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/hr for an H200. The asterisk just says "that's the price."

Funny thing about that asterisk. Everywhere else, it hides a minimum, a contract, a waitlist. Here it's just pay-per-use, and then you get on with your day.

Launch your first job: https://dashboard.oncompute.ai/run-job/environments

https://x.com/oncompute/status/2063912271919608173?s=46&t=sfyIS0XeZHZd-w68hBLkvw

👀 Something new is coming to @lunor_ai ...
News shapes how the world understands conflict, alliances, and global power. The next quest will ask you to look closely at how international actors are portrayed in war and conflict coverage: who supports whom, who criticizes whom, and where the stance is unclear.

No coding required. Just careful reading, sharp judgment, and attention to context.

🗓️ Launching: June 9 💰 Reward Pool: 2,500 USDC
Get ready for MediaLens.

https://x.com/oceanprotocol/status/2064247373073645621?s=46&t=sfyIS0XeZHZd-w68hBLkvw

An AI agent without memory isn't autonomous. It's a very expensive goldfish.

Persistent storage on Ocean Network gives agents a memory that lasts. An agent stores what it learns once in a bucket you own and control, and any future job picks up exactly where the last one left off.

It gets stronger when agents work together. Share a bucket through an on-chain access list, and many agents can read and write the same memory. One plans, others execute, and results come back to one place.

That's the missing layer for an agent economy. Agents that remember can specialize. Agents that share memory can coordinate. And the memory is yours, on your terms.

Run it on the most affordable NVIDIA H200 anywhere, $2.16/hr on the Ocean Network Dashboard: https://dashboard.oncompute.ai/run-job/environments

https://x.com/ONcompute/status/2065431928585527431?s=20

What's actually blocking your AI and ML work right now?

https://x.com/oceanprotocol/status/2066459795880640904

Compute used to be personal. Then it became centralized. Today, some of the most powerful hardware in the world sits behind waitlists and platform gatekeepers.

Ocean Network changes that with on-demand compute access and escrow-secured payments.

Train your AI and ML workloads on pay-per-use @nvidia H200 GPUs starting at $2.16/hr: https://dashboard.oncompute.ai/run-job/environments

https://x.com/oncompute/status/2066895321096225031?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Decentralized compute sounds technical, but the idea is simple.

At any given moment, GPU capacity sits idle across research labs and workstations around the world.

Decentralized compute turns that unused capacity into a marketplace anyone can access.

You list your GPU, someone rents it. You need a GPU, someone has one. No single company owns the supply or decides who gets access.

The result is a larger pool of compute, better utilization of existing hardware, and more options for builders.

That's the model Ocean Network is built on.

https://x.com/oncompute/status/2067264755874554303?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Before Airbnb, most travelers relied on hotels. Then Airbnb unlocked rooms that were already sitting empty in people's homes. The supply existed all along; it just wasn't accessible.

GPUs are in a similar position today. Large amounts of hardware sit idle across data centers, labs, and workstations. Decentralized compute turns that unused capacity into accessible supply.

Ocean Network built that marketplace for GPUs: https://dashboard.oncompute.ai/

Attention builders: $100 in complimentary tokens are waiting for you to claim on the Ocean Network Dashboard.

Claim them and deploy to high-quality @nvidia GPUs for your AI workloads.

Get started here: https://docs.oncompute.ai/ocean-network-dashboard/claim-your-compy-tokens

https://x.com/oncompute/status/2067879556111810886?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Stop waiting in line for compute.

The hardware shortage was real. The access shortage was a choice.

NVIDIA H200s are live on the Ocean Network Dashboard at $2.16/hr. You pay for what you run and nothing else

https://x.com/oceanprotocol/status/2069106670664290795?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Forget provisioning.
What if your training job spun up its own isolated container, ran on your selected GPUs, and tore down clean the moment it finished?

Here is the flow:

Open the Ocean Network Dashboard and pick the GPUs that match your specs. Lock the environment you want and take it straight to your IDE.

Write your training job the way you already do, point it at the data, and dispatch. It runs sealed in its own container on the hardware you selected, the data never leaves where it lives, and you get the output back.

The moment it finishes, the container tears down clean.

Learn more about IDE-native GPU deployment: https://docs.oncompute.ai/ocean-orchestrator/using-ocean-orchestrator-with-ocean-dashboard

10k compute jobs later, and the only queue is the one that doesn't exist.

Turns out, when you skip the waitlist, the sales call, & the enterprise pricing, work just gets done.

And if you own GPUs, one of those next 10k jobs could run on your hardware: dashboard.oncompute.ai/run-node/setup

https://x.com/oncompute/status/2069741407208681651?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Memory is the difference between a tool and a mind.

An agent that forgets everything between jobs is starting from zero every time. Persistent storage on Ocean Network changes that. It learns once, writes to a bucket you own, and every job after picks up exactly where the last one stopped.

Now let them share. Open one bucket to many agents through an on-chain access list. One plans. Others execute. The memory is common, the work divides itself.

This is what an agent economy was waiting for. Agents that remember can specialize. Agents that share can coordinate. And all of it stays yours, on your terms.

Try it here: https://docs.oncompute.ai/persistent-storage/quickstart

Claim 100 complimentary credits to access top-tier NVIDIA GPUs. Use them to run batch compute jobs on nvidia
H200s at $2.16/hr, pay-per-use with on-demand access. Learn more: https://docs.oncompute.ai/ocean-network-dashboard/claim-your-compy-tokens

https://x.com/ONcompute/status/2070430740047634880?s=20

We considered building a data center. Then we noticed everyone already had a GPU.

Billions spent on land, power, and cooling, or we just ask the GPUs already sitting online if they're free.

Submit the job on Ocean Network, pay for the compute you use, and get on with your day.

https://x.com/oceanprotocol/status/2071517244341829793

Opened the GPU bill. Closed the GPU bill. Sat in silence for a bit.

The expensive part isn't always the compute. It's the capacity you reserve "just in case" and never fully use.
With Ocean Network, you choose the GPU, RAM, CPU cores, and runtime your job actually needs. Billing is per minute, and if your job finishes early, the meter stops too.

Configure it, run it, and compare the difference: https://dashboard.oncompute.ai/run-job/environments

https://x.com/oncompute/status/2071863306441122174

Claim 100 complimentary credits and start running jobs on top-tier NVIDIA GPUs.

Spin up batch compute on H200s at $2.16/hr, on demand, and pay only for what you use.

Get started: https://docs.oncompute.ai/ocean-network-dashboard/claim-your-compy-tokens

https://x.com/oncompute/status/2072150699941802495

Builders, settle a debate for us: what do you actually rent GPUs for?

Vote below, we're curious where most of the real demand sits

https://x.com/oceanprotocol/status/2072279120768110862

Most embedding workloads are batch jobs.

Text goes in, vectors come out. Nothing about indexing millions of documents needs millisecond latency, yet many teams pay per-token APIs built around it.

Instead, rent an NVIDIA H200 for $2.16/hr, run your embedding workload, & pay only for the compute you use: https://dashboard.oncompute.ai/run-job/environments

https://x.com/ONcompute/status/2072700321621790884?s=20

Same GPU energy, different bill Started the day feeling like a superhero. By evening, felt like a cautionary tale, migrating servers at 2 AM while a rubber duck watched in silence Instead, get NVIDIA H200s on-demand at Ocean Network for just $2.16/hr, on a pay-per-use basis Access here: dashboard.oncompute.ai/run-job/enviro

https://x.com/ONcompute/status/2072998359829242246?s=20

You don't pay for compute on Ocean Network. You pay for verified completion of compute✅

Book an H200 for $2.16/hr and your budget is locked in escrow before the job starts

Once the network verifies completion, only your exact runtime cost is released and the remaining funds are automatically refunded 😎

Try it: https://dashboard.oncompute.ai/run-job/environments