Why does $CORE have real value? Because it powers real AI infrastructure. Core Compute is building physical GPU data centers and sharing real infrastructure revenue with the community. Launching on @CyreneAI on 11/03/2026 at 4:00 PM UTC. $CORE is used for: • Accessing

@Core_Compute | Showcase Stream hosted by @TonyCatoff https://t.co/xBhSpAayIv

Revenue at Scale Projection If one site at 75% utilization = $821K revenue 100 sites → $82M+ annual revenue potential Even at lower utilization, scale impact becomes exponential. Infrastructure compounds. Token demand compounds.

Expansion Plan: 100 data centers Timeline: 2–4 years 1 site = 500 GPUs 100 sites = 50,000 GPUs Now compute this: 50,000 GPUs → 145 Billion TOP-hours yearly output That is sovereign-scale compute infrastructure.

Strategic Partnership @Core_Compute 🤝 @TheImperiumLabs ImperiumLabs is backing the next generation of decentralized infrastructure, and Core Compute is building it. Through this partnership, ImperiumLabs will support the expansion of Core Compute’s global GPU infrastructure

Sustainability Metrics: 60% renewable energy mix (solar + grid) Optimized cooling systems Energy provider partnerships Future integration potential: • Carbon credit tokenization • ESG-aligned staking models • Institutional green capital alignment AI infrastructure without

Asset Position: Physical Infrastructure Value: $2,000,000 Annual Revenue (75% utilization): $821,250 Land + hardware backed Operational facility in Kazakhstan Certifications & Standards Alignment: • ISO/IEC 27001 • ISO/IEC 42001 • SOC 2 • Uptime Institute Tier Standards •

Staking Mechanics: Silver Tier → Base discount Gold Tier → Higher discount + revenue boost Institutional Tier → Priority allocation during peak demand Stake → reduce circulating supply Lock → earn server rental yield Hold → benefit from buyback pressure AI companies

$CORE Supply Model: Fixed Supply: 1,000,000,000 Chain: Solana Utility Stack: • Revenue share (30%) • 10% profit buyback • Staking tiers • Compute discounts • Enterprise priority access Every growth layer increases token sink.

Revenue Mechanics (Current Site) At 75% utilization: Estimated Annual Revenue: $821,250 Operational Cost: $646,400 Gross Operating Margin: $174,850 Now layer token mechanics: • 30% of monthly revenue → $CORE holders • 10% of profits → Buyback & Burn • 20% compute

Current Deployment Breakdown: • 70% RTX 5050 • 10% RTX 5060 • 10% RTX 4070 • 10% NVIDIA H100 Total: 500 GPUs Focused on AI model training workloads. Total setup cost (high-end build): $2,000,000 USD Annual operating cost: $646,400 USD Physical hardware. Operational

Let’s quantify real compute power. 500 GPUs Monthly Output (730 hrs): 120.45 Million TOP-hours Yearly Output (8,760 hrs): 1.45 Billion TOP-hours At 75% utilization: 1.08 Billion effective TOP-hours annually $821,250 estimated annual revenue $68,400+ monthly revenue This is AI

Jeff Bezos claims that in the future, you will not buy a gaming PC. You will only rent computing power in the cloud to play games online. He called it inefficient and predicted it won't last, saying people will instead rent computing power from the cloud, much like buying

OpenAI is close to finalizing the first phase of a new funding round that is likely to bring in more than $100 billion, sources say

We’ve identified industrial-scale distillation attacks on our models by DeepSeek, Moonshot AI, and MiniMax. These labs created over 24,000 fraudulent accounts and generated over 16 million exchanges with Claude, extracting its capabilities to train and improve their own models.

⚡️ BIG: Meta has reportedly signed a multibillion-dollar deal to lease AI chips from Google.

AI demand is exploding. Global AI infra spend: $150B+ yearly. GPU shortages persist. Cloud costs up 20–40% YoY. Startups wait months for capacity. Core Compute didn’t. We deployed: • 500 GPUs • 75% utilization • 100 paying clients • 20 AI startups training

Imagine a world with Core Compute operating in: 5 locations → 10 locations → 25 locations. Thousands of GPUs. Millions of compute hours. A real backbone for the AI economy. That’s where we’re going.

Core Compute isn’t just building GPU racks. We’re building a world where: A student can train. A founder can scale. A startup can compete. A company can deploy AI without begging cloud providers. That’s the mission.

AI compute demand is rising because: ● models are getting larger ● inference is running 24/7 ● agents are always online ● content generation is mainstream now This isn’t optional compute. This is required compute. Poster Visual: 4 bullets poster