--- title: "Modular infrastructure & resources | Aguasto" description: "Follow the physical dependencies from authorized electricity through facility integration, cooling and accepted computing capacity." canonical: "https://www.aguasto.com/topics/modular-infrastructure" last-updated: "2026-10-13" --- # Modular infrastructure & resources | Aguasto Canonical HTML: [Modular infrastructure & resources](https://www.aguasto.com/topics/modular-infrastructure) > Follow the physical dependencies from authorized electricity through facility integration, cooling and accepted computing capacity. [Aguasto](https://www.aguasto.com/) [Research topics](https://www.aguasto.com/topics) Modular infrastructure & resources [Resources](https://www.aguasto.com/resources) [Topics](https://www.aguasto.com/topics) [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) [Engineering tools](https://www.aguasto.com/tools) [Glossary](https://www.aguasto.com/glossary) [Methods](https://www.aguasto.com/methodologies) Aguasto / Research path ## Modular infrastructure & resources Follow the physical dependencies from authorized electricity through facility integration, cooling and accepted computing capacity. Evidence before conclusions Read the mechanism, inspect its sources and carry the stated boundaries into your own evaluation. [How Aguasto Research works](https://www.aguasto.com/methodologies) Aguasto Research · Revision 1 Editorial record: 2026-10-13 [Review and correction policy ↗](https://www.aguasto.com/methodologies#review) ### Which work can progress independently, and where must it meet? Prefabrication can move selected assembly and checks away from a construction site. Electricity authorization, local approvals, civil interfaces and integrated commissioning remain project dependencies. An energized piece of equipment does not prove an available data-center service. The modular thesis concerns how work is divided and coordinated, not a universal permission or schedule advantage. Define physical and operating interfaces before drawing a delivery sequence. Compare site and factory readiness at their actual handover points. For resource questions, declare the facility, IT and useful-output boundaries, the location and reporting period. Cooling topology alone cannot establish water use, energy performance or an environmental advantage. ### Follow the evidence. - #### Understand the enclosure and its interfaces A module joins structural, electrical, cooling and network systems. Factory preparation is useful only when those interfaces are accepted and site integration can close the remaining scope. Generic cutaways explain the mechanism; they are not equipment specifications or evidence of an operating facility. [Why Modular ↗](https://www.aguasto.com/infrastructure/why-modular) The thesis, contrary cases and retained dependencies.[Factory to commissioning ↗](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) Separate assembly checks from integrated acceptance. - #### Trace heat and resource boundaries Follow heat from components through rack interfaces to external rejection. Keep technology coolant and facility circuits distinct. Compare local water consumption, water withdrawal and indirect electricity-related water under explicit definitions rather than treating liquid cooling as a single environmental claim. [Cooling and resource efficiency ↗](https://www.aguasto.com/infrastructure/cooling-resource-efficiency) Explore circuits, local conditions and useful compute.[Water accounting boundaries ↗](https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal) Consumption and withdrawal are different measures. - #### Assess independent facilities honestly Distributed facilities can change delivery phases and failure exposure. They can also repeat costs and introduce shared software, network and operational dependencies. Eligibility rules, staffing, spare capacity and recovery tests determine whether an application can use another location when a fault occurs. [Distributed resilience study ↗](https://www.aguasto.com/infrastructure/why-modular#distributed-resilience) Inspect fault cases and remaining recovery conditions.[Development evidence ↗](https://www.aguasto.com/research/hyperscale-development-friction) Dated jurisdictional evidence with contrary findings. ### Cornerstone resources learn / Source-backed #### What Is a Modular AI Data Center? Distinguish a facility module, equipment assembly and GPU rack, then define the interfaces that connect an enclosure to usable AI compute. Source dates and scope in the reference [What Is a Modular AI Data Center?](https://www.aguasto.com/learn/what-is-a-modular-ai-data-center) learn / Source-backed #### How Modular AI Data Centers Are Built and Commissioned Follow the handoffs between requirements, factory integration, transport, site acceptance and compute-service evaluation without treating any single milestone as completion. Source dates and scope in the reference [How Modular AI Data Centers Are Built and Commissioned](https://www.aguasto.com/learn/modular-data-center-factory-to-commissioning) learn / Source-backed #### Liquid Cooling for High-Density GPU Systems: Follow the Interfaces Understand facility and technology cooling circuits, residual air paths and the evidence needed to evaluate a proposed GPU rack integration. Source dates and scope in the reference [Liquid Cooling for High-Density GPU Systems: Follow the Interfaces](https://www.aguasto.com/learn/liquid-cooling-gpu-rack-interfaces) research / Source-backed #### Can Data Centers Reduce Water Consumption? Define the Water Boundary First Explain freshwater substitution, peak withdrawal, direct consumption and electricity-related water use without turning a closed loop into a zero-water claim. Source dates and scope in the reference [Can Data Centers Reduce Water Consumption? Define the Water Boundary First](https://www.aguasto.com/research/data-center-water-consumption-and-withdrawal) ### Continue the research [From Power to Inference—Building a Distributed AI Compute Network](https://www.aguasto.com/learn/from-power-to-inference) [Why Modular AI Infrastructure Matters Now](https://www.aguasto.com/research/why-modular-ai-infrastructure-matters-now) [Why Electricity Availability Shapes AI Infrastructure Deployment](https://www.aguasto.com/research/electricity-availability-and-time-to-power) [Data Center Cooling: Choosing Air, Liquid, Dry and Hybrid Architectures](https://www.aguasto.com/learn/data-center-cooling-air-liquid-dry-hybrid) [The Environmental Trade-offs of High-Density AI Infrastructure](https://www.aguasto.com/research/environmental-tradeoffs-and-infrastructure-reuse) ### Keep these boundaries close. [Modular facility ↗](https://www.aguasto.com/glossary#modular-facility) [Integrated commissioning ↗](https://www.aguasto.com/glossary#commissioning) [Coolant distribution unit ↗](https://www.aguasto.com/glossary#cdu) [Power usage effectiveness (PUE) ↗](https://www.aguasto.com/glossary#pue) [Water consumption and withdrawal ↗](https://www.aguasto.com/glossary#water-consumption) ### From research to qualification Potential supplier and engineering relationships; none implied. Aguasto’s commercial offerings remain in development. No available capacity, live model API, commercial reservation or procurement relationship is implied by this research. [Discuss technical collaboration ↗](https://www.aguasto.com/partners) ### Sources & verification The register preserves each source's publication and verification date. Manufacturer claims and documentation are attributed; Aguasto has not run the cited products in production. - [Types of Prefabricated Modular Data Centers, revision2 — Schneider Electric ↗](https://www.se.com/ww/en/download/document/SPD_WTOL-97GLP9_EN/) Prefabricated systems/subsystems terminology and form-factor distinctions. Publication: 2017-09-29. Verified: 2026-10-13. - [Prefabricated modular data centers: terminology — Schneider Electric ↗](https://blog.se.com/datacenter/2013/12/02/prefabricated-modular-data-centers-sorting-terminology-confusion/) Function/form-factor/configuration taxonomy. Publication: 2013-12-02. Verified: 2026-10-13. - [DGX SuperPOD Architecture for DGXGB200 — NVIDIA ↗](https://docs.nvidia.com/dgx-superpod/reference-architecture-scalable-infrastructure-gb200/latest/dgx-superpod-architecture.html) DGXGB200 uses both direct-liquid and air cooling; Reference architecture ties systems, networks, management and storage. Last modified: 2025-11-19. Verified: 2026-10-13. - [Cooling Environments — Open Compute Project ↗](https://www.opencompute.org/projects/cooling-environments/) Cold-plate/TCS scope includes tubing/manifolds/couplings/CDUs; Different cooling architectures and standardization work. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Liquid Cooling Integration and Logistics White Paper, revision1.0 — Open Compute Project ↗](https://www.opencompute.org/documents/ocp-liquid-cooling-integration-and-logistics-white-paper-revision-1-0-1-pdf) FWS/TCS/CDU terminology and liquid-to-liquid isolation; Manufacturing, transport, inspection and commissioning handoffs; Wetted materials and project-specific tests. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Prefabricated Data Center brochure — Vertiv ↗](https://www.vertiv.com/4a7618/globalassets/shared/vertiv-prefabricated-data-center-brochure.pdf) Factory/site/service integration phases; Engineering, acceptance plans, installation and commissioning roles. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Network edge enabled by prefabricated modular data centers — Schneider Electric ↗](https://blog.se.com/datacenter/2022/05/17/supporting-network-edge-prefabricated-modular-data-centers/) Factory assembly and site work may proceed in parallel. Publication: 2022-05-17. Verified: 2026-10-13. - [Benefits and Drawbacks of Prefabricated Modules, revision4 — Schneider Electric ↗](https://www.se.com/us/en/download/document/SPD_WTOL-7NGRBS_EN/) Standardized factory integration shifts work toward site integration; Advantages are compared with drawbacks. Publication: 2026-02-03. Verified: 2026-10-13. - [Commissioning your data center for greater availability and safety — Vertiv ↗](https://www.vertiv.com/globalassets/services/services/project-services/commissioning-your-data-center-for-greater-availability-safety.pdf) Commissioning scope spans requirements/design/construction/operations; Vendor startup, integrated test and turnover are distinct. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Startup and Site Acceptance Test — Vertiv ↗](https://www.vertiv.com/en-us/services-catalog/services/project-services/startup-and-commissioning-services/) Electrical/mechanical startup checks; Functional testing in operational modes and integrated site acceptance. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [2024 United States Data Center Energy Usage Report — LBNL ↗](https://escholarship.org/content/qt32d6m0d1/qt32d6m0d1.pdf) Historical estimates and 2028 scenarios; Direct and electricity-related water boundaries; Regional indirect-water methodology limitations. Publication: 2024-12-19. Verified: 2026-10-13. - [Best Management Practice #14: Alternative Water Sources — DOE FEMP ↗](https://www.energy.gov/cmei/femp/best-management-practice-14-alternative-water-sources) Alternative water sources and cooling-tower makeup; Treatment, permits, storage and maintenance; Freshwater substitution differs from eliminated use. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Alternative Water Projects: Implementation Steps — DOE FEMP ↗](https://www.energy.gov/cmei/femp/alternative-water-projects-implementation-steps) Inventory water end uses; Match supply and treatment to applications; Size and maintain alternative water systems. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Cooling Water Efficiency Opportunities for Federal Data Centers — DOE FEMP ↗](https://www.energy.gov/cmei/femp/cooling-water-efficiency-opportunities-federal-data-centers) Closed equipment loop can reject to an evaporative tower; Annual site WUE definition; Treatment can add electricity and maintenance. Publication: 2019-01-09. Verified: 2026-10-13. - [Facility Water Cycle and Emissions Crosswalk — DOE FEMP ↗](https://www.energy.gov/cmei/femp/facility-water-cycle-and-emissions-crosswalk) Water supply, distribution and treatment use energy; Illustrative energy-water factors require local context. Publication: date not stated in the reviewed source. Verified: 2026-10-13. - [Best Management Practice #10: Cooling Tower Management — DOE FEMP ↗](https://www.energy.gov/cmei/femp/best-management-practice-10-cooling-tower-management) Evaporation, drift, blowdown and leaks; Water chemistry limits concentration cycles; Makeup and blowdown metering. Publication: date not stated in the reviewed source. Verified: 2026-10-13. ### In this research path [The decision](https://www.aguasto.com/topics/modular-infrastructure#decision) [Reading sequence](https://www.aguasto.com/topics/modular-infrastructure#reading-path) [Cornerstone resources](https://www.aguasto.com/topics/modular-infrastructure#cornerstones) [Useful definitions](https://www.aguasto.com/topics/modular-infrastructure#definitions) [Sources and verification](https://www.aguasto.com/topics/modular-infrastructure#sources) [All research paths ↗](https://www.aguasto.com/topics) ## 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.