Gonka's computing power across the entire network has exceeded 10000 H100 equivalents, with five major inference models using nearly 100 million tokens in a single day According to Gonka Browser data, the decentralized AI inference network Gonka's computing power has skyrocketed by nearly 20 times, surpassing 10000 Nvidia H100 equivalents and reaching 10729 (as of December 17th). This scale is equivalent to a large national level AI computing power center or a core AI cluster of top cloud vendors, which can simultaneously support training of billions of models and continuous high-throughput inference of billions of models. Gonka integrates global GPU resources through a decentralized approach, forming a sustainable and scalable high-performance AI computing network without the need for centralized data centers. This computing power milestone marks Gonka's official entry into the ranks of the world's top AI infrastructure networks, with commercial grade API service capabilities. Gonka currently supports 5 mainstream AI inference models. The main network has only been online for 3 months, and the total daily (per epoch) usage of inference models is nearly 100 million tokens. The mainstream model Qwen3-235B Instruction has a daily usage of about 30 million tokens, showing exponential growth potential. Currently, nearly 600 active nodes from more than 30 countries and regions have joined the Gonka computing power network, with over 2000 daily AI inference service users. The data shows that the growth rate of Gonka's inference usage has far exceeded the growth rate of network nodes and computing power, verifying the rigid demand for Gonka's decentralized inference services in the market and the feasibility of its business model. According to previous reports, Gonka, as an efficient decentralized AI inference and training network, recently received a $50 million investment from Bitfury and is supported by OpenAI investor Coatue, Solana investor Slow Ventures, and others. It is considered one of the most anticipated emerging infrastructures in the AI x DePIN direction.