Interpretation of the Web3 Native Large Language Model ASI-1 Mini

Discover a medical AI tool QBio, which focuses on breast density classification and transparent report generation. Upload X-rays, and in a few minutes, it can tell you whether the breast density is A, B, C, or D, along with a detailed report explaining the decision-making process.

It was developed in collaboration between Fetch and Hybrid, QBio is just an appetizer, the real star is ASI-1 Mini.

Fetch is a very old project. During the years when DeFi occupied the entire market's attention, Fetch focused on AI + Crypto, consistently concentrating on the research and application of universal technology for multi-model agents.

What is ASI-1 Mini

In February this year, Fetch launched the world's first Web3 native large language model (LLM) - ASI-1 Mini. What does Web3 native mean? Simply put, it means it is seamlessly integrated with blockchain, allowing you to not only use AI but also invest in, train, and own AI through the $FET token and the ASI wallet.

So what exactly is the ASI-1 Mini?

It is a large language model specifically designed for Agentic AI, capable of coordinating multiple AI agents and handling complex multi-step tasks.

For example, the ASI inference agent behind QBio is part of ASI-1 Mini. It not only classifies breast density but also explains the decision-making process, addressing the AI 'black box problem.' What’s even more impressive is that ASI-1 Mini can run with just two GPUs, which is very cost-effective compared to other LLMs (such as DeepSeek which requires 16 H100 GPUs), making it suitable for small and medium-sized institutions.

How ASI-1 Mini Innovates

The ASI-1 Mini performs comparably to leading LLMs, but with significantly reduced hardware costs. It features a dynamic reasoning mode and advanced adaptive capabilities, enabling more efficient and context-aware decision-making.

MoM and MoA

They are all abbreviations, don't be afraid, it's very simple: Mixture of Models (MoM), Mixture of Agents (MoA)

Imagine a team of AI experts, each focusing on different tasks, working seamlessly together. This not only improves efficiency but also makes the decision-making process more transparent. For example, in medical image analysis, MoM might choose one model specialized in image recognition and another specialized in text generation, while MoA is responsible for coordinating the outputs of these two models to ensure that the final report is both accurate and easy to read.

Transparency and Scalability

Traditional LLMs are often "black boxes"; you ask them a question, they give you an answer, but why they answered that way, sorry, no comment. ASI-1 Mini is different. Through continuous multi-step reasoning, it can tell you that it chose this answer for these reasons, which is particularly crucial in the medical field.

The context window of ASI-1 Mini will be expanded to 10 million tokens, supporting multimodal capabilities (such as image and video processing). In the future, the Cortex series models will be launched, focusing on cutting-edge fields such as robotics and biotechnology.

hardware efficiency

Other LLMs require high hardware costs, while ASI-1 Mini can run with just two GPUs. This means that even a small clinic can afford it, without the need for a million-dollar data center.

Why is it so efficient? Because the design philosophy of ASI-1 Mini is "less is more." It maximizes the use of limited computing resources by optimizing algorithms and model structures. In contrast, other LLMs often pursue larger-scale models, resulting in enormous resource consumption.

Community Driven

Unlike other large language models, ASI-1 Mini is trained in a decentralized and community-driven manner. ASI-1 Mini is a tiered freemium product aimed at $FET holders, who can connect their Web3 wallets to unlock all features. The more FET tokens held in the wallet, the more features of the model can be explored.

This community-driven model is similar to crowdfunding, except it is used for training and validating artificial intelligence, high-tech, and no longer belongs only to the elite, but can be participated in by everyone.

In today's relatively mature landscape of LLM, why do we still need a separate ASI-1 Mini? It is easy to understand that it fills the gap between Web3 and AI integration.

Current LLMs (such as ChatGPT, Grok) mainly serve centralized environments, while ASI-1 Mini is the first LLM designed for decentralized ecosystems. It not only makes AI more transparent and efficient but also allows community members to directly benefit from the growth of AI.

The emergence of ASI-1 Mini marks the transition of AI from "black box" to "transparent", from "centralized" to "decentralized", and from "tool" to "asset". It can not only play a role in the medical field (such as QBio), but also show potential in various fields such as finance, law, and scientific research.

This month, Fetch has partnered with Rivalz to integrate the ASI-1 Mini into Rivalz's Agentic Data Coordination System (ADCS), enabling on-chain AI inference. With this collaboration, decentralized applications can directly access advanced AI reasoning capabilities on the blockchain.

Traditional blockchain environments are resource-constrained, and smart contracts can only handle lightweight tasks, usually obtaining simple data (such as prices) through oracles, and cannot directly run complex AI models. ADCS perfectly addresses this issue, with complex computations of AI inference completed off-chain, and the results securely returned to the blockchain, ensuring decentralization and trust.