BP-FLAC Secures $10 Million in Funding to Propel Global A100/H100 Node Construction

BP-FLAC, the first generative Al Infrastructure public chain with infinite computing power aggregation capabilities, has successfully secured a significant $10 million in funding. This funding round will provide BP-FLAC with crucial momentum, enabling it to expedite the realization of key initiatives, including real GPU computing power allocation, the establishment of a global multi-node GPU computing center, the low-code AI training platform, personalized training and services in AIGC, the successful mapping of incentives from the testnet to the mainnet and token airdrop as a reward for early contributors at the mainnet launch.


This round of financing and grants received robust support from prominent investors in the blockchain and high-tech sectors, including Eureka Partners, Westlabs, Mybitdata Ltd., DecentraLabs, tech giants Amazon and NVIDIA, and the influential Alkhabeer Fund from the Middle East family office.
BP-FLAC(blockchain-powered federated learning artificial intelligence computing platform) is the Turing-complete ecological network supporting AI big computing power and intelligent applications. As a groundbreaking public blockchain in the field of artificial intelligence, stands out in the market due to its unique and innovative features:
1. Optimization of zk Algorithm Based on CUDA:
BP-FLAC successfully addresses data privacy issues in AI training by optimizing the zk algorithm based on CUDA. This innovative approach ensures the security and privacy of data during the AI model training process.
2. Ownership and On-chain Integration of A100 Machines:
Distinguishing itself from other projects, BP-FLAC possesses a significant number of high-performance A100 GPU machines and integrates all these A100 devices onto the blockchain. This initiative guarantees the stability of the blockchain network’s computing power, providing users with efficient computational resources.
3. Pioneering Chip RWA Standard:
Introducing the pioneering Chip RWA Standard, BP-FLAC sets a new benchmark, ensuring a standardized and secure framework for token issuance and exchange based on chip.
This funding will be widely utilized to develop the project, highlighting its global vision and commitment to innovation. The funds will facilitate global expansion, focusing on the intelligent allocation of real GPU computing power. Additionally, it will serve as technology, research, and development capital, aimed at introducing a low-code AI training platform and realizing personalized training and services in AIGC, driving innovation in AI technology. The development of the ecosystem and community building is also a crucial aspect of the financing, with the funds dedicated to fostering incentive mechanisms, developer ecosystems, and partnership relationships, ensuring the overall health and growth of the BP-FLAC ecosystem.
In mid-2024, BP-FLAC will achieve the mapping of incentives from the testnet to the mainnet, marking the beginning of a new journey. Plans include rewarding early contributors with a token airdrop upon the mainnet launch. Additionally, BP-FLAC aims to realize decentralized governance within the BP-FLAC community. The platform will extend its computational services to support fundamental infrastructure such as smart cities and meteorology.
“BP-FLAC, based on AI and a vast computing pool, provides limitless expansion possibilities for web3.” As stated by BP-FLAC’s CEO, Alexandrine, “We aim to meet the AI service needs of 200 million users on BP-FLAC in the future.
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About BP-FLAC
BP-FLAC(blockchain powered federated learning artificial intelligence computing platform) is the first-generation generative foundational infrastructure public chain with unlimited computational aggregation capabilities.
The Turing-complete ecological network supporting AI big computing power and intelligent applications. Based on the technological convergence of “Al+hybrid chain + NLP+ frameworks+low-code development platform”, users can create revolutionary AI products.