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Meta to Power New AI Data Center With $100M Solar Farm

Meta signed a deal with Silicon Ranch for a $100 million, 100 MW solar farm to power its planned $800 million AI data center in South Carolina. The project—Meta’s 18th with Silicon Ranch—starts operations in 2027, uses mostly U.S.-made equipment, and follows a year in which Meta added over 2 GW of solar capacity.

Published August 20, 2025 at 12:13 PM EDT in Cloud Infrastructure

Meta and solar developer Silicon Ranch announced a new $100 million, 100-megawatt solar farm in South Carolina to power Meta’s planned $800 million AI data center. Both facilities are slated to begin operations in 2027, and companies say most solar equipment will be made in the U.S.

This agreement marks the 18th deal between Meta and Silicon Ranch, a partnership that the developer says has helped spur more than $2.5 billion in investments. The announcement follows an aggressive buildout by Meta this year, during which it added over 2 gigawatts of solar capacity and signed multiple large deals across Ohio, Kansas, Texas, and elsewhere.

Why hyperscalers keep betting on solar

For companies building sprawling AI-capable data centers, solar brings two clear benefits. First, it helps meet net-zero and renewable procurement targets that matter to customers, investors, and regulators. Second—and often more operationally urgent—solar can be contracted and brought online faster and cheaper than many other power options, reducing the time-to-power bottleneck that can delay a data center’s revenue-generating operations.

What this deal means for data center strategy

  • Faster time-to-power: solar PPAs and onsite arrays compress the timeline from construction completion to energized operations.
  • Cost predictability: long-term power agreements hedge against volatile wholesale markets, lowering operating expense uncertainty for compute-heavy AI workloads.
  • Local economic impact and supply-chain considerations: U.S.-made equipment supports regional manufacturing jobs but also requires coordination on logistics and permitting.

But solar is not a silver bullet. Developers must manage grid interconnection timelines, potential curtailment, and the need for storage or complementary generation to deliver firm capacity for 24/7 AI workloads. Policymakers and utilities will need to work with hyperscalers to ease permitting and expand transmission where needed.

Practical implications for operators and developers

For data center operators, this means aligning three timelines: facility construction, power delivery, and equipment commissioning. Misalignment can leave expensive compute capacity stranded. Think of it like building a factory but waiting months for the electricity to arrive.

  • Key focus areas: interconnection study schedules, PPA contract terms tied to construction milestones, and local workforce planning.

Meta’s South Carolina project underscores a broader pattern: hyperscalers are increasingly embedding power procurement into site selection and capital planning rather than treating it as an afterthought.

How analytics and scenario planning help

Data-driven scenario modeling is the practical tool for managing these risks. Organizations that quantify time-to-power outcomes, model curtailment and storage needs, and stress-test supply-chain delays can prioritize projects that open sooner and at lower total cost. That’s the approach we bring to complex renewables-plus-data-center rollouts—combining build schedules, grid constraints, and contract economics into clear, actionable scenarios.

As Meta’s deal shows, pairing large-scale compute with dedicated renewable capacity is now standard practice. For regional planners, utilities, and corporate energy purchasers, the challenge is making sure the power and the compute arrive on the same schedule so investments deliver value from day one.

Expect more of these bundled announcements: they accelerate renewables deployment, concentrate economic activity in host regions, and tighten the link between cloud infrastructure strategy and energy markets. For organizations planning AI-capable facilities, early coordination between real estate, procurement, and grid stakeholders is now essential.

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