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In December 2024, the AI company Deepseek, established in Hangzhou, released its V3 model and was missing a fire storm of debate. The result is called ‘China’s AI shock’.
The comparable performance of Deepseek-V3 with its American counterparts such as GPT-4 and Claude 3 at lower costs makes doubts about the American dominance about AI options, underwent by the current export control policy of the United States focused on advanced chips. It also called the deep -rooted industrial paradigm, which prioritizing heavy hardware investments in computing power. To echo the comments of US President Donald Trump, the rise of Deepseek does not represent alone “A wake-up call” For the technical industry, but also a critical moment for the United States and its allies to re -assess their technological policy strategies.
What does Deepseek seem to have disturbed? Deepseek’s cost efficiency for its V3 model is striking: the total training costs are only $ 5,576 million, only 5.5 percent of the costs for GPT-4, which is $ 100 million. The training was completed with 2,048 Nvidia GPUsreaching resource efficiency eight times larger than American companies, which usually Requires 16,000 GPUs. This was achieved with the help of the less advanced H800 GPUs instead of the superior H100, but Deepseek delivered similar performance.
The cheap model of Deepseek therefore disputes the conventional wisdom that the refinement of large models corresponds to massive accumulation of computing power. This development may break the dependence on the American AI chips in the midst of semiconductor embargos, so that questions are raised about the traditional policy that focuses on high-quality calculation power control.
Unclear costs
There are various aspects of discussion about the Deepseek-V3 model that, however, require further clarification. The V3 model is on the same footing with GPT-4, while the R1 model, later in January 2025, corresponds to the advanced O1 model of OpenAi. The reported costs of $ 5,576 million specifically relate to Deepseek-V3, not the R1 model. This figure does not include the total training costs, because it excludes expenditure with regard to architecture development, data and earlier research.
The V3 model was trained with the help of data sets generated by an internal version of the R1 model before the official release. This approach was intended to use the high accuracy of R1-generated reasoning dataCombining with the clarity and conciseness of regular data. But the documentation of these associated costs is not announced, in particular on how the costs for data and architecture development of R1 are integrated into the total costs of V3.
Incremental innovation, no disturbance
From the point of view of technological competition, Deepseek’s progress in fundamental LLM technologies such as multi-head latent attention (MLA) and mixture of experts (tired) efficiency improvements show. But these claims should not cause excessive concern for policymakers, because these technologies are not tightly guarded secrets.
That said, there is real innovation behind the current excitement around the performance of Deepseek. MLA technology improves traditional attention mechanisms with the help of a low compression of key and value matrices. This drastically reduces the cachegal of the key value (KV), resulting in a 6.3-fold decrease in memory use compared to standard Multi-Head attention (MHA) structuresThis reduces both training and conclusion costs. Deepseek also seems to be the first company that successfully uses a large-scale scarce tired model, which means that their ability to stimulate the efficiency of the model and to lower communication costs by techniques for experts.
Although these developments are unusual, they can simply represent iterative improvements in the field of AI instead of a disturbing jump that could shift the overall balance of technological strength.
Indeed, nor the Deepseek-V3 nor the R1 model represents the pinnacle of advanced technology. Their benefit stems from delivering performance that are comparable to their American counterparts, but at considerably lower costs. In this respect, it is self-evident to question the cost-efficiency of the seemingly extravagant development approach that the American technical industry has assumed to equate pure computing power to the refinement of AI models.
Yet this type of cost -effective innovation is often not the focus of those on the technological pre -filling, equipped with abundant, advanced means. The initial iteration of each innovation usually makes high expenses. As the cost -saving innovations come to the fore, however, they increase the costs, so that late arrows, in particular in regions such as China, can quickly take over these progress and to overtake leaders at a lower costs.
Limits of our chip sanctions
The approach to Deepseek, with the LateComer benefit due to lower training costs, has fueled a debate about the real need for extensive computing power in AI models. Critics wonder if China should really depend on American advanced chipsChallenging the high-end computer-oriented policy that guides the current semiconductor export control scheme of Washington. If the performance parity can be achieved with chips with lower tier, the premium for chips with a higher tier cannot be justified.
However, this can be a misunderstanding, because chips with a higher tier generally offer greater efficiency. In economic terms, It would be impractical For all China -based companies such as Deepseek to prevent them from using more advanced chips if they were accessible.
In addition, the reduction of training costs may indicate the reduction of user costs to a decrease in financial barriers for AI -service -acceptance. The global AI industry is likely to see an increase instead of a decrease, in demand for computing power as competition between services intensifies. To keep track of the AI race, it needs a continuous stock of more advanced, high-end chips.
In these greetings, the scale law is still true. Deepseek has just shown that comparable results can be achieved with fewer capital investments – at least mathematical terms. In the field of hardware, this translates into more efficient performance with fewer sources, which is beneficial for the overall AI industry. And if Deepseek’s cost efficiency turns out to be feasible, there is no reason why American AI companies cannot adapt and keep pace.
Export the AI prize race from China
What should the United States and its allies really worry about? The most important question is: what if Chinese AI services can deliver performance that is comparable to their American counterparts at lower prices? Deepseek is an example of a development scenario that policy makers have to check closely – China starts a worldwide price war in AI services, a battle that is already underway in its own country.
The actual training costs of Deepseek-V3 and R1 models remain unclear. And the audience knows very little about whether they achieve such efficiency with only H800 GPUs with a lower frills. The usability of these claims must still be determined. But it is crucial here not to confuse the costs with the price. The exact expenses of Deepseek are uncertain, and it is not clear Whether the company used American models to train its own in ways that can violate terms of services. One thing that we know for sure is that Deepseek offers its AI services at exceptionally low prices.
For example, Deepseek-R1 calculates alone $ 0.14 per million input tokens (with the use of cachedages) and $ 2.19 per million executive tokens. On the other hand, the O1 -Model of OpenAi costs $ 1.25 per million in cache input tokens and $ 10.00 per million output tokens. This means that Deepseek-R1 is almost nine times cheaper for input tokens and about four and a half times cheaper for output tokens compared to OpenAi’s O1.
The competitive prices of Deepseek can in a certain sense be seen as an international projection of the Domestic AI service prize war from China from 2024. For example, Alibaba reduced the price of his QWen-Long by 97 percent In May last year and Furthermore, the costs of his visual language model, QWEN-VLAt 85 percent in December. In contrast to Deepseek, many Chinese AI companies have lowered their prices because their models do not miss competitiveness, making it difficult to match American counterparts. Even with these price reductions, attracting high -quality customers remains a challenge. Deepseek, on the other hand, offers performance that is comparable to competing products, making prices really attractive.
For Democratic allies, the rise of Chinese AI services that are both affordable and very effective evokes two primary strategic concerns, especially in the light of recent sovereign AI initiatives. Firstly, there are national security risks, in particular related to Datprivacy and the Possible manipulation of results. Secondly, the aggressive prices of China in AI services pose a threat to the development of AI industries in other countries, which resemble the dumping practices that were previously seen with solar panels And electric vehicles In Europe and America.
If this scenario unfolds, it must be acknowledged that the AI price advantage of China is unlikely, exclusively driven by lower training costs, which other companies can soon assume. Attention must also be paid to non-market mechanisms, such as government subsidies, that China can offer a competitive advantage in the future.