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Kanal postlari
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 | Bring the idea. We'll handle the universe it runs on.
Browse on-demand GPUs from the Ocean Network Dashboard. Select the hardware your workload needs. Pull that environment directly into your IDE with Ocean Orchestrator.
Run inference, fine-tuning, embeddings, or agent workloads without provisioning infrastructure or managing servers. Containerized compute jobs execute on a remote chosen node and return results directly to your workflow.
The dashboard finds the compute. Ocean Orchestrator puts it to work.
So, what will you build?
https://x.com/ONcompute/status/2061846805185245335?s=20 | 68 |
| 3 | $2.16 won’t buy you a latte.
$2.16 won’t get you a parking spot downtown.
But $2.16 will get you an NVIDIA H200 on Ocean Network for 1 hour.
In 10 hours, you could fine-tune an open model, run large-scale inference workloads, process massive embedding pipelines, or train experiments back to back without dealing with infrastructure overhead.
With 141GB of HBM3e memory and 4.8TB/s bandwidth, the H200 handles workloads that are still inaccessible to most developers, and 10 hours would cost roughly $21.60.
Most people still assume this class of compute is reserved for hyperscalers and well-funded AI labs.
Access it here: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2060400783816892724?s=20 | 119 |
| 4 | NVIDIA H200s are becoming one of the best GPUs for multi-agent AI workloads.
Agent systems create massive KV cache pressure, parallel reasoning demand, and long-context memory strain across multiple active inference streams.
That's exactly where H200s shine, with 141GB HBM3e memory and massive memory bandwidth built for high-concurrency AI workloads.
Access them through the Ocean Network Dashboard from just $2.16/hr on a pay-per-use basis: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2060013979351564567?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 230 |
| 5 | Most AI teams obsess over model quality while ignoring the biggest cost in the stack: compute utilization.
An NVIDIA H200 is $2.16 per hour on Ocean Network. The same compute can cost several times more on traditional cloud infrastructure, depending on the provider and availability.
One unit of H200 compute is enough to:
Fine-tune a model with QLoRA
Generate synthetic training data
Run large-scale inference
Plus, Ocean Network charges you only for what you use, with no vendor lock-in, and payments are made through escrow. Explore available environments: dashboard.oncompute.ai/run-job/enviro
https://x.com/ONcompute/status/2058910465099497595?s=20 | 119 |
| 6 | Many compute providers bill by the hour. Your workloads don't run by the hour.
With Ocean Network, you pay for execution time and not a cent more, even on the NVIDIA H200.
Access some of the most affordable GPUs on the market, configured exactly to what your experiment needs: GPU, CPU, RAM, disk space, and runtime.
Run a 22-minute fine-tune and pay for 22 minutes.
That's it: dashboard.oncompute.ai
https://x.com/oncompute/status/2057474746799792588?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 129 |
| 7 | The majority of LoRA, QLoRA, and fine-tuning practitioners are wasting time switching between their IDE and cloud consoles just to provision a GPU.
Ocean Orchestrator brings IDE-native GPU compute orchestration into VS Code, Cursor, Antigravity, and Windsurf. Select your exact specs on the Ocean Network Dashboard, including NVIDIA H200 on-demand access at a fraction of hyperscaler rates, and bring that instance into your IDE on a pay-per-use basis.
Your logs, outputs, and results land directly in your workspace. You never leave the IDE.
Install the Ocean Orchestrator extension and start your next fine-tune in minutes: https://open-vsx.org/extension/OceanProtocol/ocean-protocol-vscode-extension
https://x.com/ONcompute/status/2057070583641248127?s=20 | 117 |
| 8 | If you're building multi-agent systems, you've probably already noticed how fast orchestration overhead compounds. KV cache grows, inference concurrency spikes, and suddenly GPU memory and bandwidth become a problem.
Why does that happen? Running planner, retrieval, memory, and reasoning agents in parallel puts real pressure on infrastructure. This is where multi-agent orchestration becomes a serious compute workload. One slowdown in throughput and the entire pipeline slows down.
The NVIDIA H200 Tensor Core GPU was built for workloads like this. 141GB HBM3e VRAM and massive memory bandwidth, designed for long-context, memory-intensive AI systems operating at scale.
The best part? H200 GPUs are available on demand at $2.16/hr on Ocean Network: https://dashboard.oncompute.ai/
https://x.com/oceanprotocol/status/2056752487806439457?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 105 |
| 9 | GPU queues are a relic of a world that didn't know better. We fixed it.
Fine-tune 70B models, run inference, and process large-scale embeddings on demand on @nvidia H200s through Ocean Network at ~$2.16/hr.
The compute you need, the moment you need it: dashboard.oncompute.ai
https://x.com/oncompute/status/2055306710647296279?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 129 |
| 10 | With the advent of Ocean Network (@ONcompute), the distance between idea and execution is narrowing.
Being able to provision global GPU resources on demand from a unified dashboard changes the speed of iteration.
More iteration cycles, more robust AI systems.
https://x.com/oceanprotocol/status/2054557216095477832?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 130 |
| 11 | An NVIDIA H200 has 141GB of HBM3e memory and rents for roughly $2.16/hr on Ocean Network ( ONcompute ), with jobs running up to 12 hours.
In that time, a small team can run a serious LoRA/QLoRA fine-tune of Llama 3.1 70B on proprietary data, experiment with different training configs, and end the day with a domain-adapted model tuned for their exact workflow.
On narrowly defined internal tasks, these models can outperform frontier general-purpose LLMs.
Many startups spend thousands per month locking themselves into reserved GPU cloud contracts just to access this kind of capability.
On the Ocean Network Dashboard, you spin up an H200 node, run the job, and pay only for the compute you use: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2053870245786308893 | 148 |
| 12 | What if running a GPU job felt like running a local script?
Here's how it actually works:
1. Pick your compute environment inside the Ocean Network Dashboard, browsing live benchmarked nodes by GPU/CPU, RAM, disk, and duration.
2. Fund the job in $USDC through escrow-secured payments, so you're only charged for the compute you actually use.
3. Take that exact setup into your IDE via Ocean Orchestrator, where your code runs on remote GPUs while logs stream live and results come back directly to you.
Get started with NVIDIA H200s on pay-per-use pricing from as low as $2.5/hr: https://dashboard.oncompute.ai/run-job/environments
https://x.com/oceanprotocol/status/2052795671728803885?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 133 |
| 13 | We're opening a few spots for Ocean Network ambassadors. Step in and help shape how the world accesses compute.
1. What you'll do: Take something complex and make it click. Break down how Ocean Network turns compute into a commodity and peer-to-peer verifiable computation into a global market. Use your format: videos, tutorials, infographics, X carousels. Show up where real conversations are happening.
2. How it works: You apply, we review, you get into a private Discord, pick your first task, and you're in. From there, it runs in monthly cycles: you create, submit, and keep building.
3. Rewards: Your work earns points based on quality, reach, and engagement. The better your content performs, the bigger your share, with bonuses for top contributors.
All the details here: https://oncompute.ai/blog/ocean-network-ambassador-program
https://x.com/ONcompute/status/2052355511165628592?s=20 | 137 |
| 14 | 5,000+ compute jobs completed on Ocean Network 🥳
Behind every single one is a developer who chose decentralized, on-demand, affordable GPU compute built for AI workloads.
And right now, shiny new nodes just joined the network, which means more capacity and better availability for you.
Great compute should be available to everyone who builds. Now it is: https://dashboard.oncompute.ai/run-job/environments
https://twitter.com/ONcompute/status/2051697931565343217 | 293 |
| 15 | A long time ago in a data center far, far away… someone was paying for idle GPUs.
Not anymore.
May the 4th be with you. 🌊
https://x.com/oceanprotocol/status/2051293635551834256?s=20 | 137 |
| 16 | The AI agents forging the future need compute. Your GPU has it.
GPU owners are already earning from a 2,000 $USDC weekly pool by running benchmarked nodes via the Ocean Network Incentives Program.
How it works: pass the eligibility criteria, enter the pool, and earn weekly, proportional to your hardware's performance.
Your GPU is a serious piece of infrastructure. Put it to work where it matters.
Run a node, get eligible, and start earning: https://x.com/oncompute/status/2049889013285478813?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 276 |
| 17 | Ocean Network (@ONcompute) is a decentralized compute network where everyone wins.
Users define the resources, pay for what they consume, and the moment the job is done, the billing stops, all secured through escrow-backed payments.
Developers can launch containerized jobs directly from their IDE: every execution isolated, every run reproducible, with signed attestations and on-chain provenance built in.
GPU owners can monetize their hardware by joining the network and earning from real compute jobs routed their way.
And the cherry on top?
Strictly benchmarked NVIDIA GPUs for under $3/hr:
https://x.com/oceanprotocol/status/2049534426242072793?s=46&t=sfyIS0XeZHZd-w68hBLkvw | 292 |
| 18 | 90% of vibe-coded projects never reach production — and cost thousands of dollars to fix.
The truth is: you don't need vibe coding, you need fundamental skills and Vibe Computing.
With Ocean Network, you get $100 in free credits to run your training jobs on NVIDIA H200s, and when you need more, rates start as low as $3/hr on top-tier GPUs.
The best part? Ocean Orchestrator brings your compute environment straight into your IDE, so your dev environment and production are never out of sync again.
Start building the right way: https://docs.oncompute.ai/ocean-orchestrator/using-ocean-orchestrator-with-ocean-dashboard
https://x.com/ONcompute/status/2049169017995243874 | 249 |
| 19 | Fresh compute nodes are live on Ocean Network 🥞
Last week, we launched the Incentives Program, and GPU owners have been putting their compute to work ever since.
Every node has been strictly benchmarked to handle heavy workloads, full scores and history are right there on the dashboard.
Pick your node, filter by GPU, CPU, RAM, disk, and duration. Submit your job and pay only for what you use, locked in through escrow-secured payments.
Stop sitting on your project.
Verified compute is ready when you are: https://dashboard.oncompute.ai/run-job/environments
https://x.com/ONcompute/status/2048771652071133204 | 0 |
| 20 |  Ocean Network Incentives are live 🥳
GPU owners, this one's for you, and oh, it's good 👀
We're putting 2,000 $USDC into the weekly reward pool for nodes laying the foundation of our peer-to-peer compute network for AI & ML developers globally.
It's time to put your GPU to work
https://x.com/ONcompute/status/2047330947272466642 | 0 |
