On July 19, 2026, the upstream and downstream of the AI industry chain seemed to have tightened the dam gate simultaneously. On one end is Kimi: it has officially disclosed that the usage demand for the latest version Kimi K3 has surged far beyond the team's expectations in the past 48 hours, and the GPU resources on the inference side have quickly approached the current carrying limit, forcing it to announce a suspension of new subscriptions. The team can only hurriedly expand computing power while planning to gradually open new subscriptions in batches and split membership plans, temporarily setting up a dam between users eager to experience a stronger assistant and the hardware ceiling. On the other end is Changxin Technology: as the leading domestic DRAM storage chip manufacturer, it is reported that it plans to be listed on the Shanghai Stock Exchange's Science and Technology Innovation Board on July 27, with an expected initial market value of about 580 billion yuan based on a single source. Predictions claim it is poised to challenge the position of the "third largest supplier" in the global DRAM market, tightly binding this company to the narrative of the "AI upcycle." One side is the front-end application Kimi K3 rapidly penetrating the C end and developer side, transforming the tight global GPU computing power background into the reality constraint of "insufficient computing power"; the other side is an upstream storage company moving towards the capital market while DRAM is seen as being in an upcycle. The simultaneous resonance of computing power tightness on the application side and upstream storage capitalization outlines a simple premise: the current AI industry chain stands at the intersection of tight supply and heightened expectations.
48-Hour Surge: Kimi Forced to Hit the Pause Button
After the launch of Kimi K3, the demand curve has almost shot straight up. Within just 48 hours, the official description of this influx of usage requests is "far beyond expectations": C end users are using the latest version as a daily information, writing, and chat entry, while developers quickly integrate it into their workflows, calling it frequently across multiple scenarios. During the same period, however, the GPU clusters supporting these calls have not been synchronized to expand, and the computing power is being continually drained, approaching the current carrying limit, ultimately forcing Kimi to hit the brakes while user enthusiasm has not yet waned.
On July 19, Kimi officially acknowledged this pressure on computing power: GPU resources have approached the usable limit, and to avoid an overall loss of service quality, it can only suspend new subscriptions and push back the growth pace. The official response simultaneously presented directional guidance — to quickly expand computing power, later reopen new subscriptions in batches, and split the original single membership plan into two types to buffer the competition for resources among different users. When placing this incident back into a larger industry context, its direction becomes clearer: in the face of ongoing tight global GPU supply, the rapid penetration of large models and AI assistant products, and multiple products competing for the same pool of computing power, Kimi's surge in demand and pause are not just an incidental event for a popular application, but a clear slice of the entire AI application layer being squeezed against foundational computing capacity.
From GPU to Memory: How Computing Power Bottlenecks Amplify Layer by Layer
Kimi pushed GPU resources to the carrying limit within 48 hours, appearing to "get stuck at the graphics card" level, but breaking it down technically reveals that the entire computing power system is what has really tightened. Training and inference of large models have never been a solo performance of a single chip but a symphony among GPUs, high-bandwidth memory, I/O, and networks: model parameters need to reside in DRAM over time, and high concurrent requests must traverse through limited memory bandwidth. If any link falters, the peak computing power of the GPU will inevitably be forced to "downshift." Thus, when the application side suddenly amplifies request volumes, the first thing exposed is the GPU queue, but what is being simultaneously compressed behind it is the DRAM capacity, read-write performance, and the memory bandwidth redundancy of each server in the entire cluster.
This structural tension does not only occur with a single product. Global AI demand has steadily peaked recently, with training and inference spreading from points to surfaces, pushing GPUs and related memory products into a tight supply and upward price cycle. Domestic applications are running particularly fast, with new versions like Kimi K3 frequently iterating, compounded by various models being deployed in enterprises. Local data centers and computing power clusters can no longer simply "buy a few more cards" to cope, but are forced to face the systemic constraints of upstream hardware. The foundation of AI servers and training clusters is not just GPU slots, but also a full row of high-bandwidth DRAM slots; these storage chips constitute the "foundation" of computing power infrastructure and have pushed companies like Changxin Technology into the spotlight. The story of computing power is expanding from the crowded queues on the application side to a long-term game over whether upstream storage chips can keep pace.
Changxin Technology Takes the Stage: Domestic DRAM Bets on the AI Cycle
At the same juncture when computing power demand was concentratedly exposed by applications like Kimi K3, upstream domestic DRAM leaders also formally stepped into the spotlight. Changxin Technology is regarded as the listed entity corresponding to Changxin Storage (CXMT), essentially a company focused on DRAM storage chips, positioned at the forefront of AI infrastructure in that row of "memory slots." The GPU is responsible for running the models, while the DRAM is responsible for holding the parameters and data, together determining how much load an AI server can endure, which also explains why the capital market has shifted its focus from crowded inference queues to Changxin's prospectus.
Reports indicate that Changxin Technology plans to be listed on the Shanghai Stock Exchange's Science and Technology Innovation Board on July 27, 2026, with an expected initial market value of about 580 billion yuan based on a single source. While this number carries some uncertainty, it sufficiently illustrates that market interest in this domestic DRAM company has been ignited by the AI cycle. At this stage, driven by global demand for AI servers and high-performance computing, DRAM is considered to be in an upward cycle. Investors are starting to assign valuation premiums to upstream storage chips based on the narrative of "computing power shortage" and hope that Changxin Technology may have the opportunity to challenge the position of the third largest supplier of global DRAM after it goes public. While the application side Kimi is forced to throttle due to GPU tightness, the upstream Changxin is expected to enter the capital market amid high expectations. Actions on both ends point to the same main line: whoever can occupy a critical position in this AI-driven DRAM upcycle has the opportunity to reshuffle the global computing power industry chain.
A Capital Gamble: Valuation Stories and Industry Position Competition
On the same timeline when Kimi had to "hit the brakes" due to the tight GPU resources, the capital market has already begun weaving another version of the computing power story for Changxin Technology. The logic is not complex: since GPUs are tight globally, and large model training and inference have pushed AI servers to full capacity, then the high-bandwidth DRAM tied to it is seen as the next "core chip." Consequently, as the leading domestic DRAM supplier, Changxin has been compared to overseas giants such as SK Hynix. One report even suggested an initial market value of approximately 580 billion yuan, further projecting an optimistic scenario of "challenging for the third place in global DRAM." Narrative-wise, these are all bets surrounding the AI cycle, reflecting future demand curves in advance rather than based on already realized performance and market value data.
The key to this bet lies in abstracting the specific pain point of "Kimi lacking GPUs" into the grand proposition of "shortage of computing power infrastructure in the entire AI era," and then naturally extending it to the premium space of upstream storage chips. The chain for investors is as follows: the surging demand for AI assistants from C-end and developers drives continuous expansion of large model clusters comprised of GPUs and DRAM; under the propulsion of global AI demand, DRAM enters an upward cycle, where if Changxin can expand production and promote domestic replacement, it has an opportunity to achieve a higher position in the new industrial division of labor. However, this chain currently remains more in the realm of predictive views: first, Changxin has not officially gone public, and market judgments about its valuation and global ranking are still in the phase of expected games; second, even if upstream expansion proceeds smoothly and domestic DRAM accelerates penetration, whether it can truly translate into a substantial alleviation of the computing power crisis faced by applications like Kimi in terms of time and technical pathways — as opposed to merely remaining as a capital-level expected transaction — still needs to be tested by the long-term performance of reality.
After Upstream and Downstream Resonance: The Next Step in the AI Computing Power Landscape
At this moment on July 19, 2026, Kimi was forced to pause new subscriptions due to the surge in demand for K3 within the past 48 hours while GPU resources were nearing limits. Meanwhile, news of Changxin Technology's plan to be listed on the Science and Technology Innovation Board on July 27 was fermenting in the market, ringing two consecutive gongs from the application side to the hardware side, exposing the synchronized tension and acceleration of the entire AI computing power chain under the spotlight. The downstream Kimi has clearly stated it will expand computing power and gradually open new subscriptions but cannot provide a specific timeline for capacity expansion; meanwhile, upstream Changxin is expected to rely on the AI-driven DRAM upcycle to challenge for a higher global position, yet how much and at what pace it can alter the computing power supply landscape remains in the realm of future assumptions. This means there is a significant time misalignment and path deviation between the real constraints on computing power and the capital markets' expectations of "domestic computing power infrastructure accelerating to be fulfilled," and both investors and users need to be cautious not to misinterpret short-term stock prices and valuation stories as a simplistic optimistic narrative of an imminent disappearance of computing power bottlenecks. Over a longer time horizon, if Changxin Technology and more domestic upstream suppliers successfully complete capitalization, gradually fulfilling expansion and product iteration with the support of financing capability and technological investment, the result may be over a span of several years to provide more stable and cost-effective GPUs and DRAM foundations for applications like Kimi, slowly transitioning from the current occasional "surge and throttle" experience to a more predictable service quality and innovation rhythm. Ultimately, this medium to long-term change will depend on the true supply capability that the entire industry chain traverses through the cycle.
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