2980
A decentralized data exchange protocol
Agentic AI changes what matters in GPU infrastructure. It's not just FLOPs anymore.
Long-context reasoning, large KV caches, retrieval pipelines, and concurrent tool calls make memory capacity a first-class constraint.
The NVIDIA H200 was built for workloads like these. Ocean Network provides on-demand H200 access from $2.16/hr on a pay-per-use basis.
Run agents on infrastructure built for them: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2062224795979149545
$2.16 won’t buy you a latte.
$2.16 won’t get you a parking spot downtown.
But $2.16 will get you an NVIDIA H200 on Ocean Network for 1 hour.
In 10 hours, you could fine-tune an open model, run large-scale inference workloads, process massive embedding pipelines, or train experiments back to back without dealing with infrastructure overhead.
With 141GB of HBM3e memory and 4.8TB/s bandwidth, the H200 handles workloads that are still inaccessible to most developers, and 10 hours would cost roughly $21.60.
Most people still assume this class of compute is reserved for hyperscalers and well-funded AI labs.
Access it here: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2060400783816892724?s=20
Most AI teams obsess over model quality while ignoring the biggest cost in the stack: compute utilization.
An NVIDIA H200 is $2.16 per hour on Ocean Network. The same compute can cost several times more on traditional cloud infrastructure, depending on the provider and availability.
One unit of H200 compute is enough to:
Fine-tune a model with QLoRA
Generate synthetic training data
Run large-scale inference
Plus, Ocean Network charges you only for what you use, with no vendor lock-in, and payments are made through escrow. Explore available environments: dashboard.oncompute.ai/run-job/enviro
https://x.com/ONcompute/status/2058910465099497595?s=20
The majority of LoRA, QLoRA, and fine-tuning practitioners are wasting time switching between their IDE and cloud consoles just to provision a GPU.
Ocean Orchestrator brings IDE-native GPU compute orchestration into VS Code, Cursor, Antigravity, and Windsurf. Select your exact specs on the Ocean Network Dashboard, including NVIDIA H200 on-demand access at a fraction of hyperscaler rates, and bring that instance into your IDE on a pay-per-use basis.
Your logs, outputs, and results land directly in your workspace. You never leave the IDE.
Install the Ocean Orchestrator extension and start your next fine-tune in minutes: https://open-vsx.org/extension/OceanProtocol/ocean-protocol-vscode-extension
https://x.com/ONcompute/status/2057070583641248127?s=20
GPU queues are a relic of a world that didn't know better. We fixed it.
Fine-tune 70B models, run inference, and process large-scale embeddings on demand on @nvidia H200s through Ocean Network at ~$2.16/hr.
The compute you need, the moment you need it: dashboard.oncompute.ai
https://x.com/oncompute/status/2055306710647296279?s=46&t=sfyIS0XeZHZd-w68hBLkvw
An NVIDIA H200 has 141GB of HBM3e memory and rents for roughly $2.16/hr on Ocean Network ( ONcompute ), with jobs running up to 12 hours.
In that time, a small team can run a serious LoRA/QLoRA fine-tune of Llama 3.1 70B on proprietary data, experiment with different training configs, and end the day with a domain-adapted model tuned for their exact workflow.
On narrowly defined internal tasks, these models can outperform frontier general-purpose LLMs.
Many startups spend thousands per month locking themselves into reserved GPU cloud contracts just to access this kind of capability.
On the Ocean Network Dashboard, you spin up an H200 node, run the job, and pay only for the compute you use: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2053870245786308893
We're opening a few spots for Ocean Network ambassadors. Step in and help shape how the world accesses compute.
1. What you'll do: Take something complex and make it click. Break down how Ocean Network turns compute into a commodity and peer-to-peer verifiable computation into a global market. Use your format: videos, tutorials, infographics, X carousels. Show up where real conversations are happening.
2. How it works: You apply, we review, you get into a private Discord, pick your first task, and you're in. From there, it runs in monthly cycles: you create, submit, and keep building.
3. Rewards: Your work earns points based on quality, reach, and engagement. The better your content performs, the bigger your share, with bonuses for top contributors.
All the details here: https://oncompute.ai/blog/ocean-network-ambassador-program
https://x.com/ONcompute/status/2052355511165628592?s=20
A long time ago in a data center far, far away… someone was paying for idle GPUs.
Not anymore.
May the 4th be with you. 🌊
https://x.com/oceanprotocol/status/2051293635551834256?s=20
Ocean Network (@ONcompute) is a decentralized compute network where everyone wins.
Users define the resources, pay for what they consume, and the moment the job is done, the billing stops, all secured through escrow-backed payments.
Developers can launch containerized jobs directly from their IDE: every execution isolated, every run reproducible, with signed attestations and on-chain provenance built in.
GPU owners can monetize their hardware by joining the network and earning from real compute jobs routed their way.
And the cherry on top?
Strictly benchmarked NVIDIA GPUs for under $3/hr:
https://x.com/oceanprotocol/status/2049534426242072793?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Fresh compute nodes are live on Ocean Network 🥞
Last week, we launched the Incentives Program, and GPU owners have been putting their compute to work ever since.
Every node has been strictly benchmarked to handle heavy workloads, full scores and history are right there on the dashboard.
Pick your node, filter by GPU, CPU, RAM, disk, and duration. Submit your job and pay only for what you use, locked in through escrow-secured payments.
Stop sitting on your project.
Verified compute is ready when you are: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2048771652071133204
Barry found Ocean Network on Monday, and now he's out here passing benchmark jobs like a pro.
Every pass earns him a share of the 2,000 $USDC weekly rewards pool.
Your GPU could be Barry.
Stay tuned. 🐙
https://x.com/ONcompute/status/2046944613756502370?s=20
The gap between "I have a model to train" and "my job is running on an H200" should not be measured in days.
Ocean Network runs on pay-per-use pricing with escrow-secured payments, and three features make it genuinely hard to go back to anything else:
1. Test your exact workload on real compute before spending anything
2. Take your compute environment inside your IDE via Ocean Orchestrator
3. Spin up the best GPUs on the market with no waitlists
Get started: https://docs.oncompute.ai/
AI agents are built to make things faster and more efficient.
Their training should be too. Instead, you're either overpaying for compute or stuck in GPU queues just to run inference or pretraining.
Fix that with Ocean Network:
1. Define your requirements: Pick the exact node you want and run jobs with pay-per-use pricing and escrow-secured payments.
2. Explore before you commit: Browse the most powerful nodes in the network through the dashboard leaderboard before running a single job.
From training and inference to agent workflows, efficiency belongs at the compute layer too.
Start running on Nvidia H200s for under $3/hr:
https://x.com/ONcompute/status/2044792729670934951?s=20
Good news: you don’t need to leave your editor to run GPU jobs anymore.
You write the code, click run, and Ocean Orchestrator handles everything on remote compute.
Here’s how to get started:
1.Write your job in Python, JavaScript, or bring your own custom Docker image
2.Pick a node from the Ocean Nodes Dashboard (ONcompute), select your specs, and send the job to your chosen remote GPU
3.Monitor logs and job status live inside your editor
4.Receive outputs automatically in your results folder without chasing files around
Behind the scenes, Ocean Orchestrator handles container setup, Compute-to-Data execution, and node coordination, so you don’t have to think about infra at all.
https://x.com/oceanprotocol/status/2043712069782954282?s=20
EthCC, you sure left a mark! Still carrying the energy from last week
Now we’re ON to turning all that momentum into real impact on our network. Let’s see what we ship this week 👀
PS: Spot yourself on the wall? Tag away!
https://x.com/oncompute/status/2041149908762128624
Bring the idea. We'll handle the universe it runs on.
Browse on-demand GPUs from the Ocean Network Dashboard. Select the hardware your workload needs. Pull that environment directly into your IDE with Ocean Orchestrator.
Run inference, fine-tuning, embeddings, or agent workloads without provisioning infrastructure or managing servers. Containerized compute jobs execute on a remote chosen node and return results directly to your workflow.
The dashboard finds the compute. Ocean Orchestrator puts it to work.
So, what will you build?
https://x.com/ONcompute/status/2061846805185245335?s=20
NVIDIA H200s are becoming one of the best GPUs for multi-agent AI workloads.
Agent systems create massive KV cache pressure, parallel reasoning demand, and long-context memory strain across multiple active inference streams.
That's exactly where H200s shine, with 141GB HBM3e memory and massive memory bandwidth built for high-concurrency AI workloads.
Access them through the Ocean Network Dashboard from just $2.16/hr on a pay-per-use basis: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2060013979351564567?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Many compute providers bill by the hour. Your workloads don't run by the hour.
With Ocean Network, you pay for execution time and not a cent more, even on the NVIDIA H200.
Access some of the most affordable GPUs on the market, configured exactly to what your experiment needs: GPU, CPU, RAM, disk space, and runtime.
Run a 22-minute fine-tune and pay for 22 minutes.
That's it: dashboard.oncompute.ai
https://x.com/oncompute/status/2057474746799792588?s=46&t=sfyIS0XeZHZd-w68hBLkvw
If you're building multi-agent systems, you've probably already noticed how fast orchestration overhead compounds. KV cache grows, inference concurrency spikes, and suddenly GPU memory and bandwidth become a problem.
Why does that happen? Running planner, retrieval, memory, and reasoning agents in parallel puts real pressure on infrastructure. This is where multi-agent orchestration becomes a serious compute workload. One slowdown in throughput and the entire pipeline slows down.
The NVIDIA H200 Tensor Core GPU was built for workloads like this. 141GB HBM3e VRAM and massive memory bandwidth, designed for long-context, memory-intensive AI systems operating at scale.
The best part? H200 GPUs are available on demand at $2.16/hr on Ocean Network: https://dashboard.oncompute.ai/
https://x.com/oceanprotocol/status/2056752487806439457?s=46&t=sfyIS0XeZHZd-w68hBLkvw
With the advent of Ocean Network (@ONcompute), the distance between idea and execution is narrowing.
Being able to provision global GPU resources on demand from a unified dashboard changes the speed of iteration.
More iteration cycles, more robust AI systems.
https://x.com/oceanprotocol/status/2054557216095477832?s=46&t=sfyIS0XeZHZd-w68hBLkvw
What if running a GPU job felt like running a local script?
Here's how it actually works:
1. Pick your compute environment inside the Ocean Network Dashboard, browsing live benchmarked nodes by GPU/CPU, RAM, disk, and duration.
2. Fund the job in $USDC through escrow-secured payments, so you're only charged for the compute you actually use.
3. Take that exact setup into your IDE via Ocean Orchestrator, where your code runs on remote GPUs while logs stream live and results come back directly to you.
Get started with NVIDIA H200s on pay-per-use pricing from as low as $2.5/hr: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2052795671728803885?s=46&t=sfyIS0XeZHZd-w68hBLkvw
5,000+ compute jobs completed on Ocean Network 🥳
Behind every single one is a developer who chose decentralized, on-demand, affordable GPU compute built for AI workloads.
And right now, shiny new nodes just joined the network, which means more capacity and better availability for you.
Great compute should be available to everyone who builds. Now it is: https://dashboard.oncompute.ai/run-job/environments
https://twitter.com/ONcompute/status/2051697931565343217
The AI agents forging the future need compute. Your GPU has it.
GPU owners are already earning from a 2,000 $USDC weekly pool by running benchmarked nodes via the Ocean Network Incentives Program.
How it works: pass the eligibility criteria, enter the pool, and earn weekly, proportional to your hardware's performance.
Your GPU is a serious piece of infrastructure. Put it to work where it matters.
Run a node, get eligible, and start earning: https://x.com/oncompute/status/2049889013285478813?s=46&t=sfyIS0XeZHZd-w68hBLkvw
90% of vibe-coded projects never reach production — and cost thousands of dollars to fix.
The truth is: you don't need vibe coding, you need fundamental skills and Vibe Computing.
With Ocean Network, you get $100 in free credits to run your training jobs on NVIDIA H200s, and when you need more, rates start as low as $3/hr on top-tier GPUs.
The best part? Ocean Orchestrator brings your compute environment straight into your IDE, so your dev environment and production are never out of sync again.
Start building the right way: https://docs.oncompute.ai/ocean-orchestrator/using-ocean-orchestrator-with-ocean-dashboard
https://x.com/ONcompute/status/2049169017995243874
Ocean Network Incentives are live 🥳
GPU owners, this one's for you, and oh, it's good 👀
We're putting 2,000 $USDC into the weekly reward pool for nodes laying the foundation of our peer-to-peer compute network for AI & ML developers globally.
It's time to put your GPU to work
https://x.com/ONcompute/status/2047330947272466642
At this point, renting an H200 on Ocean Network is cheaper than your coffee.
GPUs on demand — GTX 1060s, Tesla T4s, H200s — all under $3/hr on execution-based billing.
See for yourself: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2046590024695918782?s=20
3,000+ compute jobs already completed on Ocean Network 🎉
Data scientists and AI developers are already executing AI workloads on remote NVIDIA GPUs, no infra to manage, no waiting on provisioning.
Everything runs directly on decentralized compute for under $3/hr.
Check the post below👇
https://x.com/oceanprotocol/status/2045180045484797970?s=46&t=sfyIS0XeZHZd-w68hBLkvw
When trentmc0 joins a conference, you’re not just getting an AI take.
You’re hearing from someone who worked on AI back when it meant modelling real-world systems, and later on the actual tech that helps design the chips everything runs on today. And we mean everything, even generating those cat memes you’re so fond of.
So when he connects today’s AI wave to what’s next [BCI/ACC and beyond] it’s less prediction, more continuation.
Lugano should be a good one 🌊
https://x.com/oceanprotocol/status/2044088517639217445?s=46&t=sfyIS0XeZHZd-w68hBLkvw
The world's most powerful chips shouldn't be locked behind enterprise contracts and $10k/month minimums.
Innovation happens when the best tools are available to the best minds, not just the biggest bank accounts. The Ocean Network stack is built exactly for that, and here's what sets it apart:
1) Test on Nvidia H200s before you scale, catching failures cheaply, not at production cost
2) Pay only for what you use, billed per GPU, down to the minute
3) Run your environment anywhere with container-based IDE execution via the Ocean orchestrator
Start running on H200s today: https://dashboard.oncompute.ai/
https://x.com/oncompute/status/2041857173198606826?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Having PRAGMAtic talks about what devs actually need ETHGlobal Pragma Cannes
Zero Infra. Zero SSH. 100% Compute.
Code → node in ONe click
That’s less complexity for more building 😉
Want to get in ON the action? Claim your complimentary credits here: https://dashboard.oncompute.ai/grant/details
https://x.com/ONcompute/status/2039665276669595941