Reasons Nuclear Plants Power Data Centers

AI’s exponential energy appetite is quietly rebooting America’s nuclear industry. In 2024, Big Tech had a critical realization: artificial intelligence isn’t just a software revolution - it’s a thermodynamic one. Training a single GPT‑4‑class model consumes ~500 MWh, that’s enough to power ~15 U.S. homes for a year. But inference is the real sinkhole. It’s always-on, everywhere, all at once. AI server racks consume >100 kW per rack, 10x more than traditional racks. Renewables can’t keep up. The sun sets. The wind stalls. Batteries are expensive, and at this scale, insufficient. Clean power isn’t the same as reliable power. And for 24/7 inference, only one option checks every box: nuclear - clean, constant, controllable baseload power. So what do trillion-dollar firms do when they realize their business model runs on electrons? They start buying the grid. ▪️ Microsoft partnered with Constellation Energy to restart Three Mile Island Unit 1 by 2028, supplying 835 MW of baseload power to its AI data centers - the first large-scale restart of a decommissioned U.S. reactor. Oh, and it’s betting on fusion too: Microsoft’s backing Helion, targeting the first commercial fusion prototype by 2028. When you have Microsoft money, you can place moonshots on the sun. ▪️Google is doing what Google does: building a portfolio. It inked a deal in October 2024 with Kairos Power for molten-salt SMRs (6–7 reactors by 2035, first demo 2030). Two weeks ago, it added Elementl Power - 1.8 GW of advanced nuclear capacity. ▪️Amazon Web Services (AWS) locked down up to 1.9 GW from Talen Energy's Susquehanna plant and, last year, dropped $650 million to buy a nuclear-powered data center campus outright. ▪️Meta finally joined the party last week, signing a 20‑year Purchase Agreement with Constellation to draw 30 MW from the Clinton nuclear plant in Illinois. The capacity is modest, but it signals a strategic shift - away from carbon offsets and toward operational baseload coverage. Even Meta sees the writing on the grid. This isn’t a hypothetical future - it’s happening now.  3 major nuclear PPAs signed within 2 weeks. Soaring federal support. Billions in private bets. What began as a GPU arms race is now an energy land grab. The next big AI breakthrough might not be a model, it might be a reactor.

NVIDIA Just Backed a Nuclear Future for AI. Yes, the same company designing chips that power ChatGPT is now investing in the energy source needed to keep it running. NVIDIA’s venture arm, NVentures, just joined TerraPower’s $650 million funding round to develop small modular reactors (SMRs), compact nuclear power plants tailored for the next generation of AI-hungry data centers. 🧠 + ⚛️ = The infrastructure of the future. Why? Because powering AI isn’t like powering your fridge. We're talking gigawatts of continuous, stable, clean power, something wind and solar alone can’t deliver 24/7. Just ask the engineers building these systems: they’re already running into limits. TerraPower, founded by Bill Gates, has raised over $1 billion from private investors and secured $2 billion from the U.S. Department of Energy. Their Natrium reactors combine nuclear fission with grid-scale thermal storage, making them flexible enough to match fluctuating AI workloads. 🚀 First launch: Wyoming, 2030. A dozen more SMRs are in the pipeline. Nuclear power isn’t returning because it’s trendy. It’s returning because nothing else can do the job. If we want AI, we need to power it, safely, cleanly, and endlessly. And that future might just be nuclear. Would you trust a reactor to power your next AI assistant? Source: https://lnkd.in/dEhmc9vM #NuclearEnergy #NVIDIA #TerraPower #SMR #DataCenters #AIInfrastructure #CleanEnergy #EnergySecurity #Natrium #BillGates #NVentures #EnergyForAI #Wyoming2030 #AdvancedNuclear #FutureOfPower #EnergyTransition

⚛️ Could Nuclear Be the Secret Weapon for AI-Powered Data Centers? ⚡ As AI accelerates demand for massive, 24/7 power across hyperscale campuses, Deloitte’s latest report lays out a compelling case for nuclear energy as the backbone of future data center infrastructure. And the numbers are staggering. 🔋 Why Nuclear Makes Sense for AI: ✅ Reliable Baseload Power – With a 92.5% capacity factor, nuclear is unmatched for consistent, around-the-clock power 💡 ✅ Compact Energy Density – A small amount of fuel generates huge amounts of electricity, ideal for high-density colocation ⚙️ ✅ Clean Emissions Profile – Near-zero greenhouse gas emissions during operation 🌱 ✅ Big Output, Small Footprint – A single reactor can generate 800+ MW—enough for a cluster of high-performance AI factories 🧠🏭 📈 Deloitte’s Forecast To meet rising data center demand, the U.S. may need to add 35–62 GW of new nuclear capacity—enough to power 10% of projected growth over the next decade. 🧠 AI Needs Nuclear, and Vice Versa As inference workloads soar and compute demand becomes “always-on”, only firm, scalable, zero-carbon power like nuclear can keep pace. Pathways to Scale: 🔧 Optimize existing reactors (many relicensed for 80+ years!) 🔁 Restart closed nuclear plants 🔬 Deploy Small Modular Reactors (SMRs) and microreactors for flexible siting 🗣️ “Advanced nuclear could be the infrastructure enabler for next-gen data centers.” 📄 Full report by Deloitte 👉 https://lnkd.in/d3n6TTVg #AI #DataCenters #NuclearEnergy #EnergyTransition #Hyperscale #DeloitteInsights #SMRs #CleanPower #Sustainability