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
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
oncompute.ai
Ocean Network Ambassador Program
Join the Ocean Network Ambassador Program. Create content (tutorials, videos, explainers) to break down complex Web3 and decentralized compute concepts for developers and researchers. Earn rewards based on impact.
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
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
Ocean Network - Decentralized P2P Compute Network
Run pay-per-use compute jobs on Ocean Network with competitive GPU pricing, editor native workflows, escrow-protected payments, and local outputs.
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
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
X (formerly Twitter)
Ocean Network (@ONcompute) on X
Ocean Network: Pure automatiON. Alpha ON.
The OrchestratiON Layer for Decentralized Intelligence. Don't just compute. Orchestrate.
Powered by @oceanprotocol
The OrchestratiON Layer for Decentralized Intelligence. Don't just compute. Orchestrate.
Powered by @oceanprotocol
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
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
X (formerly Twitter)
Ocean Protocol (@oceanprotocol) on X
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.
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.
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
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
X (formerly Twitter)
Ocean Network (@ONcompute) on X
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:…
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:…
🔥1
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
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
Ocean Network - Decentralized P2P Compute Network
Run pay-per-use compute jobs on Ocean Network with competitive GPU pricing, editor native workflows, escrow-protected payments, and local outputs.
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
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
X (formerly Twitter)
Ocean Network (@ONcompute) on X
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.…
Ocean Orchestrator brings IDE-native GPU compute orchestration into VS Code, Cursor, Antigravity, and Windsurf.…
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
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
X (formerly Twitter)
Ocean Network (@ONcompute) on X
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…
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…
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
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
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
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
Ocean Network - Decentralized P2P Compute Network
Run pay-per-use compute jobs on Ocean Network with competitive GPU pricing, editor native workflows, escrow-protected payments, and local outputs.
$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
$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
Ocean Network - Decentralized P2P Compute Network
Run pay-per-use compute jobs on Ocean Network with competitive GPU pricing, editor native workflows, escrow-protected payments, and local outputs.
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
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
X (formerly Twitter)
Ocean Network (@ONcompute) on X
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…
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…
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
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
Ocean Network - Decentralized P2P Compute Network
Run pay-per-use compute jobs on Ocean Network with competitive GPU pricing, editor native workflows, escrow-protected payments, and local outputs.