According to Richard Seroter, Google Cloud Run now supports NVIDIA RTX 6000 PRO Blackwell GPUs for AI workloads, enabling teams to serve 70B-plus parameter models without managing underlying infrastructure (source: Richard Seroter on X and Google Cloud blog). He highlights that drivers are pre-installed, no capacity reservations are required, and serverless instances offer 20 to 44 vCPUs with 80 to 176 GiB memory to streamline large language model inference and other high‑throughput tasks (source: Richard Seroter on X and Google Cloud Run documentation). This update allows on-demand scaling of LLM inference on Cloud Run while removing GPU infrastructure administration overhead for developers (source: Richard Seroter on X and Google Cloud blog).

According to @paoloardoino, Tether has open-sourced an edge-first generalized LLM inference and LoRA fine-tuning framework built for heterogeneous GPUs, highlighting a concrete AI tooling release that crypto traders can verify directly. Source: @paoloardoino on X: https://twitter.com/paoloardoino/status/1995871771875283434 According to @paoloardoino, the announcement does not include any token, pricing, or on-chain integration details, which limits immediate valuation signals but marks a notable technical step by the USDT issuer into AI infrastructure. Source: @paoloardoino on X: https://twitter.com/paoloardoino/status/1995871771875283434 According to @paoloardoino, the post frames this as the start of an AI ubiquity era, indicating Tether’s strategic positioning around edge AI compute and generalized model inference capabilities that may shape narrative monitoring for traders. Source: @paoloardoino on X: https://twitter.com/paoloardoino/status/1995871771875283434

According to Yann LeCun, a new hardware-independent Large Language Model (LLM) inference engine from ZML has been developed. In a social media post, LeCun highlighted this technology, which is significant for the AI and cryptocurrency sectors as it aims to reduce dependency on specific, high-end hardware for running AI models. For the crypto market, particularly AI-focused tokens and Decentralized Physical Infrastructure Networks (DePIN), this development could be a game-changer. By allowing AI applications to run on a wider array of hardware, it could lower the operational costs for decentralized AI projects, potentially increasing their adoption and impacting the valuation of related crypto assets. This move towards hardware independence could disrupt the current market dynamics, where a few companies dominate the AI chip industry, and foster a more decentralized and competitive AI ecosystem.