Qutrunk Technology Completes Over 1.3 Billion Yuan Series B Financing; Average Daily Output of High-Quality AI Tokens Grows Over 100-Fold in Half a Year

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

In the early stage of the Token market's development, the industry's supply side mostly "emphasized Token volume over service quality," rapidly expanding supply scale through extensive volume growth. At that time, large models were still in the pilot validation stage and had not been deeply embedded in enterprises' core production chains, so business had a large margin of tolerance for performance, stability and model quality, and this supply model was accepted by the market. However, since its founding, Approaching.AI has consistently anchored itself to production-grade inference standards, continuously invested in original innovation in inference systems, consolidated its engineering foundation, and was the first in the industry to define the concept of high-quality AI Token service.

The "high-quality AI Token" defined by Approaching.AI: anchored to production-grade scenarios, providing AI Token services with high intelligence, low latency, high speed, strong stability, and both large-scale delivery capability and controllable overall cost.

An industry inflection point has emerged, and the Token market has rapidly formed a clear quality stratification: "usable is enough" extensive supply can no longer match the demands of production scenarios, while high-quality AI Token supply that balances multiple dimensions of metrics is scarce and in surging demand, becoming a rigid market need. As the earliest layout player and deep cultivator in high-quality AI Token services, Approaching.AI, relying on its long-accumulated underlying inference system technologies and engineering capabilities, continues to advance the large-scale delivery of high-quality AI Token services. Approaching.AI has now become the leader in the high-quality AI Token service field.

The ability to continuously deliver high-quality AI Tokens at scale ultimately depends on two core propositions: whether per-unit computing power efficiency can continuously improve, and whether large-scale supply capability can grow steadily.

Around this goal, to fulfill the production standards of high-quality AI Tokens, Approaching.AI adheres to the technical route of "fewer models, deeper optimization, high quality," focusing on mainstream large models with clear market demand and wide application, continuously advancing the engineering implementation of multiple globally pioneering technologies such as "heterogeneous inference," "trading storage for compute," and "virtual-real isomorphism," and focusing on the high-quality AI Token needs of enterprise production scenarios.

"Fewer models, deeper 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 truly in an effective working state." "Fewer models" itself does not naturally bring higher utilization; only when per-unit computing power output and effective utilization rate improve simultaneously can the unit economics of the AI Token business become viable.

The business logic behind this route is very clear: enterprise customers ultimately pay for business results, not for the number of compatible models.

At present, Approaching.AI has formed a replicable model for building and operating high-quality AI Token factories: there are already commissioned projects with daily production capacity of trillions of Tokens, as well as strategic cooperation and under-construction layouts for regional industrial ecosystems and top-tier customers. More importantly, the overall business has crossed the cost line. Real performance has verified the ATaaS platform's large-scale delivery and continuous operation capabilities.

Enabling domestic computing power to support the production of high-quality AI Tokens has always been Approaching.AI's goal.

Facing performance differences between domestic and foreign chips, Approaching.AI has explored a new path to achieve breakthroughs in computing power efficiency through system-level technological innovation, and has successively taken the lead in proposing 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 the domestic computing power computation model and inference system, which have already been officially put into production in high-standard production scenarios.

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

Approaching.AI's pioneering heterogeneous collaborative inference technology, while ensuring model output quality, response speed and service stability, achieves higher effective Token output with lower hardware investment, and stably undertakes the real business needs of top-tier customers.

This breakthrough has dual pioneering value: at the commercial level, it builds a differentiated core technology moat, opens up the growth ceiling for Approaching.AI's large-scale co-building of high-quality AI Token factories, and supports continuous leaps in performance and capital value; at the industrial strategic level, it helps break away from technical competition that merely compares hardware parameters, redefines the value yardstick of domestic computing power, enables domestic chip assets scattered across the country and of different architectures to release full inference capacity, and provides a deployable core engineering solution for the country to build an autonomous, controllable, abundantly supplied and commercially economical AI inference foundation, further consolidating the security of China's large model industry chain and the foundation of computing power autonomy.

Continuous independent technological innovation is the core driving force behind the exponential growth of Approaching.AI's high-quality AI Token output.

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, continuously leading the industry; average daily output of high-quality AI Tokens has grown more than a hundredfold and climbed to the trillion-plus level, and business revenue within the year has reached nearly ten times that of the whole of last year.

In terms of industry landscape, Approaching.AI is driving AI infrastructure to upgrade from traditional computing power resource supply to large-scale production and operation of high-quality AI Tokens. Relying on the ATaaS platform and its Token factory construction and operation capabilities, the company has, within half a year, worked with industry partners to advance more than ten AI Token factory projects with daily output of trillions of Tokens, promoting the large-scale application of domestic computing power, and jointly with local state-owned capital, industry leaders and ecosystem partners, building a new AI Token supply network connecting computing power infrastructure, large models and industry applications.

Approaching.AI CEO Ai Zhiyuan said: In the race to transform computing power into high-quality productivity, the truly scarce barrier is not scale, but per-unit energy efficiency and commercial delivery capability. Over the past half year, Approaching.AI has proven the enormous commercial potential of the "fewer models, deeper optimization, high quality" route with more than tenfold business growth. The continued capital injection is an affirmation of Approaching.AI's business delivery results. Looking ahead, Approaching.AI will rely on this round of funding to accelerate capacity expansion, deepen the implementation of domestic PD heterogeneous solutions, support customers' large-scale applications with more stable, more efficient and more cost-effective high-quality AI Token services, promote domestic heterogeneous resources to enter real production scenarios more fully, and work with industry partners to build a diverse, stable and sustainable AI inference supply system.

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