Ocean Network launches Inference. It is the biggest expansion of what you can do on the network, and the capability we have been building toward since day one: infrastructure where every claim we make, the product itself can show you.
We built it this way because we were paying attention. The complaints about providers who swap a model without telling anyone. The overnight job that turned a predictable budget into a surprise bill. The developer sitting on a GPU build list, not sure the card is right for the workload. These are not edge cases in this industry. They are the shape of it, and Inference is built specifically to answer them.
The evidence comes from the community itself. On r/LocalLLaMA, someone sent the same prompt to four platforms all claiming to serve the same model and got four meaningfully different responses back. The thread gathered 1,204 upvotes. The top reply, at 188 points:
"This is why I stopped using routers entirely. If I can't pin the provider I don't want it."
On Ocean Network Inference, you pin it.
What Inference is
Ocean Network has handled compute jobs since day one, workloads that run once, do their work, and exit clean. Inference is different. Inference keeps a workload up: a model or an AI application running on a node's GPU, reachable over HTTP for as long as you have paid for it. A chat model, an embeddings endpoint, a ComfyUI instance generating images, whatever you need answering requests on dedicated hardware, booked directly from the person operating it.
Placement, payment, and lifecycle are handled by the network. You pay a node operator directly for the hardware and the minutes you use.
You know what you are paying before it starts
Per-token pricing is fine for a chat session, yet punishing for an overnight embeddings reindex, a long evaluation run, or anything repetitive enough to do the arithmetic on in advance. When the job is long and predictable, the hour beats the token, and that calculation gets considerably easier when you see the number before the session starts rather than after it ends.
On Ocean Network, the full cost of a session is calculated and shown before you commit. When the session ends, billing stops. Right now, an H200 runs at $2.16 an hour. In early testing, a 30-second video from the image-to-video generation template came in at $0.72.
That pricing is possible because there is very little between you and the hardware. Your browser connects to a node operator's machine directly. Payment is placed in escrow before the container starts, and the node claims the full booked window. Don’t worry, if you need more time, there is also the option to prolong the session. There is no central routing layer deciding which model file actually answers your request. The model you pick is the model that runs, on a card that is yours for the duration of the session.
Four ways in
The Inference tab offers four entry points, and they differ only in how much you decide versus how much is already worked out for you. Every one of them ends in the same place: a running service with an endpoint you own.
Curated Models are the fastest path to a working endpoint. Each package pairs a model with a tested engine preset, the right launch flags, and the exact hardware it needs, so there is nothing left to size or tune. For example, Qwen3-8B for everyday chat and coding, deeper reasoning models for heavier workloads; the catalogue runs from lightweight 16GB GPU setups up to multi-card configurations. Want to change something? One tap carries the package parameters straight into the Custom Models flow, so you are never starting from scratch.
Custom Models put every flag under your control, for the moments when a specific model, quantization, or context length matters more than speed of setup. Pick any model on the Hugging Face Hub, run it on vLLM or llama.cpp, and configure it yourself: context window, tensor parallel size, tool-call parser. The output is an OpenAI-compatible API, so anything calling POST /v1/chat/completions works against your service unchanged.
Services are containerised applications published and maintained by node operators. Where the model flows hand you an API, a Service hands you a UI directly in a browser tab. They ship without models loaded, giving you full control of what runs inside them once the app is up.
Templates take that further, and they are where Inference becomes genuinely accessible to people who have never configured an engine in their life. A Template is a ready-to-run option. Pick an outcome, and everything needed to produce it comes bundled and downloads when your session starts. For example, video generation templates have been fit with text-to-image, image-to-video, and reference-to-video options. You pick the result, not the stack. Every Template lists exactly what it downloads, what you supply, and what comes out the other end, so you know what you are getting before you pay for it. This is where, in early testing by our marketing team, a 30-second video from the image-to-video generation template was produced in approximately 20 minutes at a cost of $0.72.
How a session works
Whichever entry point you take, the flow adapts to your entry point. Custom Models walk the full sequence: pick the model, choose an environment, configure launch flags and any access tokens or storage buckets you need, then pay and launch. Curated Models take you straight from package selection to payment, with the configuration already decided. Templates do the same, stepping into configuration only if the template requires an environment variable or a storage bucket for model files.
Funds go into escrow, the node is authorised to claim against them for the duration you booked, and you land on the service's management page.
Your selection lives in the page URL at every stage, so a refresh, a bookmark, or a shared link resumes the same flow rather than starting over.
What you get when it is running
The management page is accessible from any device, as long as you are signed in with the same account. It gives you live container logs, a countdown on your paid window with the option to extend it, and edit-and-relaunch: swap the model, engine, or configuration in place without losing the service, its endpoint, or the time already paid for.
Models launched through the Curated or Custom flows expose an OpenAI-compatible API. Services and Templates open their web UI directly in a browser tab.
And if you have been sitting on a GPU build list, it is worth running a real workload here for a week before spending four figures on hardware that lives under your desk. Developers on r/LocalLLaMA have been giving each other exactly that advice, and the thread that started it has 587 upvotes to show how widely it landed.
Your first 6 hours are on us
To mark the launch, we are giving away 6 hours of H200 inference in complimentary tokens. Claim yours here and start generating. Product videos, music, image-to-video clips: the templates are loaded and ready to go.
Get started
You will need a wallet with funds on Base, the Ethereum L2 that Ocean Network runs on. Sign in with an external wallet or use the built-in Smart Wallet with email or social login, with a fiat on-ramp included if you need tokens. Beyond that, a rough sense of what your model needs: the catalogue already filters out environments that cannot satisfy your workload before you pick one. A Hugging Face access token only comes into play if you are running something gated.
Open the Inference tab. The GPUs are ready.
Resources: Docs · Dashboard · FAQ
