Discover a medical AI tool called QBio that focuses on breast density classification and transparent report generation. Upload an X-ray and it will tell you within minutes whether your breast density is A, B, C, or D, along with a detailed report explaining the decision-making process.
It was developed by Fetch and Hybrid. QBio is just an appetizer, and the real star is ASI-1 Mini.
Fetch is a very old project. In the years when Defi occupied the attention of the entire market, Fetch focused on AI + Crypto and has always focused on the general technology research and development and application of multi-model agents.
What is ASI-1 Mini
In February this year, Fetch launched the worlds first Web3 native large language model (LLM) - ASI-1 Mini. What is Web3 native? Simply put, it is seamlessly integrated with the blockchain, through $FET tokens and ASI wallets, allowing you to not only use AI, but also invest, train and own AI.
So what exactly is the ASI-1 Mini?
It is a large language model designed specifically for agentic AI that can coordinate multiple AI agents and handle complex multi-step tasks.
For example, the ASI <TRAIN/> inference agent behind QBio is part of the ASI-1 Mini. It can not only classify breast density, but also explain the decision-making process, solving the AI black box problem. Whats more, the ASI-1 Mini only needs two GPUs to run, which is very low cost compared to other LLMs (such as DeepSeek, which requires 16 H100 GPUs), making it suitable for use in small and medium-sized institutions.
How ASI-1 Mini innovates
Comparable in performance to leading LLMs at a significantly lower hardware cost, the ASI-1 Mini features dynamic reasoning modes and advanced adaptive capabilities for more efficient and context-aware decision making.
MoM and MoA
These are all acronyms. Don’t be afraid, they are simple: Mixture of Models (MoM), Mixture of Agents (MoA)
Imagine a team of AI experts, each focusing on different tasks and working together smoothly. This can not only improve efficiency but also make the decision-making process more transparent. For example, in medical image analysis, the MoM may choose a model that specializes in image recognition and another model that specializes in text generation. The 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 LLM is often a black box. You ask it a question and it gives you an answer, but I cant tell you why it gives such an answer. ASI-1 Mini is different. Through continuous multi-step reasoning, it can tell you that I chose this answer because of these reasons. This is crucial, especially 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 of models will be launched, focusing on cutting-edge fields such as robotics and biotechnology.
Hardware efficiency
While other LLMs require high hardware costs, the ASI-1 Mini only requires two GPUs to run. 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 models, resulting in huge resource consumption.
Community Driven
Unlike other large language models, ASI-1 Mini is decentralized and community-driven. ASI-1 Mini is a tiered freemium product for $FET holders, who can connect to a Web3 wallet to unlock full functionality. The more FET tokens you hold in your wallet, the more you can explore the models capabilities.
This community-driven model is like crowdfunding, except that it is used to train and verify artificial intelligence. High-tech is no longer just for the elite, but everyone can participate.
Why do we need to develop an ASI-1 Mini when LLM is relatively mature? It is easy to understand. It fills the gap in the integration of Web3 and AI.
Currently, LLM (such as ChatGPT and Grok) mainly serves centralized environments, while ASI-1 Mini is the first LLM designed for a decentralized ecosystem. 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 transparency, from centralization to decentralization, and from tool to asset. It can not only play a role in the medical field (such as QBio), but also show its potential in many fields such as finance, law, and scientific research.
This month, Fetch partnered with Rivalz to integrate the ASI-1 Mini into Rivalz’s Agentic Data Coordination System (ADCS) to enable on-chain AI reasoning. With this collaboration, decentralized applications can access advanced AI reasoning capabilities directly on the blockchain.
Traditional blockchain environments are resource-constrained, and smart contracts can only handle lightweight tasks. They usually obtain simple data (such as prices) through oracles, and cannot directly run complex AI models. ADCS perfectly solves this problem. The complex calculations of AI reasoning are completed off-chain, and the results are safely returned to the blockchain, ensuring decentralization and trust.
