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May 2019 – London, UK –  A year ago, the core members of Ammut Networkcreated a vision for developing the biggest decentralized supercomputer on the blockchain. Since then, the trio has raised $4 million from investors who believe in their project. Ammut Network has announced that they have been accepted as a member of NVIDIA’s elite Inception Program.
Ammut Network is a problem-solving platform for computer learning, mining scientific calculations, CGI rendering, and 3D rendering. Its software allows users to easily become a host by renting out the company’s AmCU device(s) (Ammut Computing Unit). Its interface then lets users quickly find the best deals for computing according to their specifications.
Brian Ferguson, Ammut Network Hardware Architect reinforces the importance of the NVIDIA endorsement, stating;
“We believe wholeheartedly in our project and its potential, yet to get this vote of confidence from NVIDIA reinforces the strength of our concept. Their support will have a positive effect on the continued development of our project and provide increased value for our investors.”
NVIDIA’s Inception Program is a virtual accelerator program that helps startups during critical stages of product development, prototyping and deployment. It is known for singling out and nurturing startups with the potential to revolutionize industries with advances in AI and data science.
Bruno Trentini, head of Deep Learning Startups Manager, NVIDIA, stated
“I am thrilled to have Ammut Network on board the NVIDIA Inception Program and we look forward to helping them propel their business with our expertise in GPU-accelerated computing and Deep Learning”
The Ammut platform is comprised of the following:
- Ammut Computing Unit (AmCU): The Ammut Computing Unit, designed with optimized machine learning and AI capabilities, is a Single Board Computer (SBC) about the same size as a Raspberry Pi. It was built with tech pros and hobbyists in mind because developers would not have any challenges installing open-source frameworks such as TensorFlow, Caffe, and Keras for machine learning.
- Ammut Network: The Ammut network is made up of all the AmCU devices connected to the network. AmCU takes tasks from the network, based on the earning coefficient that was initially set by the user.
- XAMM: XAMM is the native token of the Ammut Ecosystem. It’s used to perform basic transactions within the Ammut Community such as payments and exchange of value with fiat and other cryptos.
- Ammut Software: The client side of the Ammut software can be used to create tasks that will be sent to the Ammut Network. The software handles the conversion of the tasks into suiting instructions that can be used inside the network.
Melissa Alexander, Software Development, Ammut Network stated her hopes for the application of home AmCU devices:
“We hope that in the future every home will own an AmCU device that will be useful, powerful, low-cost and profitable. We hope that together we can make the world a better place and bring blockchain to everyone’s attention, one use case at a time…we could not do it without the support of our investors and organizations like NVIDIA.”
To learn more about Ammut Network – https://ammut.network/
Read the Ammut Network Whitepaper – https://ammut.network/files/ammut.pdf
Follow Ammut Network on Twitter – https://twitter.com/ammut_network
Join the  Ammut Network Official Telegram – https://t.me/ammutnetwork
Find Ammut Network on Facebook – https://www.facebook.com/ammut.network
Connect with Ammut Network on Discord – https://discord.gg/8RcvhFh
YouTube – https://www.youtube.com/channel/UC3iW1m_nn1-e9l4WnE7DQTA
Media Contact
Contact Name:Â Charlie Blake
Contact Email:Â pr@ammut.network
Ammut Network is the source of this content. Virtual currency is not legal tender, is not backed by the government, and accounts and value balances are not subject to consumer protections. This press release is for informational purposes only. The information does not constitute investment advice or an offer to invest.
Disclosure: This is a sponsored press release
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