Alibaba Cloud

ChainGPT has reached a partnership with Alibaba Cloud to expand the Solidity LLM and AIVM network based on GPU infrastructure

ChainCatcher news, according to Chainwire, blockchain AI solution provider ChainGPT announced a strategic partnership with Alibaba Cloud. The two parties will combine Alibaba Cloud's GPU computing resources with ChainGPT's artificial intelligence technology stack to provide scalable enterprise-level machine learning services for Web3 developers. The core outcome of this collaboration is the comprehensive open-sourcing of ChainGPT's Solidity large language model (LLM), which is specifically designed for smart contract development, auditing, and troubleshooting, now achieving global real-time access through Alibaba Cloud's GPU architecture.After the open-sourcing of the Solidity LLM, developers can use the model for free to accelerate the construction and security verification of decentralized applications. Alibaba Cloud's GPU infrastructure will provide high reliability support for the model, meeting the demands of Web3 AI solutions for computing power, security, and response speed. ChainGPT founder and CEO Ilan Rakhmanov stated that this collaboration aims to lower the threshold for Web3 developers to access high-performance AI technology, providing tool support for the next generation of decentralized applications.Alibaba Cloud stated on its official X platform account that the two parties will enhance decentralized AI infrastructure through the AIVM network, with future plans to utilize ChainGPT's AI virtual machine (AIVM) and decentralized GPU market SDK to integrate Alibaba Cloud resources, providing underlying computing power for AI tools, agents, and applications in the Web3 field. Currently, developers can deploy the Solidity LLM based on Alibaba Cloud's GPU computing power and optimize the model.As an extension of the collaboration, Alibaba Cloud officially joined ChainGPT's AIVM GPU network as a validation partner. Both parties stated that this collaboration not only provides scalability assurance for ChainGPT's AI models but also promotes the construction of a globally inclusive decentralized computing layer, eliminating geographical limitations on on-chain AI development. The next step for both parties is to deepen the integration of the AIVM network and explore ways to enhance the flexibility of AI model deployment and Web3 application development.

The decentralized AI training platform FLock.io has reached a strategic cooperation with Alibaba Cloud, focusing on three major technological directions

ChainCatcher news, the decentralized AI training platform FLock.io officially announces a strategic partnership with Qwen, a leading series of open-source large language models under Alibaba Cloud, marking a deep connection between decentralized AI and blockchain technology within the mainstream AI ecosystem.This collaboration focuses on three major technological breakthroughs:Technological Integration: Combining Alibaba Cloud's centralized infrastructure with FLock.io's decentralized technology to jointly develop domain-specific and general AI models, while promoting the seamless integration of decentralized AI models into centralized platforms.Data Privacy Protection: Exploring the combination of distributed ledger technology and federated learning to address data privacy and sovereignty issues in model training, providing innovative solutions for the secure application of private data.Collaborative Innovation: Through joint research and technological collaboration, creating a more inclusive, scalable, and privacy-preserving AI ecosystem, facilitating the collaborative development of centralized and decentralized AI.As one of the world's leading large language models, Qwen has demonstrated outstanding performance in multiple authoritative benchmark tests and is widely used in natural language processing, content generation, and other fields. Through this collaboration, FLock and Qwen will work together to explore deeper technological integration, maintaining the advantages of high-performance AI models while promoting the practical application of decentralized AI training in a broader range of scenarios, making it more accessible, flexible, and valuable in both centralized and decentralized ecosystems.
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