Decentralized Machine Learning Startup Gensyn AI Raises $43M Led by a16z

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Gensyn AI, a UK-based decentralized machine learning startup, has raised $43 million in Series A funding led by a16z crypto. Gensyn AI aims to connect global machine learning compute hardware, including GPUs and CPUs, and make it accessible to engineers, researchers, and academics. The platform’s goal is to enable seamless utilization of all available machine learning capable compute hardware worldwide for training work. Gensyn operates as a layer one proof-of-stake blockchain built on the Substripe protocol. The approach offers scalability without limitations and has low verification overhead, making it an attractive solution for developers. The funding round also saw participation from CoinFund, Canonical Crypto, Protocol Labs, and Eden Block, along with several angel investors. Gensyn AI’s focus is on machine learning, and their goal is to remain relevant and provide solutions in the machine learning landscape.

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Frequently Asked Questions (FAQs) Related to the Above News

What is Gensyn AI?

Gensyn AI is a UK-based decentralized machine learning startup that aims to connect global machine learning compute hardware and make it more accessible to engineers, researchers, and academics.

How much funding did Gensyn AI recently raise?

Gensyn AI raised $43 million in Series A funding led by a16z crypto.

What is the goal of the Gensyn AI platform?

The platform's goal is to enable the seamless utilization of all available machine learning capable compute hardware worldwide for training work.

What blockchain is Gensyn AI built on?

Gensyn AI operates as a layer one proof-of-stake blockchain built on the Substripe protocol.

Who participated in the recent funding round for Gensyn AI?

The funding round also saw participation from CoinFund, Canonical Crypto, Protocol Labs, and Eden Block, along with several angel investors.

What is Gensyn AI's focus?

Gensyn AI's focus is on machine learning, and their goal is to remain relevant and provide solutions in the machine learning landscape.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

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