Embracing the AI Era and Accelerating Intelligent Innovation
Through open-source collaboration and innovative technology, promote the democratization and accessibility of large language models (LLMs) and AI applications.
With low-cost, low-power hardware based on the PICUBE architecture, our goal is to enable individuals to train and infer AI models locally without relying on cloud services, thereby promoting widespread AI adoption.
A decentralized network allows users to own their data sovereignty, protecting privacy while fostering a fair computational ecosystem.
Using blockchain technology, we provide on-chain management and NFT support for computational resources and AI models. Leveraging the ECHO protocol, developers and enterprises can securely trade and share models, building a transparent and open AI value network.
Large Language Module Blaster.
An extension of the "large language model" concept, emphasizing the "module" at its core. By breaking large models into reusable and composable functional modules, developers can quickly integrate or replace components, enabling flexible optimization and feature expansion.
Symbolizes enhanced and accelerated capabilities for these modules. Our community is committed to module-level optimization and computational acceleration technology to achieve explosive performance improvements across various hardware and application scenarios.
Close partnerships with new processor manufacturers to provide efficient software and hardware solutions for large model training and inference.
Development of toolchains, algorithm libraries, and model optimization solutions for different architectures to improve efficiency and performance.
Create a decentralized and open-source ecosystem to allow more people to benefit from AI-driven innovation opportunities.
Optimize advanced hardware architectures to provide low-power, high-performance AI computing support and fully leverage hardware potential.
Achieve a modular design for rapid iteration and feature expansion, with algorithm-level optimization tailored to various hardware for enhanced efficiency.
Focus on computational optimization to deliver explosive improvements in training and inference performance, ensuring multi-platform compatibility.
Adapt algorithms for mainstream hardware ecosystems and implement low-power, efficient inference for IoT and edge computing scenarios.
Provide hardware optimization and technical support to assist enterprises and developers.
Implement decentralized governance and cryptocurrency incentive plans to promote sustainable ecosystem development.
Support cutting-edge projects and accelerate technology transformation.
Champion green, energy-efficient concepts to build a compliant and healthy AI ecosystem.