NVIDIA

Nvidia has just launched its missile against Intel AMD’s dominance in PCs and laptops. There is a problem: it is a slightly obsolete missile

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In October 2025 Nvidia launched its DGX Sparka unique workstation that the company called “the world’s smallest AI supercomputer.” that machine It was actually announced in January.but it took a while to reach the market. When it finally did, it became an interesting alternative but somewhat limited in scope. That is just what the new Nvidia RTX Spark family, which will arrive, wants to change both in the form of laptops as desktop computers, and that it will do so with a fundamental difference: Windows for ARM. Hello, Windows for ARM. The golden DGX Spark were Linux-based workstations, which targeted them at a smaller audience, but with the RTX Spark, Nvidia wanted to make the big leap to the general public. These devices are based on Windows 11 for ARM, and will take advantage of all hardware and software capabilities so that this technological solution is no longer only aimed at AI enthusiasts. Of course, that will continue to be one of the segments it will target, but these systems can also be used for both creative and gaming environments. In Xataka We wanted an ideal PC to be able to experiment with local AI models. The Framework Desktop is the answer to our prayers Approximate performance: an RTX 5070. Those responsible for NVIDIA have not yet given too many specific details about what we can expect from this platform in terms of performance, but they have indicated that the performance of the GPU It is close to that of an RTX 5070 (portable version), although the exact numbers depend on the specific application or game: in some it will be a little better, in others a little worse and in others exactly the same. Yes, they have indicated that the promise is to obtain 100 FPS in 1440p gaming as reference data. Same chip, different operating system. Hardware technical specifications They are identical to those of the DGX Spark. The main data are the following: NVIDIA Grace Blackwell Architecture CPU: up to 20 Grace cores GPU: Developed in collaboration with MediaTek, up to 6,144 CUDA cores, 1 PFLOP of AI performance Unified memory: up to 128 GB LPDDR5X at 273 GB/s with NVLink at 600 GB/s But compared to the DGX Spark, we insist, the fundamental difference is that instead of using a specific Nvidia Linux distribution for these machines, here we can take advantage of Windows 11 for ARM. When AI controls your computer. During the presentation of this platform, those responsible for Nvidia talked about the absolute rise of AI agents and how this will mark a paradigm shift in the way we use our PCs and laptops. Before we did it with a mouse and keyboard, but they see a near future in which control is taken by those AI agents, with whom we will interact in a quite different way. The example is the already famous OpenClaw and Hermeswhich with the appropriate permissions can run all kinds of tasks and applications on the computer to autonomously do things for us. Six laptops initially. The Nvidia RTX Spark platform will initially be available in six devices from six different manufacturers that will rely on this technological solution from launch. ASUS, Dell, HP, Lenovo, Microsoft and MSI will have their equipment ready this fall, although at the moment there are no specific specifications or prices announced. It is possible that during Computex we will learn more details about these devices. What can we expect in autonomy. At the moment no specific data has been given about the efficiency of these devices, but Nvidia spoke of a battery life “for the whole day.” They highlight the efficiency of the GPU and in fact indicate that GPU performance will be virtually the same whether the laptop is plugged in or not. Obviously in intensive tasks and demanding games that battery will drain much more quickly. 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 The doubt of Windows for ARM. The commitment to Microsoft’s operating system is striking, but Nvidia believes that now the system is much more mature, and that both emulation and hardware support It’s much better than in the past thanks to the work that Microsoft and Nvidia have done in the months and years leading up to this launch. They talk about a “first-class experience” for the operating system, and even commented that they have worked with the developers of anti-cheat systems in video games so that this is not a problem on these computers. And also desktop computers. When Nvidia announced its DGX Spark, then similar desktop computers appeared in format that also offered that same platform. The same thing will happen with RTX Spark, and although there was hardly any data here, Nvidia did indicate that these devices will appear in the fall from Acer, Asus, Dell, Gigabyte, HP, MSI and Lenovo. {“videoId”:”x7ztphf”,”autoplay”:false,”title”:”How to know the components of your PC (RAM, Graphics, CPU…) and the state they are in”, “tag”:”webedia-prod”, “duration”:”387″} Many unknowns and certain obsolescence. There are many doubts surrounding these devices in terms of performance or price, but there is another fundamental problem: when these laptops and desktop PCs appear starting in the fall, they will do so with chips that have been on the market for a year and therefore in a certain sense are already somewhat obsolete. Competing with the Desktop Framework. The memory bandwidth is not exceptional, and for example the Framework Desktop presented in August 2025 already offered a similar configuration in that section, with up to 128 GB of LPDDR5x memory at 256 GB/s. It will be interesting to see how the RTX Spark machines perform against alternatives like this (which makes use of a “traditional” x86 Windows 11 operating system) and whether Nvidia’s ARM chip can really make a difference in an ultra-competitive market. In Xataka |The demand for AI memories is suffocating mobile manufacturers. The largest … Read more

China can’t buy the best Nvidia chips. So Alibaba has decided to connect theirs and sell them as if they were one

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Alibaba does not want its infrastructure artificial intelligence (AI) continues to depend on Nvidia technologies. Little by little, the largest technology companies in China are assuming the request that Xi Jinping’s government made them at the beginning of October 2024: as far as possible They had to use chips produced in China. Ten months later this recommendation became a requirement. And the data centers that belong to the State throughout the country had to use at least 50% Chinese integrated circuits on their servers. This scenario especially favors Huawei, Moore Threads and Cambricon Technologies because they are Top AI GPU Manufacturers from China, but it also works great for Alibaba. In fact, Alibaba Cloud, its cloud computing subsidiary, has taken a very important step forward. A few days ago it presented a new chip for AI, the Zhenwu M890, and made official a very ambitious itinerary that describes what solutions it will develop over the next three years. This GPU has been designed by T-Head, the semiconductor division that Alibaba founded in 2018. It incorporates 144 GB of HBM3 memory and achieves an interconnection transfer speed between chips of up to 800 GB/s. As we are about to discover, this last feature is essential in the strategy that Alibaba has developed to compete in the AI ​​hardware market. Alibaba is going to spend $53 billion on its infrastructure According to Alibaba, the performance of its Zhenwu M890 chip is triple that of its predecessor. Additionally, it has been designed to perform well both during training of cutting-edge AI models and during inference. An important note: inference is broadly the computational process carried out by language models with the purpose of generating responses that correspond to the requests they receive. Alibaba wants to compete face to face with Nvidia in the deployment of infrastructure for data centers However, there is another relevant fact that is worth not overlooking: in medium precision operations (FP16) the Zhenwu M890 chip reaches 0.6 petaflops, a performance comparable to that of Nvidia’s A100 GPU and three times higher than that of the H20 chip. On the other hand, the ICN Switch interconnection chip allows link up to 128 GPUs M890 so that they work in unison. Alibaba assures that this architecture makes these GPUs work as a single chip, which, on paper, will allow it to compete head-to-head with Nvidia in the deployment of infrastructure for data centers. Regarding the itinerary that will follow until 2028, this Chinese company has anticipated that it plans to launch the Zhenwu V900 during the third quarter of 2027. According to Alibaba, it will implement its own significantly improved parallel computing architecture, will have three times the performance of the M890 chip, will be supported by 216 GB of memory and will reach an interconnection transfer speed of 1,200 GB/s. The Zhenwu J900 will arrive during the third quarter of 2028 with another major architectural leap. This roadmap It reflects that Alibaba goes all out. In fact, it has also announced that it will support this plan with an investment in 380 billion yuan (about $53 billion) over the next three years. Is the largest engagement of its kind in history of the company. Additionally, T-Head is planning its IPO to fund a more aggressive infrastructure investment program, which would put it in direct competition with Cambricon Technologies and Huawei’s Ascend line in the domestic AI chip market. Image | Alibaba More information | Alibaba | ChinaDaily In Xataka | Nvidia has to deal with the absolute distrust of several US legislators. Your plan in China is in danger In Xataka | The US wants to end Chinese AI chips sold abroad. And China knows how to defend itself

The United States promised to be very happy manufacturing its own chips. Nvidia just spent 150 billion in Taiwan

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Houston, we have a problem. A couple of days ago the CEO of Nvidia stood on the stage at Computex in Taipei and said an inconvenient truth for the United States: “Taiwan is the epicenter of the AI ​​revolution. This is where chips and packaging are made. This is where systems are created. This is where AI supercomputers were created.” The setting was Computex 2026, Asia’s biggest tech event, and it wasn’t a compliment to the host, it’s a real depiction of how the industry works. It may sound paradoxical for an American company and at a time when The United States wants to reindustrialize with chipsbut he needs it. It is a structural issue. The harsh reality of profitability. Nvidia plans spend 150,000 million dollars a year in Taiwan, much more than the 100,000 million they spend now and with an abysmal difference compared to the 10,000 and 15,000 million five years ago. If it sounds silly, it’s because it is, but so is its billing: in the first fiscal quarter of 2026 billed 81.6 billion dollars, 85% more than the previous year in that same period. Also its benefit it’s already going off the charts: 58.3 billion, more than triple compared to the same period last year. That this money goes to Taiwan and not to the United States is due to technical and objective issues: Taiwan produces 90% of the most advanced chips in the world, according to a study by the Stimson Center. Of that Taiwanese production, TSMC controls 70% and is going to invest between 52,000 and 56,000 million this year. Bottom line: If Nvidia wants cutting-edge manufacturing capability, it has to be there. Why is it important. The best way to see it is to put Vera Rubin on the table, who In Huang’s words it is “probably the biggest product launch in Taiwan’s history.” Each system contains about two million parts and is assembled with 150 suppliers, almost all Taiwanese. This mechanism is not assembled by decree or in a legislature: it requires years and putting billions of dollars on the table. There is no factory in Arizona that can do something like this at least until 2030. Constellation will be Nvidia’s new headquarters in Taipei and will come to stay permanently: 4,000 engineering professionals will work in that center that according to Huang It will be operational by 2030. It is no longer that it buys in Taiwan, it is that the most valuable semiconductor company in the world is building the heart of its R&D in that core, an island 10,000 kilometers from the United States. A splash of cold water on Trump’s aspirations. Context. In January 2026, Taiwanese companies they committed to invest $250 billion in semiconductors and AI in the United States, as part of a trade agreement with Washington. Because Taiwan and the US are a symbiosis: each needs the other to maintain its position in the race for AI. The investment of a private company like Nvidia is another expression of this pact. In fact, Nvidia is not the only one: AMD is doing exactly the same: associate with Taiwanese manufacturers such as ASE, SPIL and Wiwynn with their Helios AI platform on the horizon (expected for the second half of 2026). That the two largest AI chip designers in the world strengthen ties with Taiwan is confirmation that the island’s industry is strategically necessary for the entire industry, not a particular bet by one firm. The elephant in the room: China. China’s role in this story is twofold: it is a threat and also a client. According to Reutersin 2026 Chinese companies have placed orders for more than two million units of the H200. Trade restrictions have made the operation difficult, but they have not been able to prevent it. One of the last cases point upon the arrival of a shipment of Nvidia AI chips to China via Japan. Nvidia lives in a contradiction from which it cannot escape: Its supply chain is on an island that China considers its own. China, which is its largest potential market, is blocked. Washington prohibits him from selling to Beijing while asking for independence from Taipei. And judging by his statements, Jensen Huang has bet everything on continuing to walk that wire. Yes, but. The Nvidia CEO forgot one problem in his speech: Taiwan makes the overwhelming majority of the world’s most advanced chips, but TSMC’s diversification into Arizona, Japan and Germany will not be ready before 2028 at best. That is to say, there are almost four years ahead in which Nvidia depends totally on Taiwan, a country that matters 97% of your energy. Furthermore, the atmosphere in the Strait of the same name is increasingly heated. Concentrating the production of its most critical component in a geographically hot spot is dangerous to say the least: if something explodes, there is no plan B. The closure of the Strait of Hormuz has reminded us of this the hard way. In Xataka | Huawei has found a way to counteract US sanctions: overcoming Moore’s Law In Xataka | US companies have always had a hard time making a lot of money in China. One industry is the exception: chips Cover | freepik and Jimmy Liao

DeepSeek is good, pretty and very cheap. And above all, the weapon to create a Chinese hardware industry independent of Nvidia

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The arrival of DeepSeek-V4-Pro It hasn’t caused that much of a stir. like the one caused by DeepSeek R1 a year and a half ago, but we may be facing an even more important model. If that version revealed to the world that China was advancing spectacularly in this race, this other one is beginning to allow us to glimpse something else more interesting. What most people see is a very decent model and above all “low priced”. Which hide the company It’s another more important thing: achieve independence from Nvidia and US hardware. what has happened. Last Friday, those responsible for DeepSeek announced something surprising: their promotional offer with a 75% price cut to use their DeepSeek-V4-Pro model will be maintained permanently. That makes this model offer very decent features (but not exceptional) for a really low price: 1M entry tokens 1M tokens output DeepSeek-V4-Pro 0.435 0.87 GPT-5.5 5 30 Opus 4.7 5 25 Gemini 3.5 Flash 1.5 9 Good, pretty and very cheap. It is true that the performance of DeepSeek-V4-Pro is inferior to that of rival models from OpenAI, Anthropic or Google. Artificial Analysis tests indicate that the DeepSeek model is at a very good level, but it is also much cheaper than its competitors. This is especially relevant for agentic tasks that consume many tokens and that with this model become accessible and very affordable. According to Artificial Analysis, DeepSeek is close to the performance of the best models in the industry, and although it is slower in its responses, it is also much cheaper than the frontier models from OpenAI, Anthropic or Google. A different strategy. How is this company going to make money? It does not have subscription plans like its local competition (GLM, Kimi) or the western one (ChatGPT Plus, Claude Pro). It also does not have voice or image models. It does not have an AI agent for programming that competes with Claude Code. It publishes the open weights of its models and shares its technical innovations with the industry (and with its competitors). For those who closely follow the company and these decisions, the strategy is clear. DeepSeek’s goal is not to win the AI ​​model race. Their goal is to build a Chinese AI hardware industry that doesn’t depend on Nvidia or TSMC… and get paid their share in that process. Hardware independence. China has a structural problem in this AI race: sanctions and vetoes imposed by the US make you unable to access the most advanced chips nor to ASML UVE photolithography. And since China cannot currently compete in terms of computing power, what its companies are doing is ensuring that their AI models need less computing power to achieve similar results. Efficient architectures. The Mixture of Experts (MoE) and Multi-head Latent Attention (MLA) architectures are two key weapons in this strategy. The first already existed but was adapted by DeepSeek for their model: with it only part of the total parameters of the model are activated to answer the query without losing precision. What MLA does is compress the attention information (the so-called KV Cache) with which the model maintains the context of a conversation, reducing it by 90%. Both techniques allow us to reduce the need to use high-speed HBM memories, something that is also striking in order to reveal DeepSeek’s probable strategy. The importance of KV Cache. As the GDP analyst explains in Xthat use of MLA allows that for one million tokens, DeepSeek-V4-Pro only needs 5.48 GB of HBM memory. Competitors like Zhipo AI, which develops GLM 5, need 60 GB for the same, while Alibaba’s Qwen 3 needs 89 GB. This advantage allows DeepSeek to offer much lower prices to obtain performances similar to those of its competition, but it also means that DeepSeek models can run on Chinese memory chips that cannot compete in speed with HBM modules. Goodbye HBM, hello NAND and SSD. These innovations open the door to the use of NAND memories and even SSD drives to process this data, and there YMTC enters the scenea Chinese Flash memory manufacturer that is slowly becoming a global giant. Also CXMTwhich manufactures DRAM memoriesbecomes an alternative here and the reason is equally interesting: DeepSeek introduced a memory search module in LLMs called Engram which is also intended to avoid excessive dependence on HBM memories. How to bypass the CUDA monopoly. Nvidia continues to have a fundamental element in CUDA to maintain its market dominance, but here DeepSeek too has proposed an alternative. Is called Tile Kernels and these are software cores created with TileLang (a variant of Python for this field) that allow governing advanced AI chips (GPUs). Huawei as an invisible ally. Those responsible for Huawei recently indicated that its new Ascend AI supernodes fully support DeepSeek v4 models. Precisely this provides another fundamental advantage to the company, which thus avoids (at least in part) total dependence on the use of Nvidia chips and prepares to further strengthen Huawei’s relevance in a market in which until recently Jensen Huang’s company was queen and mistress. Open models to attract the hardware industry. US companies continue to maintain their closed and proprietary models, but DeepSeek is one of the many Chinese startups that publish them with open weights. With this, what she and the others intend to do is not only attract AI developers and users, but also create a hardware ecosystem that adopts these architectures. DeepSeek invites its rivals to use techniques such as MoE or MLA precisely so that all these advances become a de facto standard and hardware manufacturers also adopt them and integrate them in an optimized way into their designs. A round of 10,000 million to advance. The company is also preparing a financing round in which they intend to raise 10,000 million dollars and with which they would achieve a valuation of between 45,000 and 50,000 million dollars. Still far from the mammoth valuations of OpenAI or Anthropic (already close to a billion dollars) but certainly … Read more

There was a time when Nvidia was a gaming company. That business is now pocket change for the owner and lady of AI

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In 1993, Nvidia was founded with the goal of creating graphics chips for video games. For almost three decades Nvidia has been basically that: a semiconductor company for gaming that yes, I had ambition in the field of professional computing. But things change: Nvidia’s gaming business has generated $6.4 billion the first fiscal quarter of 2027and although it is a healthy business, for Nvidia it is something else: It’s almost pocket change. Gaming no longer (almost) matters. In any other company in the sector, this income (29% more than last year) would already be extraordinary, but at Nvidia they are almost a footnote, because gaming represents less than 8% of the company’s total income. The other $75.2 billion came from the data center business, which grew 92% from the previous year. AI has made Nvidia’s original business almost irrelevant in relative terms. Stratospheric numbers. Nvidia has earned $81.6 billion in the first fiscal quarter of 2027. It is an absolutely colossal figure that should be put into perspective: it is so large like GDP from Croatia, Panama or Uruguay. The company led by Jensen Huang has managed to grow 85% in revenue since a year ago, almost double. The surprising thing is that it has also done so when it seems increasingly difficult to grow at this rate. The graph shows year-over-year growth in revenue in percentage. In 2026 the trend is bullish again. Source: FT. This is non-stop. The company exceeded Wall Street expectations, which projected revenues of 78.86 billion, but Nvidia also states that its forecast for the next quarter is to earn 91 billion dollars, 12% more than the current one. It’s true that growth is slowing in percentage terms, which is normal at this point, but in absolute terms the company continues to add billions of dollars of additional revenue each quarter. Data center numbers. Those $75.2 billion in data center business aren’t just GPU sales for hyperscalers. It also includes the company’s networking solutions business, which has grown no less than 199% year-on-year to $14.4 billion: it has tripled. The reason is logical: the demand for interconnection infrastructure for the large clusters that are being created everywhere is enormous, and Nvidia provides an ideal solution for those who buy its AI chips. Beware I, Anthropic is coming. On the call with investors, Jensen Huang gave a singular fact: Anthropic has made virtually no use of Nvidia solutions to train and serve its AI models, but that is going to change. The company’s CEO highlighted that the computing capacity they are going to deploy for Anthropic this year and next is going to be “quite significant.” Or what is the same: they are going to continue selling like hotcakes even if the competition tightens. Nvidia is also an investor in startups. Nvidia’s strategy is also being curious on a financial level, because it is not content with growing its business, it is betting on AI startups. It has invested more than 26,000 million in investments in this type of companies, and that does not include the recent agreements with OpenAI or in listed companies like corning. Beware II, China is coming. All these numbers, attention, are being achieved without the help of the Asian giant. In December, the Trump administration authorized Nvidia chip exports to China (with a 25% government fee). Theoretically that should make Nvidia generate notable income thanks to said authorization. Huang explained that at the moment these revenues are zero and that there is some uncertainty about whether China will finally allow its chips to be imported. In the second fiscal quarter of 2027, income from China is not assumed, but if that market finally opens, we will have even more extraordinary numbers. Buying back shares. Nvidia has returned about $20 billion to shareholders this quarter between buybacks and dividends. The board of directors has approved investing $80 billion more in share buybacks, thus multiplying by four what had previously been authorized. That’s a clear sign of Nvidia’s confidence in its future, which will also benefit shareholders: the dividend has passed from $0.01 per share to $0.25 per share. Previously, Nvidia offered specific data on gaming revenue. From now on, stop doing so to put that division within the Edge Computing category. Gaming no longer appears in the accounts. Typically Nvidia’s financial reports divided revenue into data centers, networking, gaming, professional visualization, automotive, and a few other fields. Now that Nvidia is a fully AI-focused company, it has changed its revenue pooling structure. Everything related to gaming, PCs, consoles, workstations, robots, cars and other devices is part of the “Edge Computing” category. Gaming, we insist, no longer (almost) matters. In Xataka | For the first time in 30 years, Nvidia will not present new GPUs for gamers in 2026. They earn much more with AI

Chinese Big Tech can now buy Nvidia GPUs. The problem for Nvidia is that they don’t need it now

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The United States and China are immersed in a trade and technological war that has caught the line of fire to the AI ​​giant: Nvidia. The situation is that Nvidia must prioritize AI companies from the United States to guarantee the supremacy of this country, but as a company it would be interested in taking a bite out of the giant Chinese market. And the problem is twofold: it has not been able to do so for a long time due to trade vetoes, but now that it seems that it can sell its famous H200 to China, it turns out that China has turned the page. More or less. green light. Nvidia has gone from having a monopoly on AI GPUs in China to have a 0% quota. These are the words of the CEO, Jensen Huang, and the reason is the aforementioned trade restrictions between the powers that prevented Nvidia from selling its most powerful products to the Asian giant. Huang has spent months insisting on Donald Trump’s government to allow them to sell with a very clear logic: China is going to develop its alternatives and what better way to make a profit until then. The situation is gone relaxing at the end of last year and at the beginning of this to get to the point where we are now. According to Reutersthe US Department of Commerce already allows ten Chinese companies and distributors such as Foxconn and Lenovo acquire that long-awaited H200the company’s second most powerful AI chip. Good news for the company. Or they should be if it weren’t for the fact that the Chinese industry is going its own way looking home. Alibaba, ByteDance, JD.com and Tencent are the Chinese giants that can supposedly already buy H200. Up to 75,000 chips each, to be exact. However, it is noted that they have not yet made any shipments. Here there is a mix between very restrictive bureaucracy and, above all, that emphasis on national development. Tencent, for example, noted in September last year that they had no intention of producing AI chips, but that they were going to invest a lot of money in domestic partners. For example, they are in the process of adapting their infrastructure to be able to connect Huawei’s Ascend platform (particularly the Ascend 950 series) as the main training tool for large models. A few days ago, Tencent’s strategy director already pointed out that that strategy was still in place and that the company expects a significant increase in spending on AI GPUs designed in China. Manufacturing at home. Alibaba and Bytedance have a different approach. If Tencent is focusing on acquiring Huawei platforms, Alibaba and Bytedance are looking to create their own chips. Alibaba seeks to be the most powerful RISC-V chip created to date and it was reported that Bytedance wanted Samsung will manufacture its processor. In the end, whether buying from Huawei or developing the tool internally, the two approaches respond to the great national objective: that at least 50% of the data centers that belong to the State use at least 50% Chinese integrated circuits in their servers. That is one of the great Chinese technological impulses of recent years, one of the crucial points of the Five-Year Plan for the development of the country and, above all, the strategy that Nvidia had been warning the United States about for some time. The age of inference. Because this period of ostracism to which the US condemned China has served for the country to develop three very clear alternatives to Nvidia and encourage companies that are already working with models to develop their own hardware. This is important especially in the new AI framework we are entering, that of inference. Although the AI ​​will continue to train and GPUs will be needed for this, the next step is inference, the agentic era in which the processor or CPU is very important. AMD is moving there, same as Intel or ARMand precisely processors are something that Huawei is good at and in which the Chinese giants can shine as much as their American counterpart by developing chips tailored to their models and needs. Also, as pointed out in CNBChaving your own chips means you don’t have to fight with anyone else in a time when there is scarcity and, of course, if you don’t have to buy from an outsider, there is an improvement in the gross income margin. juicy cake. And this leaves Nvidia in that uncomfortable situation, one in which it wants to participate, but in which it seems that it is no longer needed as much as before. Because China is developing its chips for this new era of AI and Nvidia is running into a final boss called bureaucracy and the pressure groups of the ‘Make America Great Again‘. The first is due to the slowness of the export order processes, something that takes months when orders should be much more agile. The second are the aforementioned pressure groups that hold that any deals Nvidia makes with Chinese companies are less chips for American companies, something that should not be allowed. Meanwhile, Chinese companies are developing their alternatives and Huawei wants to flood the market with 750,000 chips this year, three times more than its shipments in 2025, and Nvidia is falling short of a $50 billion pie. In Xataka | The US has the best AI models. China has something else: AI too cheap to care about

For the first time in 30 years, Nvidia will not present new GPUs for gamers in 2026. They earn much more with AI

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In 1995 Nvidia presented its NV1 chipsits first multimedia card and the one that would start its particular revolution in the world of gaming. Since then, every year the company has presented a new model intended for this segment. In 2026 that tradition will be broken. what has happened. What has happened is AI. The rise of this industry has been of such magnitude that it has had a critical impact on the technological field and, little by little, on the social field. Nvidia is at the center of this particular revolution, because the company bet early on the ability of its GPUs to be used as AI chips and that bet has been rewarded. Gamers in the background. Such has been the explosion in this field, that Nvidia has decided that what is important is no longer gamers, but AI chips for data centers. From a financial and business point of view, the logic is overwhelming: the profit margin of AI chips round 75%, especially thanks to price control that allows the company to set prices to its liking thanks to the fact that it currently has almost no competition. Data centers win by a landslide. There is another element that favors it: volume. Not only is the price per unit higher, it is that the volume handled in data centers is much higher than that of gaming GPUs. Analysis like that of App Economy show how the market started timid, but in the second fiscal quarter of 2024 revenues began to skyrocket and the data center fever has made Nvidia the company with the largest market capitalization in the world. No GPUs for gaming in 2026. After the launch of the RTX 5000 in January 2025, this year Nvidia was expected to announce the “SUPER” versions of said family. These models they were going to tell with denser GDDR7 memory modules, which would allow the memory configuration of the original models to be increased. The memory crisis and the total focus on the catalog of GPUs for AI has meant that Nvidia has not announced them, and for the first time in more than 30 years there will be no renewal of the gamer catalog for this year. And the RTX 6000 even further. If the news is already bad for the SUPER versions of the RTX 5000, things are even more terrible for the theoretical RTX 6000, which will have Rubin architecture and from which a notable jump in performance is expected. According to the latest datathese graphics cards will not begin to be manufactured until the end of 2027, which would mean that they would not arrive until 2028. The current situation suggests that it is likely that they will not even arrive that year. Do we really need more powerful GPUs? On Reddit a user did an important comment when it became known that NVIDIA would probably not release new graphics for gamers. “On the one hand it makes me angry. On the other hand I realize that I am playing ‘Rimworld‘ and ‘Terraria‘”. It refers to very popular games that can be played even with integrated GPUs such as those used by many Intel or AMD processors. Others they responded that Nvidia GPUs are so powerful that they are actually often necessary because game developers don’t really squeeze the hardware. Be that as it may, it seems that the current generation is usually more than prepared for the most demanding titles, and the urgency for a new generation is perhaps not so pressing. The April 2026 Steam survey makes it clear that the next-generation RTX 5000 coexists with a market in which the RTX 4000 and RTX 3000 remain very popular. Source: Steam. The data confirms it. If you go through the April 2026 Steam Survey you see how more than a year after its presentation, the RTX 5000 has almost 24% market share, while the RTX 4000 has 35% and the RTX 3000 has 16%. The rest of the users opt for previous solutions or from rivals like AMD, which is still far away in this battle. Many users have already invested in their RTX 3000 and 4000, and it seems unlikely that they will do so again for a new GPU, especially when in the recent times The prices of these cards have skyrocketed. There is nowhere to run. There is another problem with this Nvidia strategy of turning gamers into second-class users: there are not many alternatives, at least if we want maximum performance. AMD continues to fight in this market, but its graphics still fail to capture the interest of many users. Intel has done interesting releases recent years, but not in the high range in which Nvidia is a de facto monopoly. Your efforts They are not achieving great success either.and the company is not focusing on it either because it knows that now the money is somewhere else. In Xataka | If at some point NVIDIA has to choose between giving its best chips to the US or China, its choice is very clear.

Nvidia and Samsung are the names of AI. Quietly, someone is eating up the server processor market

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Artificial intelligence is about to enter a new era. After plunder the internet and drink all human knowledgetraining is no longer the obsession of the big AI companies and the inference is about to take the baton. That inference will reach its climax with the explosion of AI agents and that implies a change in balance: GPUs will continue to be key, but CPUs will take on a greater role. Inference requires other types of resources other than training and that is why Nvidia is preparing with its platform Vera Rubinbut also the rest of the industry. Intel has already said that is moving its production lines towards the Xeon, the server processors, while ARM It is seeing green numbers because a few months ago it presented a powerful processor for AI. The one that is also seeing the ‘stonks’ grow is AMD. Although its name sounds less than that of Nvidia, AMD is very present in the AI ​​race. It has secured the best memory for its new platform, it has a GPU for training and also the processors EPYC for servers. These are precisely the ones that are giving you joy. AMD EPYCo record According to analysts Mercury Researchboth ARM and AMD have had a spectacular quarter. Both have continued to eat market share from Intel (which is why it seeks to respond) and, in the case of AMD, in the first quarter of this year they have reached a record 46.2% revenue share in x86 CPUs for servers and 30% of the CPU segment. Here are two numbers to keep in mind. The first is that AMD was already coming from a fairly comfortable position, with a 41.3% revenue share in servers in the last quarter of 2025. Thus, it seems that this growth to 46.2% is not too big, but the second number that must be taken into account is the one that allows us to see the company’s leap in this segment. It is estimated that the company It had only between 1% and 2% CPU share for servers in 2018. Since then, AMD has been doing things very well both in consumer computers with their Ryzen as in servers with its EPYC, which has allowed it to eat Intel’s share by leaps and bounds. And just as important as the quota are the company’s results, not because they interest us in terms of money, but because it gives us an idea (just like what is happening with Nvidia, SamsungSK Hynix or Micron) of how far we are from being able to see competitive prices again in the consumer market. Because it is estimated that this part of the business focused on data centers left 5.8 billion dollars in AMD, an increase of 57% year-on-year. It surpasses Intel (5.1 billion), being the first time this has happened in the data center sector and, in addition, AMD projects a growth of more than 70% year-on-year in the data center segment. In this particular battle, we have already commented that Intel is not sitting idle and has new processors for data centers, a great projection being the great american foundry and we will have to wait to see the efforts to reconvert their production lines to return to the Xeon. What is evident, according to estimates, is that the server processor market is experiencing an impressive increase due to this new generation of AI and is wait that goes beyond 30,000 million in 2025 up to 170 billion dollars by 2030. Landing this for us, the users, this implies one thing: if it was already expensive to build a PC due to RAM and SSDnow other components such as processors or motherboardswho are also reorienting the business. In Xataka | The US confesses its worst nightmare: if China invades Taiwan and controls TSMC, the US economy will collapse

China and Nvidia star in the “great technological divorce” of 2026. A bureaucratic hell that is erasing it from the market

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Talking about Nvidia is talking about artificial intelligence glue. The GPU giant has invested millions financing cocompanies like OpenAI or Anthropicbut along the way has not forgotten startups or to make purchases for strengthen your position in the market. The problem is that it is missing out on a potential $50 billion market: China. Because Nvidia is eager to enter China, but it is trapped between bureaucracy, the Trump Government, Xi Jinping’s Government, and the smuggling of its graphics cards. The great divorce. In a very short time, Nvidia has gone from dominating the Chinese GPU market for artificial intelligence to losing it completely. The restrictions of the Trump Administration and the intensification of the trade war between the powers left Nvidia out of the game. Either it would adapt its GPUs and create less capable versions of those it sold in the West or it would not be able to sell in China. For a time, Nvidia was selling the H20 to adapt to the new rules, but it is something that has taken its toll. As AI needs demanded more powerful GPUs and own chinese industry with Huawei, Cambricon and Moore Threads was developed, Nvidia was being left out of the game. Official quota. In the middle of last year, Nvidia CEO Jensen Huang pressed Donald Trump to see reason: it was better for Nvidia to be able to enter China both to make money and to slow the accelerated development of the domestic industry, one that Western restrictions had given wings to. In the end, the US gave in previous tariffs of 25% and one condition: all GPU orders from Chinese companies to Nvidia would be reviewed one by one. There is a problem: the US body in charge of reviewing these export licenses has decreased by 20% in recent months, which is causing delays of months when it comes to fulfilling an order. From when a Chinese company asks for Nvidia GPUs until they are given an answer, the ‘chinese dragons‘They have already released some product. The result? Huang points out that Nvidia has gone from being a leader in China to have a 0% quotapainting the situation as a true drama and pointing directly to the strategies of both China and, above all, the United States as the cause of his company falling into the offside of the large Asian market. Furthermore, it is China itself that encourages its companies to, to the extent possible, use Chinese hardware that they is developing at accelerated rates. “Official” fee. But the fact that Huang claims that his market share in China is 0% does not mean that there are no GPUs for AI in China because it seems that there are H100, H200 and even B200 due to something very simple: smuggling. Despite the proprietary technological solutions they are developing, it is evident that a large part of the AI ​​industry is built with Nvidia GPUs and that implies that the tools are very well optimized for them. There are several occasions in which Nvidia AI chip smuggling networks have been reported, with modest seizures on occasions (just tens of millions of dollars) and somewhat larger seizures on others (hundreds of millions in a few months). Chinese companies obtain these chips through indirect routes from Hong Kong and Singapore and, although Nvidia tries to trace the origin, the clandestine flow and opaque chains make the task complex. trapped. Someone is lining their pockets and that someone is not Nvidia. And the problem is that Huang’s pressure had an effect, but the solution they gave him is not as agile as the market needs. Returning to the issue of bureaucracythe United States Office of Industry and Security, which is responsible for reviewing these export licenses, reduced its workforce by 19% in 2024. Specifically, those who develop standards linked to the semiconductor industry and review licenses have decreased by 20%. The result is an average of 76 days to resolve export requests, something that is extending so far this year and which is disastrous news for both Nvidia and others deeply involved in the AI ​​segment, such as AMD. From China, things are not much better, since companies must make it very clear why they need Nvidia AI chips and cannot meet their objectives using national alternatives. Jensen, almost excluded. In any case, it is evident that Huang does not like to be missing the AI ​​party in China, in the same way that he is going to miss the new trip of Donald Trump and other executives to a summit between Trump and Xi Jingping that will be held between the 13th and 15th of this month. Or so it seemed. This is an event in which conversations will focus on agriculture and commercial aviation, so a priori Jensen didn’t have much in mind. But of course, alongside Trump are CEOs like Elon Musk, Cristiano Amon or Tim Cook, among others. And, although it seemed that he was not invited, as we see in South China Morning PostIn a message from Trump on his social network, it was confirmed that Huang will finally accompany him on the trip. In the end, it’s about money. Jensen Huang doesn’t want China to have the best chips because He wants to save those for the United States.but it is a very large market in which Nvidia can offer chips strategically: it makes money while making companies opt for its product instead of that of the Chinese companies themselves. In Xataka | Nvidia’s superpower is not having money, it is making everyone work for it: Foxconn is the latest to join

Corning has the solution to accelerate Nvidia chips even more

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There are good reasons why a company of Nvidia’s stature would want to collaborate with a company like Corning, specialists in manufacturing the glass that protects our mobile phones. Corning offers more products than your Gorilla Glassand that is precisely what Jensen Huang’s company is interested in. And it is that Nvidia is going to invest about 3.2 billion dollars at the glass manufacturer with the intention of multiplying the optical connectivity production capacity on US soil tenfold. What’s on the table. The financial scope of the agreement has been revealed in parts. It was initially announced that Nvidia would receive warrants (stock purchase rights) to acquire up to 15 million Corning shares at a price of $180 per share, representing a potential investment of up to $3.2 billion. Added to this is a pre-financed warrant for another additional 500 million. But the CEO of Nvidia confirmed on CNBC that the company has also made “a prepayment of several billion dollars” to finance the construction of the new factories, a figure that was not part of the initial official announcement and whose exact amount has not been made public either. Fiber optics are the thing. The data centers that power AI They house hundreds of thousands of GPUs that must communicate with each other continuously and at high speed. For decades, this communication has been carried out using copper cables, and in fact for short-distance connections within the rack (from the server to the switch), they are still used, but fiber optics end up being superior in everything, both in terms of speed, energy consumption and lower signal loss. Which Nvidia has in mind. The technical term at the center of this agreement is co-packaged optics, which refers to the integration of glass fiber directly into chip systems, progressively replacing copper cables. Inside Nvidia rack systems (such as the Vera Rubin) there are currently about 5,000 copper cables that could be replaced by Corning fiber optics. Already at last year’s GTC, Huang rated this technology “essential for the deployment of AI.” The company has been preparing the ground for months: in March invested $4 billion in Coherent and Lumentumtwo companies specializing in lasers and components that convert data between light and electrical signals, which then travel through Corning fiber cables. Who else is in the race. Nvidia is not the only one betting on this technology. Its competitors Broadcom and Marvell They have already launched similar productsand Intel also develops its own co-integrated optics solutions. For its part, Corning already had Meta as a reference client. In fact, Zuckerberg’s company announced an agreement of up to 6 billion dollars for Corning to expand its optical cables plant in Hickory, North Carolina. The alliance with Nvidia now adds three more facilities and multiplies the company’s optical connectivity manufacturing capacity in the United States by ten, in addition to increasing its fiber production by more than 50%. ““Made in America”. The agreement comes precisely at a time when the Trump administration is pushing to relocate technological supply chains that have been built for decades in Asia (Taiwan, China or Vietnam). Huang counted to CNBC that “it is an extraordinary opportunity to reinvest and revitalize American manufacturing for the first time in generations.” According to the CEO of NVIDIA, the tech sector would not be the only one to benefit, since the construction and operation of these data centers generates demand for electricians, construction workers, chip manufacturing operators and infrastructure specialists. “The skilled worker shortage and demand are incredibly high,” mentioned Huang in the middle. Converted company. Corning has become another of those companies that have seen their business benefit from the AI ​​boom. And the signature accumulates an increase of more than 300% in the last year, driven by its repositioning towards the AI ​​market and moving away from its best-known image as a manufacturer of glass for mobile screens. In Xataka | If the question is whether using ChatGPT or Claude in English is more efficient and saves tokens, the answer is: yes

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