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GM, Oceaners à @EFDevcon !

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Everyone’s talking about decentralised AI Very few are actually building it Ocean Protocol isn’t chasing hype. It’s building the rails - Privacy-preserving compute - Tokenized data access - Global network of nodes

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Earn crypto rewards using AI in our ASI Predictoor program! Submit UP / DOWN crypto price predictions to compete with other Predictoors for rewards based on accuracy. Put your AI / ML skills to good use! Join ASI Predictoor at https://predictoor.ai https://blog.oceanprotocol.com/df143-completes-and-df144-launches-2738bb2283ff

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Modern AI is starving for compute Training and inference need massive GPU power, but most of it is locked behind hyperscalers like AWS or Google Cloud This creates serious problems: 1. Startups & researchers get priced out 2. Surveillance & compliance risks rise 3. Local innovation gets crushed under central control Ocean Nodes offer an alternative, a decentralised, permissionless compute layer where: - Anyone can contribute idle GPUs/CPUs to the network - Developers can run containerised AI workloads (training, inference, validation) -All jobs are cryptographically verified with zero-trust security This turns DePIN from just hardware into intelligence Learn how Ocean Nodes are becoming the compute layer for sovereign AI:

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How Ocean Tech can act as the Data Layer for Open-Source LLMs LLMs need vast, diverse, high-quality datasets for: 1. Pretraining - large-scale text corpora 2. Finetuning - task or domain-specific data 3. Evaluation & Alignment - human feedback, bias mitigation, safety tuning Yet open-source LLMs often struggle with access, quality, compliance, and incentives Ocean Protocol solves this by transforming data into programmable, ownable, and tradable assets. LLM Lifecycle with Ocean: 1. Data Tokenisation - Researchers, DAOs, and institutions publish high-quality datasets (e.g. biomedical texts, code, low-resource languages) using Ocean CLI. Each dataset is wrapped as a Data NFT with ERC20 datatokens and registered on-chain. 2. Dataset Discovery - LLM teams can query Ocean for datasets by domain or metadata. 3. On-Chain Access - Access is granted via datatokens, enabling transparent and permissioned data use. 4. Compute-to-Data (C2D) - Instead of moving data, Ocean sends training jobs to where data resides. Privacy and compliance are preserved. 5. Monetisation - Each training run can trigger payments, rewarding data providers with usage-based royalties. Own your data. Train with Ocean

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CPUs and GPUs have powered decades of computing from desktop applications to large-scale enterprise systems CPUs are optimised for sequential tasks, ideal for logic-heavy operations and operating systems. GPUs, with thousands of smaller cores, are better at handling parallel operations like rendering and machine learning inference However, both have limits Modern AI workloads require massive parallelism and scalability that centralised infrastructure often struggles to provide due to cost, energy demands, and hardware limitations This is where distributed computing enters By aggregating underutilised compute, from gaming GPUs to idle enterprise resources, distributed systems offer a scalable, cost-effective, and energy-aware alternative to traditional cloud computing It’s not just more compute, it’s smarter, decentralised, and future-proof

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