Block-AI
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Block-AI
@7figuresoon1
New possibilities emerge with new technologies all the time | #Blockchain | #AI | #Bitcoin



The next wave of AI needs compute that’s efficient, flexible, and ready to scale ⚡️ Find the Ocean Network team at Pragma Cannes to talk pay-per-use GPU compute, Ocean Nodes, and how to access high-quality @nvidia GPUs through the Ocean Network dashboard See you in Cannes 🇫🇷👇




Building an AI model is easier than ever, until you’re paying for Idle GPUs. You hit a bug, pause to debug, maybe step away, but your instance keeps running in the background, burning money with zero progress. That’s the hidden “tax on thinking” most developers just accept. Ocean Network (@ONcompute) flips that: You only pay for actual execution time, and Jobs run in isolated containers directly from your IDE via Ocean Orchestrator. Payment is handled via escrow, so funds are released only for what actually runs. If a node fails, nothing is charged. If your code fails, you only pay for the compute that was used. Learn how to run on high-performance @nvidia H200s, without the usual cost pressure: docs.oncompute.ai/ocean-orchestr…

Ocean Network (@ONcompute) just bridged the gap between your IDE and global NVIDIA H200s starting from $2.16/hr, setting the new standard for permissionless AI infrastructure Go claim $100 worth of complimentary credits and start building⚡️





Unpopular opinion: If running a compute job still means bouncing between dashboards, terminals, and way too many tabs, the workflow is broken. Ocean Orchestrator brings containerized GPU compute jobs into your IDE, powered by Ocean Network (@ONcompute). Learn more👇 oncompute.ai/ocean-orchestr…

Decentralized compute has always had one weak spot: nodes fail, and your jobs go down with them. In a real P2P network, machines drop, connections break, and hardware isn’t standardized. That’s why most “rental GPU” platforms quietly drain time through retries, failed runs, and inconsistent results. We built Ocean Network (@ONcompute) so this stops being your problem: 1. Run on pre-qualified nodes: every machine is benchmarked before it ever touches your workload 2. Launch portable jobs: containerized execution packages your code, dependencies, and runtime, so it runs consistently across different nodes 3. Recover fast when things break: if a node goes offline or a container crashes, you see it instantly in your IDE with logs, and can rerun the exact same job on another node in seconds Open the dashboard, pick a GPU, and run your first workload with pay-per-use compute: docs.oncompute.ai/ocean-orchestr…








