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Meta is moving its AI infrastructure into a new class of scale. The company has announced a 1-gigawatt Prometheus AI cluster in Ohio, a Hyperion campus in Louisiana that is planned to expand to 5 gigawatts of compute capacity, and an El Paso campus designed to scale to 1 gigawatt. These are planned or expandable capacities—not proof that each site is already consuming that much electricity.
The spending is part of Meta’s broader artificial-intelligence investment. Meta expects 2026 capital expenditures of $125 billion to $145 billion, including servers, networking, data-center equipment, finance-lease principal payments and other infrastructure—not construction alone.
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Meta’s gigawatt-scale projects at a glance
| Project | Location | Announced capacity | Status or timing | Financing |
|---|---|---|---|---|
| Prometheus | New Albany, Ohio | 1 GW | Planned major AI cluster; Meta has indicated 2026 timing | Meta-led; power arrangements separately reported |
| Hyperion | Richland Parish, Louisiana | Up to 5 GW of compute capacity | Expanded in July 2026; phased buildout | Joint venture with funds managed by Blue Owl Capital |
| El Paso campus | El Paso, Texas | Up to 1 GW | Capacity expected online in 2028 | Venture with BlackRock; approximately $14 billion in associated development costs |
Meta’s descriptions use different terms. Prometheus is described as a “1-gigawatt cluster,” while Hyperion is described as having 5 gigawatts of compute capacity. Those figures should not automatically be treated as identical measurements of utility electricity demand.
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What does a gigawatt data center mean?
One gigawatt equals 1,000 megawatts. At data-center scale, however, “gigawatt” can describe different layers of infrastructure:
- Utility or site capacity: the electricity connection a campus may ultimately be able to draw.
- Facility power: electricity delivered to the buildings.
- IT load: the portion available to servers and networking after overhead.
- Compute capacity: a description of the usable computational footprint of an AI cluster.
That distinction matters. A 1-GW label does not reveal a fixed number of GPUs, how many machines are installed, how often they run, or how much useful AI work the site produces. Actual demand varies with construction phases, utilization, cooling requirements, hardware efficiency and the difference between peak and average load.
A campus of this scale also requires high-voltage transmission, substations, transformers, switchgear, backup generation, storage, cooling plants, water systems, server buildings, networking and extensive construction and maintenance operations.
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Training larger models
Training frontier language and multimodal models requires many accelerators to work in parallel. Larger clusters can allow more parameters, data and experiments, although additional hardware does not guarantee proportionally better models. Results also depend on algorithms, software efficiency, training data and networking.
Serving AI to billions of users
Training is only one demand source. Once a model is deployed, every interaction requires inference capacity. Meta is integrating AI features across Facebook, Instagram, WhatsApp, Messenger and other products. Those services need capacity for user requests as well as for recommendation, advertising, research and experimentation workloads.
Training tends to be concentrated and highly coordinated. Inference is an ongoing service workload. Both require expensive chips, power, cooling and network capacity.
Building more of its own technology
Meta is developing custom accelerators through its Meta Training and Inference Accelerator program and is working with Arm on data-center CPUs. Custom silicon may improve workload-specific performance, power efficiency and long-run cost, but it also requires major investment in chip design, software and deployment.
Meta’s announcements on custom AI silicon and its Arm collaboration show that the strategy extends beyond buying GPUs.
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The three major campuses
Prometheus in Ohio
Prometheus is Meta’s planned 1-GW AI cluster in New Albany, Ohio. Meta has described it as a major training cluster intended to come online during 2026. The project involves multiple data-center buildings, so the headline number should not be read as one building operating at full load on a specific date.
The important status distinction is between an announced target, construction progress, available power, installed equipment and operational compute. Public descriptions do not establish a precise GPU count or prove that the full 1-GW target is immediately available.
See Meta’s engineering overview and its 2025 highlights announcement.
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Meta says its Richland Parish, Louisiana, campus will expand to 5 GW of compute capacity, making it the largest data center in Meta’s fleet and home to its largest AI training cluster. That is an expansion of the original project scale, not a verified statement that 5 GW is already operating.
Meta says the project is expected to support more than 7,500 peak construction jobs and approximately 1,000 operational roles. Those figures are company estimates and distinguish temporary peak construction employment from permanent operations jobs.
The campus is being developed through a joint venture with funds managed by Blue Owl Capital. Meta has also said it will fund or support infrastructure related to power, affordability and clean energy. Local residents and policymakers still have practical questions about grid capacity, tax incentives, housing, roads, water use, emissions and whether the economic benefits reach nearby communities.
Meta’s claims about contracts, ratepayer protection and local economic benefits should be read as company statements, not independent findings. Relevant sources include the Hyperion expansion announcement and Meta’s report on Richland Parish contracts.
El Paso, Texas
Meta has described its El Paso AI-optimized campus as able to scale to 1 GW. A later venture with BlackRock put the total development cost for the buildings and long-lived power, cooling and connectivity infrastructure at approximately $14 billion, with capacity expected online in 2028.
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That $14 billion figure applies to the El Paso transaction and its associated development scope. It should not be used as a universal cost estimate for every gigawatt campus, nor divided mechanically to calculate a cost per gigawatt elsewhere.
How Meta is paying for the buildout
Meta reported approximately $69.7 billion in 2025 purchases of property and equipment. In its first-quarter 2026 results, the company raised its full-year capital-expenditure guidance to $125 billion to $145 billion, including principal payments on finance leases. Meta said higher component prices and additional data-center costs for future capacity were major reasons for the increase.
Meta also reported approximately $131.05 billion in contractual commitments at the end of 2025, mostly related to third-party cloud capacity, servers, networking, data centers and related infrastructure. Uncommenced lease obligations were approximately $103.77 billion, largely for data centers, colocation and network infrastructure, with terms extending from 2026 through 2030 and beyond.
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These figures are not interchangeable:
- Property-and-equipment purchases describe assets bought during a period.
- Capital-expenditure guidance is a forward-looking total covering more than buildings.
- Contractual commitments represent future obligations, not necessarily cash paid immediately.
- Lease obligations are financing and operating commitments that may extend over many years.
Meta’s Q1 2026 results and 2025 Form 10-K provide the underlying figures.
Why use joint ventures?
Joint ventures allow Meta to bring in infrastructure investors while retaining access to the facilities. Blue Owl is involved in Hyperion, and BlackRock is involved in El Paso. This can distribute construction costs and capital risk, but it does not mean Meta is spending nothing. Meta remains exposed through its equity contribution, contractual commitments, operating arrangements, power obligations and long-term use of the capacity.
The electricity challenge
AI campuses are increasingly power projects as well as technology projects. The supply chain runs from:
- Generation.
- Transmission.
- Substations and transformers.
- On-site electrical distribution.
- Backup generation and storage.
- Cooling systems.
- Servers, networking and compute.
Meta has announced nuclear-energy projects intended to support up to 6.6 GW of new and existing clean energy by 2035, including power for grids supporting its operations and the Prometheus supercluster. Supporting new generation, purchasing electricity, signing a power-purchase agreement and buying renewable-energy credits are different arrangements. None alone proves that a campus operates on carbon-free electricity every hour.
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Meta says it intends to pay for the energy and related infrastructure it uses so other customers’ electricity bills do not increase. That is a corporate pledge; the ultimate effect on rates depends on utility contracts, regulatory decisions, cost allocation and what happens if construction or demand changes. Key questions include who pays for transmission upgrades, whether new generation serves the campus directly or the broader grid, and whether the grid can support the advertised capacity simultaneously.
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Read Meta’s nuclear-energy announcement for the company’s stated plans.
What is inside a gigawatt AI campus?
The equipment mix may include Nvidia GPUs, AMD Instinct accelerators, Meta’s MTIA chips, custom CPUs, high-bandwidth memory, advanced semiconductor packaging, high-speed networking, optical interconnects, storage and data pipelines. A gigawatt total is not enough to calculate a reliable accelerator count because rack density, chip generation, cooling overhead, utilization and non-accelerator equipment differ.
Cooling is another constraint. AI racks generate substantially more heat than conventional web workloads. Possible approaches include direct-to-chip liquid cooling, rear-door heat exchangers, chilled-water loops, cooling towers, evaporative systems and closed-loop designs. The exact technology at a particular Meta campus should not be assumed without project documentation. Water consumption, electricity consumption, reliability, pump performance and leak protection all affect the economics.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What the investment could mean for Meta—and investors
Potential upside
- Faster model training and product experimentation.
- More control over AI features across Meta’s platforms.
- Lower cost per inference if utilization remains high.
- Less dependence on rented cloud capacity and outside suppliers.
- Better advertising recommendations and ranking systems.
- New consumer assistants and other AI products.
The risks behind the headline numbers
Demand risk: Meta could build capacity faster than demand develops, leaving expensive equipment underused.
Technology risk: More efficient models, new architectures or better accelerators could reduce the compute required for a given capability before the facilities are fully utilized.
Power-delivery risk: A completed building may not operate at full capacity if generation, transmission, substations or interconnection work is late.
Cost risk: Meta has already cited higher component prices and additional data-center costs in raising its 2026 outlook.
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Financing risk: Joint ventures can reduce Meta’s immediate funding burden but create debt, partner-alignment and contractual risks.
Community and regulatory risk: Projects can face disputes involving permits, tax incentives, environmental reviews, water, air quality, utilities and local infrastructure.
Accounting risk: New facilities and equipment increase depreciation and operating expenses. A lower cost per computation does not necessarily reduce total spending if Meta continues adding capacity.
What this means for households and smaller businesses
Meta’s campuses are not products that consumers can buy directly. Their broader financial significance is indirect: they may increase demand for chips, construction, electrical equipment, cooling systems, power generation and specialized labor. They may also affect local tax bases, utility planning, housing and infrastructure.
Organizations that need AI compute but cannot build a gigawatt campus generally rent capacity from cloud providers. AWS offers accelerated EC2 instances with public pricing for many configurations. Google Cloud offers GPU compute and TPUs. Owned infrastructure can make sense for consistently high utilization, but buyers must account for power availability, cooling, software compatibility, financing and deployment time—not just chip prices.
How to evaluate future announcements
- Identify the capacity layer. Is the figure site power, facility power, IT load or compute capacity?
- Check the status. Separate announced, expandable, under-construction, expected-online and operating capacity.
- Define the spending number. Determine whether it covers buildings only or also chips, networking, leases and power infrastructure.
- Follow the electricity. Look for generation, transmission, interconnection, storage, cooling and cost-allocation details.
- Separate company claims from verified outcomes. Job totals, ratepayer protections and local economic benefits may require utility filings, tax records or independent analysis.
- Ask about utilization and returns. A large facility creates value only if Meta can keep its hardware productive and monetize the resulting AI capabilities.
Meta is not merely buying more servers. It is assembling a vertically integrated AI infrastructure stack—chips, buildings, power, cooling, networking, financing and software. The strategic opportunity is substantial, but the headline gigawatt figures do not by themselves establish the projects’ final cost, emissions, utilization, delivery date or financial return.
Frequently Asked Questions
Is Meta building 5 gigawatts of electricity generation in Louisiana?
No. The verified announcement says Meta plans to expand Hyperion to 5 gigawatts of compute capacity. That is not the same as a disclosed plan to build 5 gigawatts of power generation.
Does Meta’s $125 billion to $145 billion outlook represent data-center construction spending?
No. The 2026 capital-expenditure guidance includes servers, networking, data-center equipment, finance-lease principal payments and other infrastructure, not just buildings.
Are Meta’s gigawatt data centers already fully operational?
The public descriptions cover planned, phased or expandable capacity. Prometheus has been associated with 2026 timing, while El Paso capacity is expected online in 2028. Announced capacity should not be confused with fully operating capacity.
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