NVIDIAs

DeepSeek no longer wants to compete only with models. Its new front aims directly at NVIDIA’s business, according to Reuters

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In just over a year, DeepSeek has stopped sounding like a rarity in the Chinese industry to become one of those names that already appear every time we talk about the global race in artificial intelligence. First we look at it for its models, for its efficiency and for the shock it caused beyond China. Now the question begins to move to another terrain: what happens when a company that competes in software understands that the next advantage may be in the chips that make it possible to execute that AI on a large scale. The jump to hardware. The information that opens this new front comes from Reuters. The agency assureda, citing three people familiar with the matter, that DeepSeek is developing its own artificial intelligence chip, aimed at inference tasks and not training new models. We will see the technical nuance immediately, because it changes the reading of the movement quite a bit. For now, caution is mandatory: DeepSeek has not publicly confirmed the project would be in an early phase and the company did not respond to the agency’s request for comment. The key is in inference. The easiest way to understand this is to think about what happens after training. Once the model is built, every question we ask and every answer we receive requires putting it to work again. It is not an isolated operation, but a routine that is repeated millions of times if the product works. That is why a chip designed for that phase does not aim so much at technical prestige as at something more earthly: making using AI cheaper, faster and less dependent on third parties. The move is best understood if we look at what DeepSeek has depended on so far. The company has used chips from NVIDIA and Huawei to train and run its models, including the base that held R1, trained on NVIDIA H800a chip designed for the Chinese market whose export to China was banned by Washington at the end of 2023. Since then, DeepSeek has increasingly relied on Huawei: In April it launched its V4 model adapted to Ascend and Huawei said its processors were used in part of V4-Flash training. DeepSeek is no longer a footnote: Until not so long ago, the global debate on AI seemed to revolve almost entirely around American companies such as OpenAI, Google, Microsoft, Meta or Anthropic. DeepSeek changed part of that conversation by demonstrating that China could also produce models capable of circulating outside its domestic market and force the industry to look towards Hangzhou. Recall that the company was widely celebrated in China as a national AI champion. The trend is already seen in a good part of the sector. Google has been developing its TPUs for years, Amazon has Inferentia for inference payloads, Microsoft has Maia and Meta works at MTIA. Reuters also cites two recent movements especially close to the case: OpenAI announced its Jalapeño chip with Broadcom in Junealso oriented to inference, and Anthropic was considering designing its own chips. The pattern is quite clear: large AI companies want to rely less on third-party providers and better control the cost, performance and availability of the computing that powers their services. The big obstacle is manufacturing it. Designing a competitive chip is not the same as wanting to have it. Developing an AI accelerator typically requires years, a lot of capital, and a network of design, foundry, and memory partners. For a Chinese company, furthermore, the problem does not end at the technical level: US export controls limit access to the most advanced foreign factories and also to high-bandwidth memory, a key component for this type of chips. Times change. NVIDIA arrived at the AI ​​boom with an advantage built over decades: in 1999 it launched the GeForce 256, presented by the company itself as the industry’s first GPU, and in 2006 launched CUDAthe architecture that helped take the parallel processing of its chips beyond graphics. When the models started requiring massive amounts of compute, I already had the hardware and ecosystem in place. For years, for much of the industry, competing in AI meant going through its chips. What the DeepSeek case suggests, with all caution, is that this dependency is beginning to have cracks. Images | Xataka with Nano Banana In Xataka | Samsung earns 19 times more than a year ago. Investors have reacted by sinking the stock 7%

Nvidia’s solution to reduce water consumption in data centers to zero aims to be genius: use hot water

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Nvidia has announced a system of liquid cooling very special. Above all, because the concept of “refrigeration” is a bit confused here. The company has managed to reinvent this type of systems, and according to those responsible “the challenge of water consumption for data centers it is practically solved.” That’s saying a lot… or isn’t it? Jacuzzi cooling. The own Nvidia engineers They begin the description of their system by talking about how in jacuzzis the water is usually between 38 and 40 ºC, a temperature that makes most people last about 15 minutes in them. And the funny thing is that Nvidia’s new AI servers can use liquid cooling with water that is even hotter: up to 45ºC (113ºF). That is precisely the key to making the entire system efficient. Goodbye to fans. Nvidia’s new Rubin architecture claims to be the first in the world with an end-to-end liquid cooling system. At Nvidia they seal the server boards and eliminate noisy fans that shoot noise levels above 85 decibels. Elementary physics. The concept behind this idea is counterintuitive, but also brilliant. The chips generate so much heat that a liquid composed of 75% water and 25% propylene glycol entering at 45ºC is capable of absorbing the thermal load dissipated by these chips. The fluid absorbs that heat and ends up leaving the circuit at around 55ºC without the performance of the processor degrading at all. Good for 45ºC. The key is that Nvidia starts from that starting temperature of the water, which is 45º. In a liquid cooling circuit in PCs, the liquid is usually between 25 and 30 ºC. Here Nvidia manages to ensure that with this initial temperature the thermal difference with the outside air is high enough for the system to work passively in most temperate climates. The heat is expelled by gigantic external radiators. Water is only needed on the first fill. Even more interesting is the fact that this water circuit only needs to be filled once for the entire useful life of the plant, at least in theory. This eliminates traditional systems that use evaporative cooling towers. These systems consume enormous amounts of water, and according to Nvidia this makes it possible to cut water consumption of data centers by almost 100%. But. Although Nvidia’s idea is promising, the company only talks about what happens within the four walls of data centers. The impact internally in the data center is extraordinary, but what about outside? The problem, they explain on TechCrunchis that data centers they need a lot of energyand both the generation of that energy and the creation of the chips themselves used in data centers can fold either triple to the consumption of the data center itself. The gas and coal bill. Although renewable energies are increasingly covering a greater part of the needs in these data centersthe use of coal and natural gas will continue to be very notable in these facilities. According to the International Energy Association (IEA), these two sources will continue to represent 40% of the total used in AI data centers until at least 2030. And the generation of both types of energy requires vast quantities of water: natural gas plants they use 1.17 liters of water for every kWh of electricity they generate. Coal ones are even worse, requiring 2.2 liters per kWh. Good news, but not so good. The system devised by Nvidia can solve the problem within data centers and is even promising when it comes to reduce noise levels generated by these facilities. However, there are still equally important challenges to ensure that water consumption in other phases of this cycle is not so colossal. It is a good step, without a doubt, but the room for improvement is still notable in this area. In Xataka | Jensen Huang has taken a look at the idea of ​​putting data centers in space and has come to one conclusion: let’s not freak out

The US opened the door to Nvidia’s H200 chip in China. The Chinese army has been waiting for a long time on the other side

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Jensen Huang, the CEO of Nvidia, has been forced to “fight” with the US Department of Commerce for months, but he has achieved what he wanted: your company can now deliver some of its Chinese clients its chip to artificial intelligence (AI) H200. As we explain to you On May 14, Alibaba, Tencent, ByteDance and JD.com are four of the ten Chinese companies that already have access to this powerful GPU. And they have it because the US Department of Commerce, which is the institution that grants or denies export licenses, has authorized at least ten Chinese companies and several distributors, including Lenovo and Foxconn, to acquire the second most powerful AI chip that Nvidia has. This decision has come almost two months after the US Government confirmed which was going to allow the company led by Jensen Huang to deliver its H200 chip to its Chinese customers. However, Nvidia likely won’t have time to savor this victory. Once again, dark clouds are gathering over it that threaten to compromise, once again, its business in China. And, according to Bloombergat least seven Chinese universities linked to the country’s armed forces and defense industry are trying to obtain H200 chips. This disclosure comes from China’s public procurement records, so it is presumably reliable. Remote rental: the avenue that the Department of Commerce still does not know how to close In the US there is a pressure group that opposes the sale of advanced American AI chips in China. Chris McGuire, senior fellow on China and emerging technologies at the Council on Foreign Relations, holds that “any deal that allows Nvidia to sell more chips to China means fewer Nvidia chips for US companies and a smaller US advantage over China in AI.” Besides, McGuire argues that “it is surprising that President Trump continues to allow himself to be convinced to put Nvidia’s interests before those of America.” Chinese entities increasingly resort to renting airtime on servers equipped with restricted Nvidia chips What is happening right now with Chinese universities is the ideal breeding ground to reinforce the theses of this pressure group in the US. Two of the institutions that have expressed interest in H200 chipsBeihang University and Northwest Polytechnic University, are among China’s “Seven Sons of National Defense”, a select group of universities dedicated to supporting the People’s Liberation Army. Both have been included in the blacklist of the US Department of Commerce for their involvement in the advancement of Chinese military capabilities. And public procurement records reveal that the Beihang School of Cyber ​​Science and Technology, which claims to have “national defense characteristics and aerospace advantages,” is attempting to rent the use of Nvidia chips. Northwestern Polytechnic University’s School of Cyberspace Security is also trying to rent access to H200 chips, according to those same records. Chinese entities are increasingly resorting to time of use rental on servers equipped with restricted Nvidia chips as a way to access prohibited hardware without having to import it directly. This is the strategy that the US Government will surely try to dismantle. What is not clear at the moment is how he is going to do it. Image | Nvidia More information | Bloomberg In Xataka | The US remains committed to stopping China. Now it has targeted the second largest Chinese chip manufacturer

China already has a GPU that competes with Nvidia’s RTX 3060. The bad thing is that it arrives five years late and worse

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The china crusade for achieving the complete independence in the field of semiconductors has taken a new step. The problem is that this step has not been as promising as we expected, and in fact it makes it clear that today the Asian giant is still far away of the semiconductor manufacturers that dominate the market. The alternative for gamers that promised. Lisuan Tech (砺算科技), a Chinese company dedicated to manufacturing semiconductors and solutions such as graphics cards for the end-user market, has launched its new GPU for the consumer market, the LX-7G100. The price and expectations. The official starting price is 3,299 yuan (about 420 euros at the exchange rate), and at that price the equivalent graphics card should be at least an RTX5060 Ti, which is usually below 400 euros. What we get in performance is far from that. Performance tests of the LX-7G100 typically fell well short of the RTX 3060.Source: NotebookCheck. Worse than the RTX 3060. The problem is that those who have had access to this graphics card and have evaluated their benefits They have realized that this manufacturer’s GPU is very far from that price/performance estimate. In fact, it usually competes more with the RTX 3060 of 2021, but even with it it loses: it offers approximately 65% of the performance from its rival NVIDIA. Good specifications. On paper, the LX-7G100 should offer more performance. It has a 7G106 GPU, 12 GB of GDDR6 memory and decent bandwidth, for example. However, it does not have truly mature support for DX12 and does not offer an alternative to Nvidia’s DSLL or AMD’s FSR. When used in modern games, performance plummets due to rendering glitches and code translation bottlenecks. Not even for AI. At Lisuan Tech they have also tried to bet on their ability to run local and private AI models. However, most of the development of AI projects is linked to Nvidia’s CUDA architecture. It is true that the Chinese company has its own compatibility layer to translate PyTorch and CUDA code to its native architecture, but the loss of efficiency is notable, which makes inference or local model training tasks become too slow compared to those allowed by Nvidia graphics. difficult to compete. Lisuan Technology announced the first milestones of this launch a year ago. The rumors they indicated that its G100 graphics processor is manufactured by SMIC with a 6nm photolithographic process that complies with US restrictions. An attempt was made to launch in 2023, but Lisuan had financial problems and a capital injection of $27.7 million managed to keep the project going. It remains to be seen if sales ultimately follow through, although certainly its price/performance ratio makes it attractive only to audiences like the Chinese, who may have more difficulties accessing models like the Nvidia RTX. In Xataka | The end of Nvidia in China seems to be very near: its current market share is 0%

China prepares a 2nm AI chip to end NVIDIA’s dominance. Your problem is how you are going to manufacture it

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A new chip designer for artificial intelligence (IA) is preparing to take the field in China. And he intends to make a lot of noise. In fact, it is already doing so. It’s called Dishan Technology, and, according to SCMP, is already verifying the prototype of a 2nm AI GPU that uses a hybrid integration technology that combines FinFET and GAA transistors (Gate-All-Around). However, this is not the only thing that has emerged. According to Dishan Technology, this chip will be 40% more energy efficient than its predecessor and will be compatible with CUDA (Compute Unified Device Architecture), from NVIDIA. This latest technology brings together the compiler and development tools used by programmers to develop their software for NVIDIA GPUs, so if Dishan’s chip is really compatible it will be much easier to integrate it into facilities that already have GPUs from this American company. Although, as I mentioned above, Dishan already has a prototype of its chip, it will take another year or two to refine its technology enough to make large-scale manufacturing possible. Be that as it may, what has not been revealed is who is going to manufacture it. SMICthe largest Chinese semiconductor producer, can currently only manufacture 7nm chips using the multiple patterning. And TSMC, Intel and Samsung, which could produce it, will hardly do so in the current geopolitical context due to the demands of the US sanctions on China. We will see how Dishan Technology solves this challenge. China already has three “champions” in its AI chip ecosystem The country led by Xi Jinping you already have three alternatives very clear to NVIDIA. Although not as well-known as Huawei or Moore Threads, Cambricon Technologies is one of the companies specialized in designing GPUs for AI with the greatest growth potential. In fact, in August 2025 it received approval from the Shanghai Stock Exchange (China) to raise $560 million. He is allocating them to the design of four chips for training and inference of AI models, and also to the development of an alternative to CUDA. Moore Threads has developed several GPUs that rival advanced solutions from NVIDIA, AMD or Huawei On the other hand, Moore Threads has developed several GPUs for AI applications that, on paper, rival some of the advanced solutions that NVIDIA, AMD or Huawei have placed on the market. The cards MTT S4000 and MTT S3000 They are its most interesting proposals right now, although, curiously, the MTT S80 card also appears in its portfolio, a proposal for games and content creation that, according to Moore Threads itself, has a computing capacity of 14.4 TFLOPS in single-precision floating point operations. The other indispensable player in the Chinese AI chip industry is Huawei. His most ambitious proposal right now is the chip Ascend 950PRwhich aims to surpass the performance of the GPU NVIDIA H100. However, this Chinese company also launched its chips last year Ascend 910D and 920. This last solution is clearly intended to compete in the Chinese market with NVIDIA’s H20 GPU. Presumably at the end of 2026 it will launch its Ascend 950DT chip, and the Ascend 960 and 970 GPUs will arrive in 2027 and 2028 respectively. Image | Generated by Xataka with Gemini More information | SCMP In Xataka | TSMC acknowledges that it has considered taking its factories out of Taiwan. It’s impossible for a good reason. In Xataka | The looming bottleneck in AI is neither RAM nor gas: it’s that TSMC’s N3 node is absolutely saturated

Samsung is NVIDIA’s best friend. AMD just got into the relationship and TSMC looks askance

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Lisa Su has been at the head of AMD since 2014. Captaining such an important ship, it is assumed that on some occasion he will have visited one of its main component suppliers. But it turns out that, in his role as CEO, he had never traveled to South Korea, home to one of the world’s leading foundries. The journey has paid off and AMD turns with latest generation memory. But the one who is happiest is the one who is going to allow AMD and NVIDIA to create their new platforms for AI. Samsung. Visiting. In Pyeongtaek, south of Seoul, is one of the main factories from Samsung. The South Korean company is expanding and has the objective of becoming one of the names of the American industry while maintaining its local muscle, and the plant inaugurated a few years ago is an example. This is how Samsung makes money: the secret is in the IPHONE As it could not be otherwise in these times, the facility is focused on the creation of memory chips to power the AI ​​hardware. SK Hynix and Micron are the two big competitors of Samsung in this field and are also opening and purchasing plants to increase their memory production. And AMD wants a piece of that pie because Samsung is, right now, the main supplier of next-generation memory. The agreement. The trip, apart from seeing the facilities, was the perfect setting to make the announcement that Samsung was will convert in the main memory supplier HBM4 from AMD. Specifically, the Instinct MI455X GPU, the next generation of the American company. Because when we talk about GPUs for AI, we talk more about NVIDIA (which also they just presented news) because they are pulling with everything (and in all sectors), but AMD is the other big one that doesn’t want to be left out of the conversation. They are achieving billion-dollar agreements with companies like Metathey have some growth forecasts stratospheric and although far from NVIDIAthey want to be in charge of providing the hardware for AI. Happy managers | Photo: Samsung HBM4. That Samsung is the one that supplies the HBM4 memory to AMD is great news for them because they are the ones that, at least for the moment, have the most refined manufacturing process for this type of memory. In the past they had already supplied the HBM3E for AMD’s current MI350X and MI355 accelerators, but the new agreement means that they will access the same type of memory as their own Samsung exclusively supplies -for now- to NVIDIA. Memory is not everything, obviously, but it plays a fundamental role. The higher and faster bandwidth, the more data per second it can handle. Think of this memory as a very wide and perfectly paved highway. And Samsung was the only one that had managed pass demanding NVIDIA tests for your new architecture Vera Rubin. Samsung at its best. And in this agreement it is evident that both parties win, but Samsung is achieving extreme recognition in recent months. Achieving the agreement first with NVIDIA and now with AMD implies that they separate from their main rivalalso South Korean SK Hynix, which is somewhat further behind with the development of its HBM4 chips. But, furthermore, the release AMD indicates that Samsung will also supply DDR5 memory to AMD’s EPYC servers and the possibility of them manufacturing some of AMD’s future chips has been discussed. Because Samsung manufactures memory, yes, but also other processors. There they have their own Exynos for the Galaxy S26but in the past they manufactured the most powerful Qualcomm Snapdragons and it has been proposed again that the South Korean company be the one make 2 nanometer chips from Qualcomm. On the other hand, they have already won a contract of more than 16,000 million with Tesla to create chips focused on AI. It is clear that TSMC is the main foundry in the world, but Samsung is determined to be one of the main hammers with which to build the future of AI. And, speaking of the king of Rome, the agreement means that Samsung manages to take over TSMC and AMD achieves a second role to reduce its dependence on the Taiwanese company. because there We already know that there is a best friendand it is undoubtedly NVIDIA. In Xataka | “It’s not a temporary squeeze, it’s a tsunami”: we are seeing live how the cheap smartphone disappears

If anyone was waiting for the AI ​​bubble to burst, NVIDIA’s results have a message: sit tight

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NVIDIA just published your results of the fourth quarter of its last fiscal year and has left Wall Street speechless. Revenues of $68.1 billion, a net profit that almost doubles that of the same period of the previous year, and a forecast for the following quarter that has far exceeded analysts’ expectations. And all this in a turbulent context where more efficient models and other alternatives are beginning to appear. The crash of DeepSeek is far away, and the demand for chips does not slow down. We tell you the numbers in detail. In case your position was not clear. Only a handful of companies in history have exceeded $100 billion in annual profit. Alphabet, Microsoft and Apple are in that club. NVIDIA has just joined them, with $120 billion in profits in the last twelve months, according to the report. The difference is speed: just three years ago, its annual profit was 4.4 billion. We can say with certainty that no technology company has ever grown so quickly on that scale. AI, and more AI. The engine that has driven these profits is its data center business, which generated $62.3 billion in the quarter, 71% more than a year ago. Within that segment, if we focus on their Blackwell chips, they have gone from entering 32.6 billion to 51.3 billion, while the networks (NVLink, Spectrum-X and InfiniBand) grow from 3,000 to 11,000 million. Gross margin is 75%, and earnings per share nearly double to $1.76 in GAAP terms (which is the official rulebook that companies follow to demonstrate transparent accounting). What Jensen Huang says. “Without computing, there is no way to generate tokens. Without tokens, there is no way to grow revenue.”, counted directly the CEO of NVIDIA in the meeting with investors. Their thesis is that in the new AI economy, computing power directly equates to revenue for their customers. That is why the large cloud service providers (Google, Amazon, Microsoft, Meta) continue increasing your capex budgetswhich together will exceed 500,000 million dollars in 2026 to build AI data centers. And NVIDIA is the main beneficiary of that expense. What DeepSeek has not broken, but accelerated. At the beginning of 2025, the emergence of the Chinese DeepSeek model generated an unprecedented tremor in the markets, leaving a simple question in our minds: if AI becomes more efficient, why do we need so many chips? The answer from NVIDIA’s results is that efficiency does not reduce infrastructure demand, it multiplies it. Every improvement in inference efficiency lowers the cost per token, encouraging more companies to deploy more AI applications, which in turn requires more compute. It’s like Jevons’ paradox, but applied to AI: efficiency expands the market instead of contracting it. Agentic AI as the next catalyst. On the same call with investors and analysts, Huang stood out that “enterprise adoption of agents is skyrocketing.” AI agentsthese systems that make decisions and execute tasks autonomously, require many more inference cycles than chatbots. They are the next step in the AI ​​value chain, and NVIDIA is once again in a privileged position. Colette Kress, CFO of the company, confirmed In addition, the first samples of Vera Rubin, the next generation of chips that will arrive later this year, have already been sent. China and the competition. Not everything is green. NVIDIA acknowledged that its forecast for the next quarter ($78 billion) does not include computing revenue in China. The company has generated just about $60 million from H20 chips since the Trump administration reapproved some sales in August 2025, according to SEC filings, and has yet to earn revenue from the most recently approved H200. Regulatory uncertainty with Beijing remains a small China in Huang’s shoe. In parallel, competitors such as AMD, Broadcom or Google’s own custom chips (TPUs) are gaining ground. But the NVIDIA CEO remains focused on his vision. And according to pointed at the meeting: “Every company depends on software, and all software will depend on AI.” As long as this is fulfilled, everything indicates that NVIDIA will continue selling the blades and picks. Cover image | NVIDIA In Xataka | NVIDIA was founded by three engineers, but only Jensen Huang remains CEO: “I wish I had kept some shares”

If you’re in a hurry to upgrade your PC, NVIDIA’s CEO has bad news: don’t be in a hurry

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Talking about artificial intelligence is talking about Jensen Huang. The CEO of NVIDIA has become the figure of an industry: that of artificial intelligence. In large part, it is your company’s products that are driving the engine of the data centers and, at the same time, enormous semiconductor industries and memory are the essential components of NVIDIA GPUs. And if Huang has been commenting for a few weeks that this 2026 it’s going to need wafers and a lot of RAMhas now asked for patience with AI. Because he has another seven or eight years of unchecked climbing left. In short. When we talk about artificial intelligence, there are two poles. On the one hand, those who see signs of a bubble that will burst in the short term. On the other hand, those who defend the billion-dollar investment against all odds. In that boat is Jensen Huang, who recently noted in CNBC that this massive spending is “necessary and appropriate” because a “once-in-a-generation infrastructure” is being shaped. The most interesting thing is that, for him, this career will continue for several years, pointing that the investment and construction of infrastructure for AI has seven or eight years left. Mortars of money. In his statements, Huang pointed out that companies like Anthropic and OpenAI are making money despite everything invested and that their current brake is not so much the budget as the limit of computing power. That is why you want your suppliers –Samsung in HBM4 memories new generation or TSMC with the processors- increase the pace. It remains to be seen, however, if the pace can be maintained over the next five years. On CNBC, the CEO of NVIDIA pointed out that, despite the astronomical amount of money, the spending is sustainable. And proof of this is that it is increasing. If in 2025 the total spending of Big Tech did not reach 400,000 million, wait that this year the number of American companies will rise to 650,000 million. Only between Amazon and Alphabet -Google-, they will invest about 385,000 million. They see the AI ​​computing race as the next “whoever wins the most,” and none are willing to lose – DA Davidson analyst Gil Luria speaking to Bloomberg Parallel career. And that, as we say, in American companies, since China is the other pole in this race for artificial intelligence. The Asian giant is the birthplace of several extremely capable models, but also something that is missing in the United States: energy to feed the enormous needs of AI. China is betting on AI, but also on robotics, and all this at the same time buy NVIDIA products and develop your own semiconductor network with the goal of achieving technological sovereignty. It is another race parallel to that of the United States, and apart from the two poles of infrastructure development, we have particular names. That so much money is being invested means that opportunities are being created, and there are companies that have gone through a bad patch and want to surf the wave. For example, a Intel that, after needing a rescue by the United Statesis positioning itself as one of the great foundries in the United States. In addition, they are putting their foot in a segment that they had not explored, that of DRAM memory, and They are doing it with the Japanese giant SoftBank. Japan has not had a say in the memory industry since the 80s, when South Korea snatched their positionand now they may have another chance. Translation for the user. These are a couple of examples of companies that are taking advantage of the conditions to obtain financing and expand, seeking to position themselves in what they have determined is the future of the technology industry. With that amount of money and investment, there is a question you may be asking yourself: will I be able to buy a PC? The answer It is not hopeful. Giants like Micron -one of the heavyweights in the RAM segment- They are investing a lot to expand facilities and be more capable when creating memories, but they will not be for us: they will be for data centers. If the end of 2026 or 2027 was targeted as the end of the component crisis like the RAM or SSD (which are still components with memory modules), now it is Lip-Bu TanCEO of Intel, who states that It won’t be until 2028at the earliest, when we can see a horizon in the current panorama. So, yes, the entire tech industry has turned to AI and those that can increase their production of key components will do so over the next few years. The issue is that they are going to focus on components that users neither care about nor care about, neglecting those that we really need on a day-to-day basis. AND an example is NVIDIA itself. Image | NVIDIA In Xataka | Apple has been the industry’s first customer for decades. AI is relegating it to the background

Intel refuses to be left out of the AI ​​race. Your next move points directly to NVIDIA’s territory

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The AI ​​fever is not only redefining software, it is also turning the map of power in the chip industry upside down. On this new board, the GPU has become the essential engine for building models and scaling data centers, to the point that demand has skyrocketed and placed its main manufacturers in a dominant position. For Intel, the diagnosis is difficult but evident: if the next decade of computing is decided in this area, it is not enough to protect the kingdom of the CPU. Intel’s move. The Santa Clara company has chosen a very specific setting to begin organizing its speech. During an AI Summit organized by Cisco, the company’s CEO, Lip-Bu Tan, said that Intel will start to produce GPUs and has just hired the “chief GPU architect” who will lead that effort. The manager avoided giving details about the name, but he did leave a message consistent with the moment in the sector: the GPU matters and will continue to matter. The missing piece. According to Reutersthe talent hired by Intel is Eric Demers, from Qualcomm. On the other hand, the initiative would fall under the umbrella of Kevork Kechichian, executive vice president and head of Intel’s data center business, incorporated in September within the framework of a series of hires aimed at strengthening the company’s technical profile. AI, before gaming. The nuance is important, because talking about GPU can automatically activate the imagination of graphics cards for gaming, but reality goes in another direction. Intel already has a presence in graphics on the PC, with its Arc productsbut the announcement targets GPUs for AI and data centers. The initiative as a still early plan, with a strategy that will be developed based on customer demand, a coherent approach with an AI infrastructure market where the most intense battle is being fought today. Intel’s corporate moment. According to CNBCthe stock market value has risen in the last year in the heat of optimism about your business foundrybut the company is still mainly dedicated to manufacturing chips for its own catalog. It’s no secret that Intel has lost ground to companies driven by the AI ​​data center wave, and is now taking steps to respond. No relief until 2028. In the same forum, Tan slipped in another element that helps dimension the challenge of AI infrastructure. He spoke of the memory chip shortage which is disrupting the market due to the mismatch between supply and demand, driven by the construction of AI-oriented data centers. That environment is giving manufacturers room to continue raising prices, and Tan was blunt in describing AI as the “biggest challenge” to memory. He also released an estimate that leaves little room for optimism: he stated that he does not expect “no relief until 2028.” Images | Brecht Corbeel In Xataka | Goodbye to the duopoly of Intel and AMD in Windows: the arrival of NVIDIA processors is imminent and brings 8 laptops under its arm

That the US authorizes Nvidia’s H200 to reach China is not a concession, but a plan. They prefer money to competition

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The chip war between China and the US has mutated from a blockade to a commercial transaction. Donald Trump has announced that he will allow Nvidia export its high-performance H200 chips to China. The authorization carries an unprecedented condition: the US government will receive a 25% commission about these sales. This “reverse tariff” transforms China containment into a source of income, breaking with the strategy of total suffocation and offering a lifeline to Nvidia in its most critical market. End of free blocking. The decision is a direct result of a meeting last week between Trump and Jensen Huang, CEO of Nvidia. The White House’s logic has changed: it argues that this measure is carried out under strict national security conditions, extending the model to competitors such as Intel and AMD. It is a movement that formalizes what was already intuited a few months ago, when Nvidia managed, after a first meeting with Trump, lift veto on bottom H20 chip. At that time, a precedent was already established of transferring 15% of income to the country, a figure that now scales to 25% for the most powerful hardware. Tap on the image to go to the original post A dose for China. That they chose this chip is no coincidence: the H200 is significantly more powerful than the H20—the trimmed model that China had started to boycott— but it is still behind the cutting-edge Blackwell architecturewhich is still banned. According to advisors such as David Sacks, the North American country seeks to keep China addicted to its technology: if they are denied all access, they are forced to look for alternatives of their own. In fact, Huawei has already admitted that it will take two years to match the performance of the H200, making this chip the perfect tool to slow down Chinese development while monetizing its need. Cracks and black market. The reality is that the total blockade was failing. Recent investigations showed how Chinese companies used shortcuts through Indonesia to access the power of banned chips. Furthermore, the second-hand market had become the main avenue for China get H100 and A100 GPUs off the radar. By allowing the sale of the H200, the US is trying to regain control over a flow that already existed, but in the shadows. At the same time, the Department of Justice announced “Operation Gatekeeper” to dismantle smuggling networks in countries like Hong Kong. China’s response. The great unknown is precisely this, the reception of the news in Beijing. Although Trump claims that Xi responded “positively,” the reality on the ground seems different. China has been for months banning your local businesses buy Nvidia chips to promote its domestic industry. The CAC (Cyberspace Administration of China) came to investigate the H20 looking for rear doorssomething that generated a climate of mistrust that not even the previous July agreement managed to completely dissipate. Jensen Huang, who warned about the danger of an “AI silk road” If the US continued to block sales, with this pact it gets a golden opportunity to not lose a market that represents 13% of its income, although its Chinese clients must now pay the price of American geopolitics. Cover image | Composition with images from Nvidia and RawPixel In Xataka | China has just redrawn the map of strategic minerals: its new rules on rare earths target the United States

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