Many people think that deploying AI models on the blockchain is a high-threshold technical task that only experienced engineers can handle. But on OpenLedger, with the help of ModelFactory and OpenLoRA, ordinary developers and even non-technical users can achieve a complete loop of 'model registration → on-chain deployment → inference call → revenue settlement'.
In today's article, we will systematically break down these two key components and see how they build the 'model infrastructure layer' of @OpenLedger .
1️⃣ What is ModelFactory? It is not a development platform, but a model publishing engine.
ModelFactory is the model building and registration interface provided by OpenLedger; you can think of it as the 'Hugging Face + model application market of the Web3 world.'
Its function is not to help you train the model itself (you still need data and algorithms for training), but to provide you with:
The registration link for the model.
Integration channel with the data subnet (Datanet).
The scheduling entry for inference resources.
Revenue tracking and call statistics tools.
Once you register a model with ModelFactory, it will become a 'callable smart agent' on the OpenLedger network and have the ability to generate revenue.
2️⃣ Complete breakdown of the model deployment process: it only takes a few steps from upload to launch.
Assuming you have already trained a model using a certain data subnet, the subsequent on-chain deployment process is roughly as follows:
Register model metadata: including model name, purpose, version, compatibility with Datanet, etc.
Upload model structure files and weight parameters: currently supports mainstream deep learning framework formats (such as PyTorch, ONNX).
Bind resource pool: choose which OpenLoRA node group will host the inference resources.
Set calling parameters and pricing: specify the API interface format of the model and calling costs (priced in OPEN).
Review and publish: complete registration on-chain, allowing other users to use your model for inference.
The entire process is like launching an 'AI Dapp' on-chain, and the revenue will accumulate according to the number of calls, automatically entering your bound wallet address.
3️⃣ What is the role of OpenLoRA? The 'lightweight operation engine' for model inference.
Deploying a model is just the first step; to really make the model 'run,' an efficient mechanism to respond to inference requests is needed, which is precisely the mission of OpenLoRA.
Its core functions include:
Low-latency inference scheduling: rapid response under multi-model coexistence.
Resource compression optimization: supports parameter sharing and quantization processing to alleviate on-chain inference pressure.
Multi-tenant resource management: resource isolation between different models ensures fair calling.
Inference paths are traceable: every inference can trace the data source and model calls, in conjunction with attribution profit sharing.
OpenLoRA is essentially a set of decentralized computing nodes that share the on-chain inference load while ensuring the results are reliable and measurable.
4️⃣ How is the model's revenue calculated? The profit-sharing mechanism is not 'entirely to the author.'
The model revenue structure of OpenLedger is a profit-sharing system under multi-role collaboration, as follows:
Model deployer: receives the majority of the base fee for calls.
Upstream data contributors: identified through the attribution engine, sharing profits according to contribution.
Inference service nodes: bear the computing costs and receive base subsidies based on call frequency.
OpenLedger protocol pool: charges a very small percentage as a fee for ecological governance and public expenses.
You are not the only beneficiary, but you are one of the core nodes in this 'model value network.' The more the model is called, the more all contributors along the chain can earn revenue, which is precisely what makes OpenLedger so appealing.
5️⃣ Non-developers can also participate: reuse open-source models and still share profits.
If you are not a model developer and do not want to train models from scratch, you can still participate in the ModelFactory operational process:
Find some excellent open-source models (e.g., large language models or image recognition models).
Upload to OpenLedger for registration and deployment according to the licensing agreement.
Set calling entry and pricing strategy, optimizing inference parameter configuration.
Promote the models you have launched, encouraging others to call them.
Collect revenue from actual usage.
This is like the role of an 'AI model mover + application publisher,' with a lower operational threshold but still possessing commercial potential.
6️⃣ Risk reminders and suggestions: how to avoid deploying 'unnoticed' models.
Deploying a model may be simple, but it’s not 'just get on-chain to make money.' You need to pay attention to:
Is the model really useful? Is the Datanet data sufficient?
Is the call frequency substantial? It is recommended to deploy popular general models or focus on specific verticals.
Is the pricing strategy reasonable? Too high may lead to no usage, too low may not cover costs.
Are the inference nodes stable? OpenLoRA supports custom scheduling schemes and recommends binding quality node pools.
It is recommended to prioritize deploying lightweight models with practical application scenarios (such as sentiment analysis, transaction recognition, text classification, etc.) and gradually expand the pool of users.
Summary.
Deploying AI models on OpenLedger is no longer exclusive to experts, but a new track that all creators and operators can participate in.
Through ModelFactory, you can achieve model registration and launch; through OpenLoRA, you can ensure stable and low-cost operation of model inference; through the attribution mechanism and profit-sharing system, you can turn each model call into real OPEN revenue.
Next, if you are a developer, you might want to try publishing your own model. If you are a content operator, you can also 'proxy on-chain' some popular models. The deployment of AI models in Web3 is no longer an experiment but practical operation.
