OpenLedger Depth Research Report: Building a Data-Driven, Model-Combinable Agent Economy on the Foundation of OP Stack + EigenDA

1. Introduction | The Model Layer Leap of Crypto AI

Data, models, and computing power are the three core elements of AI infrastructure, analogous to fuel (data), engine (model), and energy (computing power), all of which are indispensable. Similar to the evolutionary path of infrastructure in the traditional AI industry, the Crypto AI field has also undergone similar stages. At the beginning of 2024, the market was once dominated by decentralized GPU projects, which generally emphasized a rough growth logic of "competing in computing power." However, as we enter 2025, the industry's focus gradually shifts to the model and data layers, marking the transition of Crypto AI from competition for underlying resources to a more sustainable and application-value-driven mid-level construction.

General Large Model (LLM) vs Specialized Model (SLM)

Traditional large language models (LLMs) rely heavily on large-scale datasets and complex distributed architectures, with parameter sizes often ranging from 70B to 500B, and the cost of training once can be as high as several million dollars. In contrast, SLM (Specialized Language Model) is a lightweight fine-tuning paradigm for reusable foundational models, typically based on open-source models, combined with a small amount of high-quality specialized data and technologies like LoRA, to quickly build expert models with specific domain knowledge, significantly reducing training costs and technical barriers.

It is worth noting that SLM will not be integrated into LLM weights, but will cooperate with LLM through methods such as Agent architecture calls, dynamic routing via the plugin system, hot-swappable LoRA modules, and RAG (Retrieval-Augmented Generation). This architecture retains the broad coverage capabilities of LLM while enhancing specialized performance through fine-tuning modules, forming a highly flexible composite intelligent system.

The value and boundaries of Crypto AI at the model layer

Crypto AI projects are essentially difficult to directly enhance the core capabilities of large language models (LLMs), the core reason being

• High technical barriers: The data scale, computing resources, and engineering capabilities required to train Foundation Models are extremely large, and currently only technology giants such as the United States and China possess the corresponding capabilities.
• Limitations of Open Source Ecosystem: Although mainstream foundational models like LLaMA and Mixtral are open source, the key to advancing model breakthroughs still lies mainly with research institutions and closed-source engineering systems, leaving limited participation space for on-chain projects at the core model level.

However, on top of the open-source foundational models, the Crypto AI project can still achieve value extension by fine-tuning specialized language models (SLM) and integrating the verifiability and incentive mechanisms of Web3. As the "peripheral interface layer" of the AI industry chain, it is reflected in two core directions:

• Trustworthy Verification Layer: Enhances the traceability and tamper-resistance of AI outputs by recording model generation paths, data contributions, and usage on-chain.
• Incentive Mechanism: Utilizing the native Token to incentivize behaviors such as data uploading, model calling, and agent execution, creating a positive cycle of model training and services.

AI Model Type Classification and Blockchain Applicability Analysis

It can be seen that the feasible landing points of model-type Crypto AI projects mainly focus on the lightweight fine-tuning of small SLMs, on-chain data access and verification of RAG architecture, and local deployment and incentives for Edge models. Combining the verifiability of blockchain with the token mechanism, Crypto can provide unique value for these middle-to-low resource model scenarios, forming differentiated value for the AI "interface layer."

The blockchain AI chain based on data and models can provide clear and immutable on-chain records of the contribution sources for each piece of data and model, significantly enhancing the credibility of data and traceability of model training. At the same time, through the smart contract mechanism, rewards distribution is automatically triggered when data or models are called, transforming AI behavior into measurable and tradable tokenized value, thus building a sustainable incentive system. In addition, community users can evaluate model performance, participate in rule formulation and iteration through token voting, improving the decentralized governance structure.

II. Project Overview | OpenLedger's AI Chain Vision

OpenLedger is one of the few blockchain AI projects in the current market that focuses on data and model incentive mechanisms. It was the first to propose the concept of "Payable AI", aiming to build a fair, transparent, and composable AI operating environment that incentivizes data contributors, model developers, and AI application builders to collaborate on the same platform and earn on-chain rewards based on actual contributions.

OpenLedger provides a complete closed-loop chain from "data provision" to "model deployment" to "profit-sharing calls", with core modules including:

• Model Factory: No programming required, you can use LoRA for fine-tuning training and deploying custom models based on open-source LLM;
• OpenLoRA: Supports coexistence of thousands of models, dynamically loads on demand, significantly reduces deployment costs;
• PoA (Proof of Attribution): Achieving contribution measurement and reward distribution through on-chain call records;
• Datanets: Structured data networks aimed at vertical scenarios, built and verified by community collaboration;
• Model Proposal Platform: A composable, callable, and payable on-chain model marketplace.

Through the above modules, OpenLedger has built a data-driven, model-composable "agent economy infrastructure" to promote the on-chainization of the AI value chain.

In the adoption of blockchain technology, OpenLedger uses OP Stack + EigenDA as the foundation to build a high-performance, low-cost, and verifiable data and contract execution environment for AI models.

• Built on OP Stack: Based on Optimism technology stack, supporting high throughput and low-cost execution;
• Settle on the Ethereum mainnet: Ensure transaction security and asset integrity;
• EVM Compatible: Allows developers to quickly deploy and scale based on Solidity;
• EigenDA provides data availability support: significantly reduces storage costs and ensures data verifiability.

Compared to general-purpose AI chains like NEAR, which focus more on underlying layers and data sovereignty with the "AI Agents on BOS" architecture, OpenLedger is more focused on building AI-specific chains aimed at data and model incentives. It is committed to enabling the development and invocation of models to achieve a traceable, composable, and sustainable value loop on-chain. It serves as the model incentive infrastructure in the Web3 world, combining model hosting, usage billing, and on-chain composable interfaces to promote the realization of "models as assets."

3. Core Components and Technical Architecture of OpenLedger

3.1 Model Factory, code-free model factory

ModelFactory is a large language model (LLM) fine-tuning platform under the OpenLedger ecosystem. Unlike traditional fine-tuning frameworks, ModelFactory offers a purely graphical interface operation, eliminating the need for command-line tools or API integration. Users can fine-tune models based on datasets that have been authorized and reviewed on OpenLedger. It realizes an integrated workflow for data authorization, model training, and deployment, and its core process includes:

• Data Access Control: Users submit data requests, providers review and approve, and data is automatically integrated into the model training interface.
• Model selection and configuration: Supports mainstream LLMs (such as LLaMA, Mistral), with hyperparameter configuration via GUI.
• Lightweight fine-tuning: Built-in LoRA / QLoRA engine, real-time display of training progress.
• Model Evaluation and Deployment: Built-in evaluation tools support exporting for deployment or ecological sharing calls.
• Interactive Verification Interface: Provides a chat-style interface for easy direct testing of the model's question-and-answer capabilities.
• RAG Generation Traceability: Answers come with source citations, enhancing trust and auditability.

The Model Factory system architecture includes six major modules, encompassing identity authentication, data permissions, model fine-tuning, evaluation deployment, and RAG traceability, to create a secure, controllable, real-time interactive, and sustainable monetization integrated model service platform.

The following is a brief table of the large language model capabilities currently supported by ModelFactory:

• LLaMA Series: The most extensive ecosystem, active community, and strong general performance, it is one of the most mainstream open-source foundational models currently.
• Mistral: Highly efficient architecture with excellent inference performance, suitable for flexible deployment in resource-limited scenarios.
• Qwen: Excellent performance in Chinese tasks, strong comprehensive abilities, suitable as the first choice for domestic developers.
• ChatGLM: The Chinese dialogue effect is prominent, suitable for vertical customer service and localization scenarios.
• Deepseek: Excels in code generation and mathematical reasoning, suitable for smart development assistance tools.
• Gemma: A lightweight model launched by Google, with a clear structure that is easy to quickly get started with and experiment.
• Falcon: Once a performance benchmark, suitable for basic research or comparative testing, but community activity has decreased.
• BLOOM: Strong support for multiple languages, but weak inference performance, suitable for language coverage research.
• GPT-2: A classic early model, suitable only for educational and validation purposes, not recommended for actual deployment.

Although OpenLedger's model combination does not include the latest high-performance MoE models or multimodal models, its strategy is not outdated; rather, it has made a "practical first" configuration based on the real constraints of on-chain deployment (inference costs, RAG adaptation, LoRA compatibility, EVM environment).

Model Factory, as a no-code toolchain, has all models built-in with a contribution proof mechanism to ensure the rights of data contributors and model developers. It features low barriers to entry, monetization, and composability advantages compared to traditional model development tools:

• For developers: Provide a complete path for model incubation, distribution, and revenue;
• For the platform: to form a model of asset circulation and combination ecology;
• For users: Models or Agents can be combined and used like calling an API.

3.2 OpenLoRA, on-chain assetization of fine-tuned models

LoRA (Low-Rank Adaptation) is an efficient parameter tuning method that learns new tasks by inserting "low-rank matrices" into pre-trained large models without modifying the original model parameters, significantly reducing training costs and storage requirements. Traditional large language models (such as LLaMA, GPT-3) typically have billions or even hundreds of billions of parameters. To use them for specific tasks (such as legal Q&A, medical consultation), fine-tuning is required. The core strategy of LoRA is: "freeze the parameters of the original large model and only train the newly inserted parameter matrices." Its parameter efficiency, fast training, and flexible deployment make it the mainstream fine-tuning method most suitable for deploying and combining Web3 models.

OpenLoRA is a lightweight inference framework built by OpenLedger, specifically designed for multi-model deployment and resource sharing. Its core goal is to address common issues in current AI model deployment, such as high costs, low reuse, and GPU resource wastage, promoting the implementation of "Payable AI".

OpenLoRA system architecture core components, based on modular design, covering key processes such as model storage, inference execution, and request routing, achieving efficient and low-cost multi-model deployment and invocation capabilities:

• LoRA Adapter Storage Module (LoRA Adapters Storage): The fine-tuned LoRA adapter is hosted on OpenLedger, enabling on-demand loading to avoid pre-loading all models into GPU memory, thus saving resources.
• Model Hosting & Adapter Merging Layer (: All fine-tuned models share the base model, and during inference, the LoRA adapter is dynamic.