DeCompute Network
In the 1980s, PCs became useful as standalone devices, but their true value was unlocked by networking them together. Likewise, AI models today are standalone,...
Last updated
In the 1980s, PCs became useful as standalone devices, but their true value was unlocked by networking them together. Likewise, AI models today are standalone,...
Last updated
Today, the control of GPU resources has largely shifted to hyperscalers and major tech firms like Microsoft, Amazon, and Google, as well as various well-funded data centers and startups within the industry.
As AI emerges as the first significant technological innovation in more than a decade, capturing the public's imagination, the demand for high-performance computing is expected to skyrocket. Consequently, the cost of accessing such resources is also climbing.
However, a vast, underutilized potential lies dormant: the immense processing power of individual GPUs and small GPU clusters. These machines, often owned by gamers, video editors, or even crypto miners, sit idle or underutilized. The shift from Proof-of-Work (PoW) to Proof-of-Stake (PoS) in the cryptocurrency space has rendered many such machines "obsolete" for their original purposes. Yet, their computational muscle remains highly valuable for training and running AI models.
We harness the power of decentralized GPU resources. SynonAI is built upon the concept of DeCompute Network (Decentralized Compute Network). This system enables owners of idle or underutilized GPUs to contribute their computing power to the SynonAI network in exchange for rewards (details outlined in the SynonAI Token section). This globally distributed network of GPUs forms the foundation of SynonAI's infrastructure, providing the necessary processing power for its training and inference platforms.
Our platform acts as an intelligent dispatcher, routing training and inference requests to the most suitable volunteer computing nodes. This ensures efficient resource allocation, matching the specific needs of each AI project with the optimal hardware within the network.
Participants who contribute their idle GPU resources to the network are rewarded with SynonAI Token (details outlined in the SynonAI Token section), creating a strong incentive for users to join and contribute to the platform. A smart-contract based system is used to automatically distribute rewards based on each participant's contribution to the training process.
