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

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

“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

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

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

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

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

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

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

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