Qujing Technology Completes Over 1.3 Billion Yuan Series B Financing, Raising a Cumulative 2.4 Billion Yuan This Year

Zhihui Dongxi (Smart East-West) news, October 26: Recently, Beijing-based AI Token production service provider Approaching.AI completed a Series B funding round of over RMB 1.3 billion; within this year it has raised a cumulative RMB 2.4 billion.

This round was jointly led by Zhecai Innovation, Xiaoshan Capital, ICBC Investment, Zhongguancun Science City, Xinglian Capital and Qianjiang Century City. Cloud Creation Capital, Hunan Gaoxin Zongheng, Jiantou Huake, Shenwan Innovation Investment, CITIC Juxin, Zhuhai Technology Industry Group, SenseTime, Wangsu Science & Technology, Gansu Shuzhi, Changjinkong, Yuanzhi Future, Qingzheng Xingzhu, Xiaoka Capital and others followed on, while existing shareholder Shangshi Capital continued to invest.

This is another large funding round for Approaching.AI shortly after it completed its Series A round in July this year. It is reported that over the past six months, several of Approaching.AI's core operating metrics have grown more than tenfold within half a year.

The funds raised in this round will be focused on expanding high-quality AI Token production capacity, upgrading and iterating the ATaaS (Approaching.AI Token as a Service) platform, R&D into underlying inference technologies and new products, and improving commercialization cooperation and delivery systems. Approaching.AI defines "high-quality AI Token" as the smallest production unit connecting computing power, models and application results.

Approaching.AI was founded in 2024 and is located in Beijing. Core members of the founding team come from the High Performance Computing Institute of Tsinghua University's Department of Computer Science and Technology. Among them, Zheng Weimin, an academician of the Chinese Academy of Engineering, serves as chief scientific advisor; Professor Wu Yongwei of Tsinghua University serves as chief scientist; and Zhang Mingxing, associate professor in Tsinghua University's Department of Computer Science and Technology, is a co-founder of the company.

Approaching.AI CEO Ai Zhiyuan holds a doctorate from Tsinghua University's Department of Computer Science and Technology, and his doctoral research focused mainly on parallel computing, distributed storage, AI computing engine optimization and related fields. He has extensive industry experience, having served as R&D head of multiple departments at Sangfor, including big data, digitalization and AI applications.

▲Approaching.AI CEO Ai Zhiyuan

In terms of technical approach, Approaching proposes "fewer models, deep optimization, high quality": "fewer models, deep optimization" addresses "how many high-quality AI Tokens the same batch of computing power can produce," while stable production loads and cross-cluster operations address "how much time this computing power is actually in effective working state." "Fewer models" does not inherently bring higher utilization; only when per-unit computing output and effective utilization improve simultaneously can the unit economics of the AI Token business become viable.

At present, Approaching.AI has formed a replicable model for building and operating high-quality AI Token factories. Over the past six months, along with the continuous launch and engineering refinement of multiple pioneering technologies, the company's average AI Token production efficiency per unit of computing power has climbed month by month, average daily output of high-quality AI Tokens has grown more than a hundredfold and risen to the trillion-plus level, and business revenue this year has reached nearly ten times that of the whole of last year. The overall business has crossed the cost line.

Facing performance differences between domestic and foreign chips, Approaching.AI has proposed a series of innovative technical solutions such as "domestic Prefill-Decode (PD) heterogeneous collaboration," "high-performance heterogeneous KV Cache conversion" and "heterogeneous computing power computation fusion," restructuring domestic computing power computation models and inference systems, and these have already been put into formal production in high-standard production scenarios.

In real production-grade environments, it has enabled domestic chips to carry trillion-level leading large models and achieved large-scale production of high-quality AI Tokens, while overall cost-effectiveness surpasses the world's most advanced computing power.

In 2026, domestic Token factories have become one of the most sought-after AI Infra tracks, with Tsinghua-affiliated entrepreneurs becoming a major force.

Previously, on October 12, Shanghai AI Infra company Infinigence AI confirmed it had completed a RMB 2.3 billion Series C round, with cumulative funding reaching RMB 4.3 billion, setting new domestic AI Infra industry highs for both a single round and cumulative funding, and it is preparing for an IPO; on the same day, Beijing AI Infra startup Qingcheng Jizhi, also Tsinghua-affiliated, completed a Series A round jointly led by China Telecom and Hanhui Capital. Previously, on September 20, Beijing AI Infra startup SiliconFlow, which has submitted a listing application to the Hong Kong Stock Exchange, announced it had successively completed the second phase of its Series B+ and its Series C round, with cumulative funding in 2026 approaching RMB 2.9 billion.