AION owns and operates GPU compute infrastructure for AI, offered through fractional hardware ownership https://xitter.cf/t.co/C8nozjhFT0 https://xitter.cf/t.co/S8fX5IH0cQ

Metaverse
Joined February 2026
Most AI compute projects ask you to trust a token. We’d rather show you the GPU. AION Quests are live. Season 1: No Black Box. → Learn what a Compute Unit actually is → Find the real hardware behind it → Climb the weekly leaderboard Link below 👇
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Two Cards, On Purpose. B200: up to 192GB, NVLink, built for big models. RTX 5090: 32GB, high-throughput inference, image gen, fine-tuning. Two jobs. Two kinds of renter. One serial number per Device either way 👇
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People see RTX 5090s on our fleet and ask: "isn't that a gaming card?" Yes. It's also one of the best tools for a huge slice of AI work. Why we run it next to the B200, on Medium at lunch.
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If you co-owned part of a GPU, what's the one thing you'd want to see every morning? Rented or idle? Hours this month? Device health? Where it sits in its life cycle? Tell us. We read every reply.
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The least useful number in GPU ownership: the hourly rate. The most useful: hours actually rented. A Device rented most of the month at a modest rate can bring in more than a premium one idle half the time. Rates get headlines. Hours do the work.
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Ask any AI compute project one question: “Which GPU is mine?” Most can’t answer. You get a token, a dashboard, and a promise. At AION, every Compute Unit maps to a specific, serial-numbered GPU. A real B200 or RTX 5090, in a real rack, rented to real AI teams. That’s what No Black Box means. What’s the first thing you’d want to see before trusting a compute project?
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A single B200 can draw around 1,000 watts. Power, cooling, rack space, bandwidth, maintenance, people. Any GPU pitch without a fee line is either very generous or very vague. Ours is disclosed at checkout.
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Why device-level and not one big pool? A pool smooths everything out. It also hides which machine is actually working. We'd rather you see your Device, even on a quiet week. Pool or device-level: which would you pick?
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It’s open. 5 quests, one rule: no black box. 👇 zealy.io/cw/aioncomputecrew/…
Most AI compute projects ask you to trust a token. We’d rather show you the GPU. AION Quests are live. Season 1: No Black Box. → Learn what a Compute Unit actually is → Find the real hardware behind it → Climb the weekly leaderboard Link below 👇
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Follow the Hour. The hour lands on one Device Disclosed fees come off What's left splits 40 ways Your share follows your Fractions No pool. No smoothing. No floor
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A renter pays for one GPU hour. Where does that money actually go? Most pitches stop at the top line. We're following it all the way to a single Fraction. Medium piece drops at lunch.
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Honest question. What would make you trust a GPU ownership product? A: a serial number for every machine B: live rented-or-idle status C: an independent copy of the ownership register D: something else (tell us) Reply in comments
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The question we get most: "What happens if nobody rents it?" Straight answer: for that period, there's nothing to attribute. Zero. The hardware still ages. The clock runs either way. That's why we talk about hours, not headline rates. And why we'll never promise you a number.
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Monday read on AI compute: B200 listings this morning: $3.20/hr (spot, sold out) to $17.80/hr across 38 providers. AWS: $14.24. RTX 5090: $0.25 to $2.52 across 20. Same silicon, a 5x+ spread. Price is the headline. Hours are the story. This week we follow one rented hour all the way down.
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Before next week: What's one question about GPU ownership you've never seen answered straight? Drop it below. The best ones become next week's posts
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Sunday reading. This week's three AION pieces, in order: 1. Who Actually Rents a GPU? 2. The GPU Hour, Decoded 3. Inference Is Eating the World All on Medium 👇 medium.com/@aioncloud.io
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Unpopular take: the most important number on any GPU isn't its speed. It's how many hours a month it's actually working. A fast chip that sits idle is just a fast way to depreciate.
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Weekend question: Agents, reasoning models or AI video. Which burns the most GPU hours by 2027? We have a guess. Want yours first.
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