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A decentralized data exchange protocol
Weeks of quiet work, converging into one system.
Open source models are about to run the way they were always supposed to. Almost ready!
https://x.com/oncompute/status/2094801199778406629
Open Source keeps shipping the weights. Nobody's been shipping the place to run them.
Very, very soon!
https://x.com/oceanprotocol/status/2092998743708594457
Ask Claude to "find compute to fine-tune this model on my dataset using Ocean MCP" and it handles the busywork.
It finds available GPUs on Ocean Network, shows you rates so you can pick, runs the job, and hands you the finished model.
Try it for free: https://docs.oncompute.ai/on-mcp/quickstart
https://x.com/oncompute/status/2092327921427919100?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Security is of utmost importance to us.
That’s why we have robot cat overlords taking care of GPUs 🐈
https://x.com/ONcompute/status/2091893800771707269?s=20
Reserving an H200 is as easy as ordering a coffee, and at $2.16/hr, it's cheaper too.
Connect the Ocean Network MCP server to your agent and provision GPU compute for your AI training workloads with a single prompt.
Get started here: https://docs.oncompute.ai/on-mcp/quickstart
https://x.com/ONcompute/status/2090014411083653218?s=20
Just "spin up a GPU" is not a strategy.
Before the workload starts, configure the exact compute you need on Ocean Network: GPU, CPU, RAM, disk, and runtime.
Then bring that environment straight into your IDE with Ocean Orchestrator.
H200s are live at $2.16/hr: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2088265384331976997?s=46&t=sfyIS0XeZHZd-w68hBLkvw
CEO: "Can we build our own ChatGPT?"
Engineer: "Sure."
Skips the trillion-token pretraining run, reserves an H200 on Ocean Network, fine-tunes Qwen3-8B with LoRA instead.
The smartest engineering decision is usually knowing what not to build
https://x.com/oceanprotocol/status/2087577350007361576?s=20
Before, AI agents could write your training script.
Today, they can find the compute, launch the job, monitor it, and hand you the results.
The interface is changing from dashboards to conversations.
https://x.com/oceanprotocol/status/2085740711354253780
Kimi K2 Distilled 14B isn't asking for a GPU cluster. It's asking you to stop overthinking infrastructure.
An NVIDIA H200 has 141GB of HBM3e, enough for LoRA and QLoRA fine-tuning, and you can rent one on Ocean Network from $2.16/hr.
Train your adapter, export it, shut the GPU down, and move on to the next problem.
That's what pay-per-use compute is supposed to feel like: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2085386162659594452?s=20
The GPU is no longer the product. Compute is.
No engineer wakes up wanting to rent an H200. They wake up wanting embeddings generated, models fine-tuned, datasets processed, and jobs finished.
The GPU is just the means to get there.
That's exactly what on-demand compute on Ocean Network gives you, with NVIDIA H200s starting at $2.16/hr: https://dashboard.oncompute.ai/
https://x.com/ONcompute/status/2084648803362328621
100 complimentary tokens are ready to claim, and it only takes a couple of minutes.
Once they're in your wallet, put them to use: fine-tune a model, deploy an agent, or kick off a job on an H200.
Grab yours: https://dashboard.oncompute.ai/grant/details
https://x.com/oceanprotocol/status/2083193715187966368?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Finding an available H200 at a fair price, without waiting in a queue, is still the hard part
Ocean Network gives you access to idle H200 capacity across providers, so you can launch the GPU that fits your workload and only pay while it runs
From $2.16/hr: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2082481774362538140?s=46&t=sfyIS0XeZHZd-w68hBLkvw
The hardest part of using an H200 isn't the H200.
It's everything that happens before your code ever runs, and Ocean Network skips most of that.
Launch an NVIDIA H200 in minutes for $2.16/hr on the Ocean Network Dashboard: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oncompute/status/2081756500909805965?s=46&t=sfyIS0XeZHZd-w68hBLkvw
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
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
How many tabs does it currently take to run inference on one model? We counted and it's too many.
But fret not, we're building something with exactly one🤓
https://x.com/oncompute/status/2093346364163801200?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Pick a model, run it, get your output. That's the whole idea.
We know it sounds too simple, but that's kind of the point.
Wait till you see how few steps "too simple" actually takes.
https://x.com/oncompute/status/2092630144288792754?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Ask Claude to "find compute to fine-tune this model on my dataset using Ocean MCP" and it handles the busywork.
It finds available GPUs on Ocean Network, shows you rates so you can pick, runs the job, and hands you the finished model.
Try it for free: https://docs.oncompute.ai/on-mcp/quickstart
https://x.com/oncompute/status/2092327921427919100?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Compute and AI Models🧠
Barry's been keeping them in separate rooms for a while, but not anymore, he's just connecting the two now.
Stay Close🔜
https://x.com/oncompute/status/2091796348005736498
Barry looks tired
Something's been running behind the scenes for a while now, and it's almost ready👀
Stay close⌛️
https://x.com/ONcompute/status/2089390296379392010?s=20
Cloud computing made developers stop thinking about servers. The next shift makes them stop thinking about GPUs.
@ONcompute started as decentralized compute. It's becoming the layer where you stop renting hardware and start just running models.
Stay tuned 👀
https://x.com/oceanprotocol/status/2087892678159503585?s=46&t=sfyIS0XeZHZd-w68hBLkvw
Hey anon, before you reserve GPUs, ask yourself:
1️⃣ Did I size my infrastructure correctly? GPU/CPU, RAM, disk space, and time duration.
2️⃣ Am I paying for resources I'll actually use?
3️⃣ Is my workload close to the compute? Keep your code, containers, and datasets near the compute node to minimize startup time and data movement.
A good reservation starts long before you click "Reserve." Ocean Network helps you get it right from the start.
Get started: https://docs.oncompute.ai/ocean-orchestrator/using-ocean-orchestrator-with-ocean-dashboard
https://x.com/ONcompute/status/2086844269629968741
Your GPU wasn't built to admire your desktop wallpaper. It was built to run workloads.
Ocean Network lets you make your compute available on demand while you stay in control of your hardware and earn from it.
Put your GPU to work: https://docs.oncompute.ai/ocean-network-dashboard/ocean-network-incentives-program
https://x.com/oceanprotocol/status/2085402016290132112?s=20
We're teaching AI agents to write code. The next step is teaching them to provision compute.
ON MCP lets agents discover compute on Ocean Network, choose an environment, and launch jobs using natural language instead of clicking through cloud dashboards.
Get started: https://docs.oncompute.ai/on-mcp/quickstart
https://x.com/ONcompute/status/2085011695403946247?s=20
Run containerized AI workloads straight from your IDE.
Pick an NVIDIA H200 at $2.16/hr from the Ocean Network Dashboard, send the environment straight into Ocean Orchestrator, and launch your compute job without leaving your IDE.
Install Ocean Orchestrator: https://open-vsx.org/extension/OceanProtocol/ocean-protocol-vscode-extension
https://x.com/oncompute/status/2084188971702034876
Seeing a "Not enough available CPU" error on Ocean Network?
It doesn't mean anything is misconfigured. It simply means the environment you selected is at capacity right now.
The fix is simple:
1. Try another node or environment
2. Switch GPU types if your workload allows
3. Or wait a bit. Capacity becomes available as other jobs finish.
https://x.com/oncompute/status/2082859513338888197?s=46&t=sfyIS0XeZHZd-w68hBLkvw
“I want to fine-tune Llama 3 8B on my dataset. Find me an H200 node and get it running using Ocean MCP."
One prompt, and Ocean MCP finds you a live node with real specs and pricing, no external search required.
Then it walks you through the rest: dataset, fine-tuning approach, and funding, before anything spends.
Get started here: https://docs.oncompute.ai/on-mcp/quickstart
https://x.com/oncompute/status/2082120644700016682?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
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
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