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

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

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

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

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

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

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

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

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

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

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

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

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

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

Open Source keeps shipping the weights. Nobody's been shipping the place to run them.

Very, very soon!

https://x.com/oceanprotocol/status/2092998743708594457

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

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

MCP is turning AI agents from chatbots into operators.

Instead of stopping at an answer, they can now discover infrastructure, launch compute, pay for it, and retrieve results.

That's exactly what we built with Ocean Network's MCP server.

Connect your Claude to Ocean Network: https://docs.oncompute.ai/on-mcp/quickstart

https://x.com/ONcompute/status/2095170222336446778?s=20