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

The best fleets aren't owned. They're joined

Every GPU on Ocean Network belongs to someone who decided to put it to use, instead of letting it sit idle

Every node gets benchmarked, stress-tested, proven, before it touches your job

That's what makes our fleet efficient

Choose your node here: https://x.com/oceanprotocol/status/2074939163388895546?s=46&t=sfyIS0XeZHZd-w68hBLkvw

We’re bringing @ONcompute to Pragma Lisbon 2026! 🇵🇹

Come find us at the booth and see how easy it is to access on-demand compute for your next AI project.

We’ll tell you all about it over a couple of pastéis

https://x.com/oceanprotocol/status/2075574807316287873?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Full fine-tuning isn't the only way to adapt Llama 70B

LoRA can achieve performance close to full fine-tuning while using a fraction of the GPU memory. QLoRA reduces memory requirements further, making some Llama 70B fine-tuning workloads feasible on a single high-memory GPU

Ocean Network lets you choose the setup that fits your workload and pay only for the compute you actually use: https://dashboard.oncompute.ai/run-job/environments

https://x.com/oncompute/status/2076705715184640141?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Ideas are easy; shipping is what counts

Build on NVIDIA H200s at $2.16/hr, launch your workload, and let the results speak for themselves.

Claim 100 complimentary tokens and run your first job on Ocean Network: https://docs.oncompute.ai/ocean-network-dashboard/claim-your-compy-tokens

https://x.com/oncompute/status/2077076155396714854?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Need one H200 for just 20 minutes? That's what you pay for

Most compute contracts make you pay for the biggest GPU config upfront, whether your job needs it for 10 minutes or 10 hours. Ocean Network lets you configure GPU/CPU type, RAM, Disk space, & time duration to run compute workloads on a pay-per-use basis

Configure your exact job: https://dashboard.oncompute.ai/run-job/environments

https://x.com/oncompute/status/2077431748234023383?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Your AI agent can now run compute jobs on Ocean Network🤖

🔌Connect Claude, Cursor, ChatGPT, GitHub Copilot & more at our hosted MCP endpoint: mcp.oncompute.ai/mcp

It can discover compute providers, write algorithms, launch jobs & manage storage using natural language

https://x.com/oncompute/status/2077670789835293154

The Singapore Court of Appeal has affirmed the Singapore High Court’s decision that confidentiality has been lost in relation to the emergency arbitration proceedings. The Court of Appeal upheld the High Court’s finding that having regard in large part to Fetch’s conduct, Fetch could not have reasonably believed that confidentiality continued to subsist.

Following this decision, the interim orders preserving confidentiality pending the appeal have been discharged.

Therefore, we republish the emergency arbitrator’s award here: https://x.com/oceanprotocol/status/2078017924061638907?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Every World Cup cycle, the tournament gets bigger; this year's 48-team format nearly doubled the total matches from 2022

Compute scales the same way: more data, more parameters, more memory needed to hold it all. That's exactly what H200's 141GB of HBM3e is built for

Run your next job from $2.16/hr: https://dashboard.oncompute.ai/run-job/environments

https://x.com/ONcompute/status/2078126576638763238?s=20

The clock hit 90, and the match didn't care.

Spain kept creating chances after regulation ran out, and it took until the 106th minute, deep into extra time, to find the goal that actually settled it. An AI batch job doesn't know what an hour is either; it stops when the job's actually done, not when the clock hits a round number.

Ocean doesn't round up, and you get billed for the seconds the GPU actually worked, not the hour it happened to fall in. Access on-demand compute: https://dashboard.oncompute.ai/run-job/environments

https://x.com/ONcompute/status/2079227333161152829?s=20

MCP is becoming the interface layer for agentic AI.

Once an agent has an MCP endpoint, it doesn't need provider-specific APIs. It just discovers compute, checks live pricing, runs the job, and retrieves the results.

That's what agent-native infrastructure looks like

https://x.com/oceanprotocol/status/2079602362814001331?s=20

An Uber across town costs more per hour than an NVIDIA H200 on Ocean Network.

$2.16/hr gets you 141GB of HBM3e memory and 4.8TB/s of bandwidth, enough headroom to run 70B class models on a single GPU, with room left over for KV cache that most cards can't spare.

Access here: https://x.com/oncompute/status/2079951350457385295?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Want to join us at Pragma Lisbon? We've got 5 tickets to give away😄

Meet the Ocean team & learn how to run training jobs on Ocean Network on nvidia H200s while paying only what you use

Code expires tomorrow

Claim yours with PRAGMAOCEAN: https://luma.com/pragma-lisbon2026


https://x.com/oncompute/status/2080294109059678719?s=46&t=sfyIS0XeZHZd-w68hBLkvw

Distilling GLM 5.2 is basically a five-step workflow now

You don't need a research lab or weeks of infrastructure setup anymore.

1) Grab the open, MIT-licensed GLM 5.2 weights: https://docs.z.ai/guides/llm/glm-5.2
2) Write your distillation script and package it as a Docker image
3) Open Ocean Network Dashboard and choose an H200: https://dashboard.oncompute.ai/run-job/environments
4) Attach your dataset, hit run from VS Code, Cursor, or Windsurf with Ocean Orchestrator and watch the logs stream live: https://open-vsx.org/extension/OceanProtocol/ocean-protocol-vscode-extension
5) Download the distilled model straight back into your project folder

The interesting part is how little infrastructure you have to think about to get it done

https://x.com/oceanprotocol/status/2080558350253851036?s=20

What if your AI agent could find a GPU, compare prices, and launch the job itself, without you opening a dashboard or writing a custom integration?

That's now possible with Ocean MCP⬇️

https://x.com/oceanprotocol/status/2080649198761079119?s=46&t=sfyIS0XeZHZd-w68hBLkvw

The team at @ONcompute asking the question many have been avoiding:

Why own an H200 when you only need it for 37 minutes?

Rent the compute. Ship the model

https://x.com/oceanprotocol/status/2080962042312249407?s=46&t=sfyIS0XeZHZd-w68hBLkvw