According to Kekalf, The Vawlent (@NFT5lut), there is a proposed experiment to run decentralized AI on edge devices using open source frameworks, aiming to provide a freely available, faster, and cheaper solution compared to traditional centralized models (source: Twitter, June 1, 2025). This approach could significantly reduce operational costs and latency, directly benefiting decentralized AI crypto projects and edge computing tokens. The adoption of decentralized AI infrastructure may attract further investment in related cryptocurrencies, impacting market sentiment and trading volumes for tokens like Fetch.ai and Bittensor.

According to gensyn (@gensynai), RL Swarm has expanded its decentralized network by adding Metal support, enabling Mac users with Apple silicon (M2, M3, M4) to train AI models significantly faster (source: @gensynai, May 28, 2025). This upgrade increases the network's computational capacity, potentially making decentralized AI training more efficient and accessible. For crypto traders, this enhancement may boost interest in RL Swarm-related tokens and decentralized AI infrastructure projects, as faster training could drive adoption and ecosystem growth.

According to Paolo Ardoino, the future of AI model training will not rely on the brute force of 1 million GPUs. Instead, the development of better models will significantly reduce training costs, emphasizing that access to data will remain crucial. Ardoino suggests that inference will move to local or edge computing, making the current expenditure on brute force methods seem inefficient in hindsight.