POWER-ALIGNED DISTRIBUTED AI INFRASTRUCTURE
Put AI Compute
Where
Power Can Support It.
Scattered Metal develops relocatable compute modules for AI inference, selected fine-tuning, data processing and regional workloads—matching hardware and density to the power and operating conditions of each site.
Broaden where AI capacity can exist without forcing every workload into another hyperscale campus.
Modular. Standards-based. Serviceable. Remotely operated.
AI’s constraint is physical infrastructure.
485 TWh
Estimated global data-center electricity consumption in 2025.
950 TWh
Projected global data-center electricity consumption in 2030.
3X
Projected growth in electricity use by AI-focused data centers from 2025 to 2030.
Five clusters
Nearly half of U.S. data-center capacity is concentrated in five regional clusters.
These statistics are supported by current International Energy Agency, Energy and AI and Key Questions on Energy and AI, 2025–2026 analysis.
THE PHYSICAL LIMITS OF AI
AI is scaling in software time. Infrastructure is not.
Models, applications and demand can change in months. Generation, transmission, substations, transformers, cooling systems and large data-center campuses move on physical timelines measured in years.As AI infrastructure becomes denser, each new development places greater electrical and thermal demand on a limited number of established regions. Grid queues lengthen. Equipment supply tightens. Water and land become community issues. More capital is committed before useful compute becomes available.The problem is no longer limited to acquiring accelerators. It is building the physical systems needed to run them.
The speed of AI is colliding with the speed of power infrastructure.
The problem is not only how much compute we build. It is where and how we build it.
Dense campuses concentrate compute where power, water and infrastructure are already constrained.
Conventional development begins with a target level of compute density and then asks the site and power system to accommodate it.Scattered Metal reverses that sequence.Each deployment begins with the site’s available power, reliability, thermal conditions, network access and target workloads. The compute configuration is then designed to fit that operating envelope.
Match compute to power. Not power to maximum density.
THE SCATTERED METAL MODEL
Change the unit of deployment.
Scattered Metal replaces the all-or-nothing campus as the only unit of expansion with a smaller, repeatable compute module.Modules can be deployed across suitable regional sites, configured for the workloads they will run and managed as part of one operating fleet. Capacity can be added in increments, upgraded by subsystem and relocated when long-term site conditions change.This does not eliminate dense data centers. It creates another infrastructure layer for the large class of AI workloads that do not require hyperscale concentration.
The speed of AI is colliding with the speed of power infrastructure.
Compute Module
An integrated environment for compute, cooling, power distribution, networking, monitoring and safety.
Iron-Class Servers
Balanced CPU, memory, multi-GPU and storage capacity for varied AI and data workloads.
Fleet Ops Layer
Remote monitoring, control, scheduling, maintenance and lifecycle management across distributed sites.
Integration
Configuration based on local power, climate, connectivity, environmental conditions and workload requirements.
A complete compute environment, deployed as a module.
Not a temporary rack. Not a miniature hyperscale hall.
Each Scattered Metal module is designed as an integrated operating environment. Compute systems, cooling, power distribution, networking, environmental monitoring and safety controls are planned together rather than assembled as unrelated components.The modular form supports repeatable deployment while allowing each site to be configured for local climate, power quality, connectivity, security and workload requirements.
Integrated environmental control.
Power monitoring and remote switching.
Network and access control.
Fire detection and protection.
Environmental telemetry.
Serviceable equipment layout.
Remote operations.
Site-specific configuration.
Repeatable deployment design.
Relocatable enclosure.
BALANCED AI COMPUTE
Built like infrastructure, not a sealed appliance.
Balanced for useful work, serviceability and multiple hardware generations.
Iron-Class AI servers combine strong CPU resources, high memory capacity, multi-GPU acceleration and substantial local storage.The design does not pursue maximum rack density at any cost. It balances accelerator performance with data movement, memory, storage, thermal headroom, component access and the requirements of the workloads assigned to the site.Standards-based, commercially available components support field replacement and staged upgrades without requiring the entire platform to be discarded when one hardware generation changes.
Balanced Compute
CPU, GPU, memory and storage are selected as one workload system.
Thermal Headroom
Density is controlled to preserve cooling capacity and support future configurations.
Serviceable
Components can be accessed, repaired and replaced without treating the server as a sealed device.
Multi-Generation
Compute subsystems can be refreshed while the surrounding infrastructure remains in service.
Density is a design variable. Useful compute is the objective.
AI-FIRST WORKLOADS
Built for the AI work that can be distributed.
Not every AI workload requires thousands of accelerators operating as one tightly coupled machine.
AI Inference
Serve language, vision, audio and multimodal models across regional or dedicated capacity.
Batch Inference
Process large datasets, media collections and queued jobs where throughput matters more than immediate response time.
Fine-Tuning & Experimentation
Support selected adapter training, model customization and development workloads when the model and networking requirements fit the module.
Retrieval & Embeddings
Generate embeddings, construct indexes and operate storage-rich retrieval-augmented generation pipelines.
Data Preparation
Run preprocessing, transformation, labeling, quality evaluation and feature-generation workloads across CPUs and GPUs.
Generative Media
Support image, video, audio and other generation tasks that can be scheduled across independent workers.
Simulation & Synthetic Data
Run parallel scientific, engineering and synthetic-data workloads that benefit from mixed compute and local storage.
Regional & Controlled AI
Place capacity closer to an organization, dataset, user population or required operating jurisdiction.
Workload suitability depends on model size, accelerator memory, network topology, data locality, latency, availability and service requirements.
Scattered Metal complements the tightly coupled clusters used for frontier-scale model pretraining. It does not claim to replace them.
ONE FLEET, MANY SITES
Scattered by location. Unified in operation.
Distributed infrastructure only works when every module is observable, controllable and governed by a common operating model.Scattered Metal modules are designed to function as a coordinated fleet. Capacity can be classified by hardware profile, location, network conditions, power characteristics and service requirements. Workloads can then be placed where the technical and operating fit is strongest.
Operating capabilities
Module and server health monitoring.
Power and environmental telemetry.
Remote power control.
Capacity inventory.
Workload placement.
Hardware-profile matching.
Maintenance records.
Upgrade planning.
Utilization monitoring.
Site-level operating policies.
Incident and alert management.
Fleet reporting.
COMPUTE THAT FITS THE GRID
Start with the real power envelope.
Every deployment begins with the power that is actually available—not the power a maximum-density design would prefer to have.Scattered Metal evaluates capacity, reliability, power quality, carbon intensity, thermal conditions, network access and operating constraints. The server and workload mix can then be configured around that site.Capacity can be expanded in modules rather than requiring a single, campus-scale commitment.
Smaller Expansion Steps
Add an appropriate increment of capacity rather than committing to one large build.
Broader Site Options
Consider industrial, energy-adjacent, brownfield and regional sites outside the most congested data-center clusters.
Workload-to-Site Matching
Place workloads based on their actual latency, compute, storage, data and availability needs.
Power-Aware Operation
Schedule flexible workloads around site and power conditions where service commitments permit.
Adaptable Capacity
Upgrade, expand or relocate capacity as demand and energy conditions change.
MEASURED SUSTAINABILITY
Sustainability is a system, not a label.
No single hardware choice or energy contract makes compute sustainable.The environmental profile of an AI deployment depends on the power source, workload efficiency, cooling method, climate, utilization, equipment life and the amount of infrastructure retained through each upgrade cycle.Scattered Metal’s model is designed to address these factors together.
Power
Configure capacity to suitable existing power conditions and prioritize lower-carbon or otherwise appropriate sources where technically and commercially viable.
Water
Use non-evaporative cooling configurations where climate and hardware allow, reducing direct cooling-water demand. Report both direct site water and the water associated with electricity supply.
Utilization
Use workload scheduling to reduce stranded or idle hardware, subject to customer commitments and operating requirements.
Materials & Lifecycle
Retain the enclosure, power, cooling, networking and safety infrastructure across multiple compute-component generations where practical.
Site Selection
Evaluate power availability, climate, water stress, network capacity, environmental constraints and community impact before deployment.
Performance and environmental outcomes vary by workload, hardware configuration, climate, power source and site conditions.
Lower density is not automatically lower energy. Air cooling is not automatically lower total water. The operating result must be measured.
A multi-generation platform, not a 20-year server.
Keep the infrastructure. Upgrade the compute.
GPUs, CPUs, memory and networking evolve quickly. The systems surrounding them should not become disposable at the same rate.Scattered Metal separates the fast-moving compute layer from the longer-lived module, power, cooling, network and safety layers. Hardware can be replaced in stages as workloads and component generations change.
Relocatable by design
Power contracts change. Regional demand changes. Site economics change.Modules are designed to be decommissioned, transported and recommissioned at another suitable location rather than becoming permanently stranded with one property or power arrangement.
Lifecycle principles
Component-level replacement.
Accelerator upgrades.
Memory and storage expansion.
Network refreshes.
Standards-based parts.
Planned spare-parts inventory.
Subsystem-level maintenance.
Reuse of retained infrastructure.
Responsible component retirement.
Relocatable does not mean instantly mobile. Transport, permits, civil work, power reconnection, network reconnection, testing and recommissioning are still required.
COMPLEMENTARY, NOT UNIVERSAL
A different layer of AI infrastructure.
Dense compute remains essential for the workloads that require it.Scattered Metal addresses the rest of the AI infrastructure landscape: workloads that can be placed, partitioned, served regionally or scheduled across distributed capacity.
| Attribute | Dense hyperscale infrastructure | Scattered Metal |
|---|---|---|
| Primary strength | Extreme scale and tightly coupled accelerator fabrics | Distributed, incremental and regional capacity |
| Deployment unit | Large permanent campus | Repeatable compute module |
| Density | Maximized for the facility | Balanced to power, thermal and workload conditions |
| Expansion | Large capacity increments | Modular capacity increments |
| Siting | Concentrated in established data-center regions | Broader set of technically suitable sites |
| Hardware model | Highly integrated platform | Standards-based, serviceable architecture |
| Lifecycle | Large coordinated refresh cycles | Staged component and subsystem upgrades |
| Mobility | Permanent facility | Relocatable module |
| Strongest workloads | Frontier training and large tightly coupled systems | Inference, batch, data processing, selected fine-tuning and regional AI |
| Sustainability opportunity | Scale efficiency | Siting, direct-water reduction, reuse, utilization and power alignment |
Not every AI workload should be forced into the same physical model.
Where distributed AI infrastructure creates value.
Regional AI Capacity
Add inference and processing capacity nearer to a user base or operating region.
Enterprise & Dedicated AI
Provide capacity for organizations that require greater physical or operational control than a shared public-cloud environment.
Data-Rich AI Pipelines
Place compute and substantial storage together for retrieval, preprocessing, analytics and model-serving workflows.
Media & Batch Processing
Run asynchronous generation, rendering and transformation workloads across independent workers.
Research and Simulation
Support parallel compute, synthetic-data and mixed CPU/GPU workloads.
Infrastructure and Energy Sites
Deploy compute at technically suitable sites with available power, network access and operating conditions.
Capacity Expansion
Add smaller increments of capacity while avoiding a single all-or-nothing campus project.
Two engagement paths
I need AI capacity
Tell Scattered Metal about the workload, region, data requirements and service objectives.
I have power or a site
Provide the location, available power, connectivity and site characteristics for an initial fit assessment.
FROM SITE FIT TO OPERATING CAPACITY
A repeatable deployment process.
Keep the infrastructure.
Upgrade the compute.
Site & Power Assessment
Evaluate power availability, quality, reliability, network connectivity, climate, access, permitting and environmental conditions.
Workload Profiling
Determine model types, data volumes, latency, throughput, storage, accelerator memory, availability and security requirements.
Module Configuration
Match servers, storage, networking, cooling and controls to the site and target workload.
Integration & Commissioning
Prepare the site, connect power and networking, complete environmental and operational testing and validate workload performance.
Fleet Operations
Monitor the module, schedule workloads, maintain components and plan upgrades through the operating lifecycle.
FAQ
| Inquiry | Response |
|---|---|
| What is Scattered Metal? | Scattered Metal is developing distributed AI infrastructure composed of modular, relocatable compute environments. The modules combine AI-capable servers, power distribution, cooling, networking, monitoring and safety systems and are designed to operate as a coordinated fleet. |
| Is each module a data center? | A module performs many of the functions of a data center, but it uses a smaller, repeatable and relocatable deployment model rather than a large permanent campus. |
| What AI workloads can it run? | Target workloads include inference, batch processing, embedding generation, retrieval pipelines, data preparation, generative media, simulation and selected fine-tuning. Final suitability depends on the model, hardware, networking, latency, data and service requirements. |
| Can it train large AI models? | The architecture may support selected training and fine-tuning workloads. Frontier-scale training across very large numbers of accelerators generally requires tightly coupled, high-bandwidth and low-latency infrastructure and is not the primary target. |
| What makes the design sustainable? | The model is intended to improve site selection, reduce local compute density, support non-evaporative cooling where appropriate, improve utilization, preserve infrastructure across hardware generations and integrate with suitable lower-carbon energy. Actual performance must be measured by deployment. |
| Does it use water? | Cooling can be configured to minimize direct site water, particularly when non-evaporative cooling is appropriate. Electricity generation can still carry an indirect water footprint, so both direct and source water should be measured. |
| Is it mobile? | The correct term is relocatable. A module can be decommissioned, transported and recommissioned, but movement still requires logistics, permits, power and network connections and testing. |
| Does the hardware last 20 years? | Individual CPUs, GPUs, memory and network components will require replacement much sooner. The goal is a longer-lived infrastructure platform that can support multiple generations of replaceable compute components. A specific 20-year claim should only be made after lifecycle validation. |
| Does it use commodity hardware? | The architecture is designed around standards-based, commercially available components. It can still use enterprise CPUs, GPUs and networking equipment. “Standards-based” does not mean consumer-grade or unspecialized. |
| Does Scattered Metal support Web3? | AI is the primary focus. Compatible decentralized-compute workloads may be supported when capacity is available and doing so does not conflict with AI commitments. |
| Is Scattered Metal cheaper than a hyperscale data center? | No cost comparison should be made until equivalent workload, availability, power, networking, cooling, site and lifecycle costs have been measured. The architectural advantage is a smaller, distributed and more serviceable unit of deployment. |
| Can modules be deployed anywhere? | No. Suitable deployments require adequate power, connectivity, climate controls, site access, security, permits and environmental conditions. |
EVALUATE A DEPLOYMENT
Bring AI capacity to a site that can support it.
Tell us about the workload, power, location or infrastructure opportunity.Scattered Metal will assess whether a distributed compute deployment is a technical and operating fit.Submitting this form begins a technical-fit review. It does not constitute a capacity, pricing or deployment commitment.Information submitted through this form will be used by Scattered Metal Inc. to evaluate and respond to your workload, site, power, or infrastructure inquiry.Submission does not create a contract, reserve capacity, establish a confidential relationship, or obligate Scattered Metal Inc. to provide pricing, services, equipment, or a deployment.Do not submit confidential, proprietary, sensitive, regulated, classified, or export-controlled information unless Scattered Metal Inc. has first authorized the submission under a written agreement.Please review our Privacy Policy before submitting personal information.
© 2026 Scattered Metal Inc. All rights reserved.
LEGAL DISCLAIMER AND IMPORTANT NOTICESLast updated: July 30, 2026This website, www.scatteredmetal.com (the “Site”), is owned and operated by Scattered Metal Inc. (“Scattered Metal,” “we,” “us,” or “our”). This notice applies to the text, images, diagrams, specifications, statistics, comparisons, descriptions, and other content presented on the Site.1. GENERAL INFORMATION ONLYThe Site provides general information about Scattered Metal, its technology direction, distributed AI infrastructure model, potential products and services, contemplated deployment methods, and possible applications.Site content is provided for informational and discussion purposes only. It is not professional engineering, architectural, electrical, environmental, cybersecurity, utility, safety, legal, tax, financial, investment, permitting, or regulatory advice. It should not be used as a substitute for a site-specific assessment performed by qualified professionals.No person should design, purchase, finance, install, operate, relocate, or rely on an infrastructure system solely on the basis of information presented on this Site.2. DEVELOPMENT STATUS AND COMMERCIAL AVAILABILITYDescriptions of Scattered Metal compute modules, Iron-Class servers, fleet operations, integrations, workload capabilities, cooling systems, power systems, monitoring systems, lifecycle features, and deployment processes may include existing designs, design objectives, planned capabilities, development work, proposed configurations, anticipated use cases, or concepts that remain subject to engineering, testing, validation, procurement, financing, regulatory review, and commercial approval.Unless Scattered Metal expressly confirms otherwise in a definitive written agreement:• No product, service, feature, configuration, capacity level, deployment location, component, supplier, specification, price, delivery date, or commercial availability is guaranteed.
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• Hardware and software compatibility.
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• Self-propelled.
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