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    Colossus 2
    Posted on Monday, October 06, 2025 @ 17:30:15 UTC by vlad

    General
    From Anastasi In Tech YouTube channel: World’s Largest AI Supercluster — What No One’s Telling You

    There is a Colossus effect underway in computing. Something so massive it's reshaping the entire energy industry, computing and even geopolitics.  Let me show you. Back in March, this was an abandoned factory. Just 6 months later, Colossus 2 stands here. A machine the size of a city built not for people but for intelligence. 

    It's nearly a million GPUs under one roof eating up a gigawatt of power. This is the story of the world's first AI gigafactory and the insane journey took to build it...


    I am an engineer who spent  a decade building the most critical chips for this technology. That's why I'm so excited about this particular story and its impact. Subscribe to the channel right now and let me explain. Today,  Amazon, Microsoft, OpenAI, and Meta, everyone is pouring billions into AI factories because the  race isn't just about smarter models anymore. 

    Every new generation needs at least 10 times more compute power. So, the hyperscalers are no longer just running datacenters. They are building power plants to fuel AI faster and cheaper.

    Amazon needs it to defend the cloud. OpenAI to push the frontier. Meta just to catch up. So slowly but surely AI labs are turning into energy companies. And now comes xAI.

    Last September, they shocked the industry with a Colossus 1. When just in 122 days, they turned an empty shell into one of the world's largest AI training sites. Then it took them 19 days, just 19 days, to wire and to deploy 100,000 NVIDIA GPUs. That alone was historic ... the  speed, the scale.

    No one in the industry have ever built like this before. But today's story is about  something far bigger. Colossus 2. And on paper, it might look like just another datacenter, but in reality, it's four giants rolled in one. Just imagine at peak, it will draw up to 1.2 gigawatts of power. And that's a lot. That's enough to keep the lights on in more than 2 million homes. So what does it take to build a gigawatt datacenter?

    And here it's getting interesting because you actually don't start with GPUs. You start with power in every sense of this word. Without gigawatts of stable energy, the racks stay dark.

    And that's where Colossus 2 becomes fascinating. Imagine you want to build a datacenter. The first challenge is simple and brutal. Where do you find  a gigawatt of power? And even if you secure that much power, how do you push it into one building without frying the grid? At this scale, you have to actually build a new electrical backbone. You need substations, switch gear, transformers, backup, and distribution lines feeding straight into the racks. This means when you're building an AI datacenter, the first thing you have to build is a power factory...

    More: https://youtu.be/RxuSvyOwVCI

     
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    The 80% Collapse: How Decentralized LENR Rewrites the Energy Economics of AI Data Cen (Score: 1)
    by vlad on Saturday, January 03, 2026 @ 18:02:49 UTC
    (User Info | Send a Message) http://www.zpenergy.com
    From https://newfireenergy.substack.com : The 80% Collapse: How Decentralized LENR Rewrites the Energy Economics of AI Data Centers / Why the Future of AI Isn’t Bigger Grids or Micro-Nuclear but Power Generated at the Point of Use

    The exponential growth of Artificial Intelligence (AI) is rapidly colliding with a fundamental constraint: energy. Modern AI data centers, characterized by extreme power density and insatiable demand, are becoming the single largest drain on global power grids. However, a radical shift is emerging from the fringes of energy science. Assuming the successful commercialization of Low-Energy Nuclear Reaction (LENR) technology, specifically the megawatt-scale E-Cat NGU, a deep analysis reveals that this decentralized power source could replace or eliminate up to 80% of the total energy processes and associated infrastructure currently required to power and operate a traditional AI data center.

    This is not a claim of reducing the IT load itself, but a fundamental re-engineering of the entire energy supply chain, cooling, and reliability infrastructure that constitutes the majority of a data center’s non-IT overhead.

    The modern AI data center is a marvel of engineering, yet it remains fundamentally inefficient due to its reliance on a centralized, external power grid. The industry uses the Power Usage Effectiveness (PUE) metric to quantify this inefficiency:...




     

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