core Scientific: reading contracted, energized and billed capacity](https://www.aguasto.com/research/cases/core-scientific-capacity-disclosures) - [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) - [What Is a Modular AI Data Center?](https://www.aguasto.com/learn/what-is-a-modular-ai-data-center) - [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) - [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces) - [Why Electricity Availability Shapes AI Infrastructure Deployment](https://www.aguasto.com/research/electricity-availability-and-time-to-power) - [What Is KV Cache? Capacity, Reuse and Isolation](https://www.aguasto.com/learn/kv-cache-capacity-and-policy) - [Continuous Batching: Throughput Without Hiding Latency](https://www.aguasto.com/learn/continuous-batching-and-latency) - [Applied Digital: Reading the Polaris Forge 1 Delivery Milestones](https://www.aguasto.com/research/cases/applied-digital-polaris-forge-capacity-milestones) - [Crusoe Abilene: Separate Delivered Infrastructure from Expansion Plans](https://www.aguasto.com/research/cases/crusoe-abilene-delivery-and-expansion) - [Data Center Cooling: Choosing Air, Liquid, Dry and Hybrid Architectures](https://www.aguasto.com/learn/data-center-cooling-air-liquid-dry-hybrid) - [Can Data Centers Reduce Water Consumption? Define the Water Boundary First](https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal) - [Measuring PUE, WUE and AI Compute Efficiency at the Right Boundary](https://www.aguasto.com/learn/measuring-pue-wue-and-ai-compute-efficiency) - [The Environmental Trade-offs of High-Density AI Infrastructure](https://www.aguasto.com/research/environmental-tradeoffs-and-infrastructure-reuse) - [Selecting GPU Systems for Enterprise Inference](https://www.aguasto.com/learn/selecting-enterprise-inference-gpu-systems) - [Networking Rack-Scale AI Systems: Define the Communication Boundary](https://www.aguasto.com/learn/rack-scale-ai-networking-boundaries) - [Official LinkedIn](https://www.linkedin.com/company/aguasto) This is a generated representation of the public HTML page. Diagrams are conceptual. GPU and inference services are planned and are not currently available to book or call. The contact page describes the available inquiry flow.