Last updated
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Yes✅
Yes✅
Yes✅
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$50.6M
Website
Website
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Phase 0 - Currently Live 🔥 - Details: Start Time: 31.03.2025 Ending Time: TBA
Gensyn is an innovative decentralized machine learning infrastructure that optimizes the training of large neural networks. It uses an efficient protocol to connect and validate work done outside the main network (off-chain), reducing computing costs and ensuring scalability.
Gensyn works through the use of decentralized protocols that allow machine learning to run on a variety of computing nodes without the need for centralized management. The platform offers a neutral and open infrastructure for performing complex computational tasks related to training deep neural networks. It also provides users with the ability to optimize model training, reducing time and resources. Support for distributing tasks and computations to unknown or unreliable nodes reduces infrastructure costs while maintaining high efficiency.
Gensyn addresses the current scalability and efficiency challenges for deep learning that arise from the high cost and limited availability of traditional computing solutions. Through decentralization and the use of new methods of performance verification, Gensyn ensures efficient use of computing resources without high costs and with high accuracy. This approach opens up new opportunities for artificial intelligence research by lowering barriers to access to computing power.
Example of comparison: In traditional machine learning systems, such as using cloud platforms (e.g., AWS or Google Cloud), users face high costs for computing resources, in particular for using powerful GPUs or TPUs to train complex models. This can be prohibitively expensive, especially for small research teams or startups.
In contrast, Gensyn uses a decentralized infrastructure where participants can use their computing resources to perform machine learning tasks and get rewarded for their participation. This reduces computing costs and makes the process accessible to more participants. Thus, Gensyn reduces the cost of computing and provides greater flexibility and scalability than traditional centralized solutions.
