Ocean Protocol

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https://blog.oceanprotocol.com/df144-completes-and-df145-launches-74bb81a3782a

Global GPUs/CPUs are now just a node away

Ocean Nodes give AI researchers on-demand access to scalable compute via a decentralised network no cloud lock-in, no inflated fees

Run large training jobs securely using Compute-to-Data, where your code goes to the data (not the other way around). Sensitive datasets stay private, and you pay only for what you use

Idle hardware from around the world, CPUs, GPUs, even specialised machines, can join the network and earn rewards, making computing cheaper and more abundant

The unified Ocean CLI handles it all: job discovery, scheduling, and results, no manual container configs or Kubernetes headaches

Build reproducible AI pipelines with built-in incentives and privacy

Learn more:

AI engineers, we get it, accessing high-quality data while staying compliant can be a real challenge

That’s why we built the Ocean VS Code extension

It lets you publish datasets, manage datatokens, and run compute-to-data jobs all from the comfort of your editor

No need to jump between tools. Just open VS @code and start building privacy-preserving, decentralised AI pipelines with full traceability baked in

Explore what’s possible:

What if your dApp could:
- Run machine learning models without exposing data
- Seamlessly offload compute to a decentralised network
- Reward both developers and node operators in one integrated flow?

Start here: https://docs.oceanprotocol.com/developers/ocean-node

What if you could license your dataset in minutes, not months?

With Ocean CLI:

1. Upload your dataset
2. Tokenise it with a Data NFT
3. Sell access with Datatokens

Smart contracts do the lawyering. You focus on research

https://docs.oceanprotocol.com/developers/contracts/data-nfts

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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https://blog.oceanprotocol.com/df143-completes-and-df144-launches-2738bb2283ff

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:

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

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

Gm

As AI models grow exponentially, traditional computing machines can no longer keep up

Ocean Nodes enable distributed computing, harnessing a global network of distributed, high-quality compute to meet the demands of modern AI training

Explore more:
https://docs.oceanprotocol.com/developers/ocean-node

The Ocean CLI tool is your gateway to privacy-preserving AI development

With a single command, such as "npm run cli publish path/to/metadata.json", you can easily tokenise your data into on-chain Data NFTs

Once tokenised, you can leverage Ocean Compute-to-Data to:

1. Perform federated learning on private datasets
2. Train ML models on fresh, real-world data
3. Build data cooperatives, all while staying compliant

Learn how it enables data scientists, AI developers, and researchers to unlock data value securely

It’s Web3 meets DataOps, built for the age of AI superintelligence

Ocean Data NFTs let data creators own, control, and monetise their data, without giving it away

They also power privacy-preserving AI pipelines with token-gated access and crypto incentives

Learn more:

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https://blog.oceanprotocol.com/df142-completes-and-df143-launches-662d524e3ef8

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AI folks, we’ve all hit this wall

The traditional “download data → train model” workflow is outdated and risky

Ocean C2D has a solution!

Instead of moving the data to your model, Ocean C2D lets you send your ML/AI algorithm to the data in a secure, sandboxed environment, where only insights are returned

This is made possible by:

1. Containerised execution (e.g., Docker, Kubernetes)
2. On-chain access control (via data tokens)
3. Decentralised orchestration across compute providers via Ocean Nodes

Read how it bridges the gap between data privacy and AI utility:

Did you know?

The Ocean VS Code Extension lets you test and run compute jobs directly from your editor, no external tools required

Simulate Ocean network jobs locally in a built-in sandbox, speeding up AI workflows with zero blockchain overhead

https://docs.oceanprotocol.com/developers/vscode

Make your Data an asset class with Ocean CLI

It's a powerful, scriptable tool that lets you interact with Ocean core features. Here’s what you can do with it:

1. Publish data services - Create a Data NFT, datatoken, metadata DDO, and set a price in one go (whether it’s a downloadable file or a Compute-to-Data service)
2. Update assets anytime - Easily edit metadata and pricing post-publish
3. Consume data assets - Order datatokens and download content with a single command
4. Enable Compute-to-Data - Using approved algorithms, launch decentralised compute jobs on your data.

To get started, clone the Ocean CLI repository, set your environment variables (e.g., PRIVATE_KEY, RPC, NODE_URL), and run commands using "npm run cli"

Try it here:

Data privacy shouldn't be optional in the AI era

Ocean Protocol provides the tools to build responsibly:

1. Compute-to-Data - Run algorithms without exposing data
2. Data NFTs - Own, tokenize & monetize your datasets
3. Ocean Nodes - Decentralized, low-latency compute for AI at scale

🔗

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Read the weekly digest here:

https://blog.oceanprotocol.com/df139-completes-and-df140-launches-ddec0479cdd8

Ocean Compute-to-Data lets AI models train on private data, without exposing the data

In sectors like Transportation & IoT, this unlocks powerful, privacy-preserving AI:

-Vehicles & sensors become secure Data Nodes
-AI companies buy compute access, not raw data
-Algorithms run at the source, insights only
-C2D scales to thousands of nodes

🔗