According to @DeepSeek_AI, DeepSeek has disclosed the training methods behind its advanced mixture-of-experts AI models, DeepSeek-R1 and DeepSeek-V3, achieving significant cost savings by utilizing 2,048 Nvidia H800 GPUs and implementing memory-efficient techniques such as FP8 precision (source: DeepSeek_AI Twitter, June 2024). These breakthroughs in AI training efficiency could lower entry barriers for blockchain projects leveraging AI, potentially reducing development costs for decentralized AI protocols and increasing competition in AI-powered crypto tokens. The adoption of advanced GPU hardware and novel optimization methods may also influence demand for related crypto infrastructure and layer-1 blockchain projects.

According to @theamrelhady on Twitter, Hyperbolic's compute marketplace now features H100 GPUs starting at just $0.99 per hour, providing cost-effective access for AI model training and crypto mining (source: twitter.com/theamrelhady). This competitive pricing can lower operational costs for traders leveraging GPU acceleration for crypto algorithmic trading, mining, or machine-learning-driven market analysis. The platform's transparent pricing allows users to compare and select the most affordable compute resources, supporting profit optimization strategies in the volatile crypto market.

According to Hyperbolic Labs (@hyperbolic_labs), traders and developers can now access the lowest GPU rental prices this week, enabling rapid deployment of computational resources for AI model training or crypto mining in under 60 seconds (source: Twitter, May 19, 2025). This cost reduction in GPU infrastructure has direct implications for crypto market participants, as it lowers barriers for mining operations and accelerates blockchain-based AI project development. Reduced GPU costs may lead to increased mining activity, potentially impacting the network difficulty and profitability of major cryptocurrencies such as Bitcoin and Ethereum.

According to @bolsaverse on Twitter, MirraTerminal has introduced an incentivized system where users can tag the platform with quality crypto-related content on Twitter. If the submission is validated, participants earn points, with a daily cap of 5 attempts. This mechanism directly supports an AI model that is being trained on the validated content, creating a feedback loop for content curation and AI training. Additionally, content creators can accumulate XP, further boosting engagement and potential rewards. This model offers early adopters opportunities to earn by contributing to AI-powered crypto content curation, potentially influencing future trading intelligence tools (source: @bolsaverse, April 29, 2025).

According to Paolo Ardoino, Tether has unveiled a preview of its new AI model training platform, which will be available as a Platform-as-a-Service (PaaS) for companies to train their own models. The platform utilizes Holepunch technology, enhancing data structure resilience, crucial for reliable AI model development.

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.