Recreating the 'DeepSeek Moment'? Wall Street Says Kimi K3 Actually Strengthens Demand for Computing Power

Author: Long Yue

The market is panicking, treating Kimi K3 as "DeepSeek Moment 2.0," but this time, Wall Street's judgment is completely different.

On the night of July 16, Moonlight released Kimi K3 in Shanghai. This open-source model with 28 trillion parameters scored 57 on the Artificial Analysis Intelligence Index, ranking third to fourth globally, on par with Anthropic's Claude Opus 4.8 and OpenAI's GPT-5.5. More critically, K3 topped the Frontend Code Arena programming leaderboard created by the University of California, Berkeley, with a score of 1679, surpassing Claude Fable 5 and GPT-5.6 Sol, becoming the first open-source model to outshine all overseas closed-source models on an authoritative programming leaderboard.

On July 17, the U.S. semiconductor sector experienced a noticeable decline. The market's reflexive response is understandable—when DeepSeek R1 was released in early 2025, it triggered a sharp drop in computing stocks, based on the logic: As Chinese models become stronger, do American AI companies still need to spend so much on computing power? If Chinese models can approach cutting-edge capabilities at a lower cost, will the demand for Nvidia, HBM, servers, and network equipment be reassessed?

However, according to reports from the Wind Trading Desk, the latest research reports from investment banks such as UBS, Nomura, Bank of America Merrill Lynch, and Citigroup believe: Kimi K3 is not the end of computing power demand, but rather an accelerator.

Kimi K3 and DeepSeek R1 are not the same kind of impact. R1 made the market see "efficiency"; K3 emphasizes "scale." With 28 trillion parameters, a 1M token context, always-on inference, native multimodal capabilities, and MoE architecture, these features do not tell a story of light assets. They will raise the pressure on inference, memory, network, and storage together.

How powerful is Kimi K3?

Kimi K3 was released by Moonlight on July 16, 2026, with complete model weights scheduled to be opened on July 27. It is a 28 trillion parameter open-source large model, referred to by multiple institutions as the largest open-source weight LLM currently available.

The core configuration includes three points:

First, a 1M token context window. The model can handle longer texts, larger codebases, and more complex enterprise profiles and research tasks.

Second, always-on inference. It is not just simple Q&A but is aimed at long-chain reasoning and Agent tasks.

Third, native visual capabilities. K3 not only processes text but also addresses multimodal tasks such as video, images, game development, front-end design, and CAD.

In terms of architecture, K3 uses Kimi Delta Attention, Attention Residuals, and Stable LatentMoE. The MoE part activates 16 experts for each token out of 896 experts. Moonlight states that compared to Kimi K2, the overall scaling efficiency has improved by about 2.5 times.

This explains why K3 is not just a "cheaper K2." According to pricing compiled by Nomura, the input price for K3 is $3 per million tokens, with cache hit input at $0.30 and output at $15 per million tokens; according to Artificial Analysis, the cost of each task for K3 is about $0.94. This price is lower than Claude Fable 5's approximately $2.75 and Claude Opus 4.8's approximately $1.80, close to GPT-5.6 Sol's $1.04, but significantly higher than GLM-5.2's $0.32---$0.47, and far above DeepSeek V4 Pro's $0.04.

Thus, K3's positioning is not the lowest price, but rather achieving cutting-edge model capabilities at a lower price.

Four Investment Banks Intensively Set the Tone: This is Not a Demand Weakening

In response to market concerns about the "DeepSeek moment" impact, analysts from Nomura's Asia-Pacific technology team, led by Duan Bing, wrote in their report: "We believe that competition and innovation in the global large model market will not stop. As we get closer to Artificial General Intelligence (AGI), the application of generative AI on both consumer and enterprise fronts will continue to expand. Leading AI labs and hyperscale cloud platform companies may continue to invest during this phase to maintain their competitive positions—we interpret this competition as a positive for the AI infrastructure value chain."

Citigroup semiconductor analyst Peter Lee titled his report on July 19 as "Another Jevons Paradox." What does Jevons Paradox mean? In simple terms: the efficiency of coal steam engines improves, leading to greater coal consumption because more people can afford it and more scenarios can utilize it. The same goes for AI models—when high-quality models become cheaper, developers and enterprises will deploy more applications and process more tokens, ultimately increasing computing power consumption.

Peter Lee believes that even if K3 is widely used, the demand for general memory such as server DDR5 and eSSD will still increase. The reason is that K3's inference efficiency is comparable to other cutting-edge models, but the KV cache usage will expand with the context growth, increasing the pressure on memory rather than decreasing it.

Bank of America semiconductor analyst Vivek Arya expressed more directly in his report on July 17. He believes that the response from American leading AI labs is "not less computing power, but more." If Chinese open-source models continue to approach, OpenAI, Anthropic, and Google must maintain differentiation through larger-scale training, heavier inference, and faster iterations. Arya also mentioned a background that is easily overlooked: media reports indicate that Google's Gemini 3.5 Pro has been delayed for several months compared to the plan, and its programming performance has not met internal targets, making it "increasingly difficult to defend its leading position."

UBS analyst Timo Arcuri's team pointed out in their report on July 20 that there are indeed parallels between K3 and DeepSeek R1, but K3 is more about scale—it is the world's largest open-source model, with 28 trillion parameters and a 1 million token context window. Analysts emphasize that open-source models typically consume more memory than closed-source cutting-edge models because the context window is longer, and the KV cache demand continues to grow in absolute terms even after quantization, making the deployment of open-source models more reliant on HBM and storage.

Who Truly Benefits from This Competition?

Storage: The most direct beneficiary sector. UBS estimates that the cumulative free cash flow (FCF) of the storage and memory sectors is expected to reach about 30% of market value by 2028, the highest proportion among all sub-sectors—Micron (MU) alone accounts for as much as 47%. Both Citigroup and Nomura maintain buy ratings on Samsung Electronics, citing that the global memory market is in a state of extreme supply tightness. Citigroup analyst Peter Lee pointed out that Kimi K3's inference-side memory demand is no less than that of other cutting-edge models, and the expansion of KV cache volume will directly drive the demand for server DDR5 and enterprise-level solid-state drives (eSSD). He particularly noted that Kimi K3's large-scale deployment requires a "super-node" cluster configuration of more than 64 GPUs.

Computing Infrastructure: TSMC and Nvidia are the primary beneficiaries. Whether the scale law on the training side remains effective or the token demand on the inference side grows, it ultimately points to a greater demand for advanced process chips. Nomura reiterated buy ratings on TSMC, ASE, MediaTek, and others. Nvidia has publicly stated that the inference performance-to-power ratio of modern MoE (Mixture of Experts) models on GB300 NVL72 has improved by up to 25 times compared to the previous generation Hopper architecture. Models like K3 are naturally beneficiaries of Nvidia's latest hardware.

Networking: The super-node trend creates structural opportunities. Kimi K3 requires super-node clusters, and China's local computing power is limited by high-end chip export controls, necessitating reliance on super-node architectures to bridge the performance gap of single cards, which drives demand for optical modules, optical chips, and other network layer suppliers. Nomura is optimistic about Zhongji Xuchuang and Suzhou Xuchuang.

Cloud Platforms: Benefit from ecological aggregation effects. Cloud platforms hosting various cutting-edge open-source models have stronger bargaining power and do not rely on a single closed-source model supplier. Nomura is optimistic about Alibaba (BABA) as the core of China's AI cloud ecosystem, as well as GDS and VNET as data center operators.

How Fast is the Global Penetration of Chinese AI Models?

This may be the most easily underestimated data point in the entire narrative.

According to statistics from the open API gateway OpenRouter, the token usage of Chinese AI models accounted for less than 2% of global developer traffic a year ago, and it has now exceeded 45%. Data from Bank of America Merrill Lynch corroborates the acceleration of overall AI penetration: currently, about 55% of U.S. enterprises have subscribed to AI models, platforms, or tools, with Anthropic's enterprise adoption rate reaching 42% and OpenAI at 40%. Top AI consumers (the top 1% of enterprise users) are spending an average of $4,833 per employee per month on AI.

The market is diversifying. On one hand, there are Chinese open-source models represented by DeepSeek and Kimi K3, covering the economy and mid-to-high-end cost-performance markets; on the other hand, top U.S. cutting-edge models are focusing on more complex workloads (such as scientific computing), maintaining technological and pricing premiums. Nomura's judgment is that leading large model players on both sides of China and the U.S. will benefit—provided they can continue to stay at the forefront of the technology curve.

What K3 Truly Changes is the Competitive Rhythm, Not Just a Single Company's Story

After the release of K3, the market's first reaction was still to compare it with DeepSeek R1. This comparison is useful, but it cannot stop at the level of "is it going to crush AI hardware again?"

DeepSeek made the market reassess training efficiency. K3 shows the market another thing: open-source models can also push scale, long context, Agent, and multimodality to the forefront.

This will force American leading labs to continue investing and will allow Chinese models to continue expanding in the global developer ecosystem. Closed-source top models retain technological and pricing premiums, while open-source models cover more price ranges and deployment scenarios, with cloud vendors providing model distribution and enterprise landing, and the hardware chain bearing the pressure of training and inference.

In the short term, trading may fluctuate due to the "DeepSeek memory" effect. In the medium term, as long as token usage continues to grow, long contexts and Agents continue to spread, computing power, HBM, storage, network, and IDC will remain unavoidable cost items.

This is also the reason why multiple institutions have drawn similar conclusions after K3: stronger open-source models are not the endpoint of AI infrastructure demand, but may instead be the entry point for the next round of demand diffusion.

However, Bank of America Merrill Lynch also clearly left a tail risk: "If the speed of efficiency gains exceeds the growth of workloads, we may see some pullback in infrastructure construction." In other words, if models become cheaper, but usage does not expand significantly in tandem, the logic of computing power demand growth will be discounted.