Decentralized Cloud Computing Empowering AI Model Training on Akash Network

Monday, 1 April 2024, 22:00

The collaboration between Overclock Labs and ThumperAI on Akash Network showcases the potential of decentralized cloud computing in advancing AI model training. The project aimed at addressing challenges in generative AI foundation model training, paving the way for accelerated developments in the AI and cloud computing integration. Through leveraging Akash Network's GPUs, the initiative marked a significant milestone in demonstrating the feasibility and efficiency of training foundational models in a decentralized cloud environment.
https://store.livarava.com/5291124f-f076-11ee-892e-87cc5c87fb08.jpg
Decentralized Cloud Computing Empowering AI Model Training on Akash Network

Using Akash Network to Train AI

The initiative aimed to push the boundaries of decentralized cloud computing by training a foundation model in AI. High costs, hardware requirements, and software complexities were addressed through this groundbreaking project, emphasizing the need for decentralized platforms to cater to the demands of AI startups.

ThumperAI's Lora Trainer Service

ThumperAI focused on enhancing its Lora Trainer service for AI developers by utilizing LoRA techniques for model training. On the other hand, Akash Network showcased its capability in AI model training efficiency on a decentralized cloud platform through this collaborative effort.

  • Proved concept
  • Attracted developer community
  • Demonstrated demand for GPU providers
  • Strengthened Akash Network's position in open-source contributions

Outcomes and Milestones

Despite challenges with dataset quality, the project successfully demonstrated the training of foundational models on Akash Network. The feat highlighted the convergence of AI and blockchain technology, laying the groundwork for further advancements in utilizing decentralized platforms for AI model training.


This article was prepared using information from open sources in accordance with the principles of Ethical Policy. The editorial team is not responsible for absolute accuracy, as it relies on data from the sources referenced.


Related posts


Newsletter

Get the most reliable and up-to-date financial news with our curated selections. Subscribe to our newsletter for convenient access and enhance your analytical work effortlessly.

Subscribe