chips

Huawei has a patent with which to manufacture 2nm chips. The only problem is that it’s just a patent.

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Huawei has just applied for a patent in which a new and unique process of advanced chip production. The patent focuses on improving one of the limitations of the technology of deep ultraviolet photolithography (UVP) to try to compete in this way with the extreme ultraviolet machines (UVE) to which China still unable to access. There are, however, many uncertainties here. The patent. Huawei formally submitted the technical documentation in June 2022 to the Chinese patent office, allowing the invention to be “protected” since then. The detailed content of their study was made public in January 2025, but It is now that it has come to light. The patent is only applied for, not granted or granted. The patent office is examining the application to determine if it meets the requirements. Why is it important. This patent tries to address the limitations of the so-called edge placement error (EPE, Edge Placement Error) in the advanced interconnection process used when manufacturing advanced chips. The method discovered makes it theoretically possible to use “metal spacings” smaller than 21 nm, even when using deep ultraviolet (UVP) technology instead of extreme ultraviolet (UVE), which is the most advanced photolithographic technology today… and to which Chinese manufacturers like Huawei do not have access. If it achieves its objective, the firm could have access, for example, to chips that would theoretically compete even with chips made with 2nm photolithography. Metal spacing? That term (metal pitch in English) refers to the minimum distance that exists between the metal lines that form the interconnections within the integrated circuit or, in this case, the chip. These lines carry power and data signals between the transistors, and that metal spacing is extraordinarily small for advanced nodes. The objective of the patent is precisely to allow the manufacture of these lines with a spacing of less than 21 nm. This gives rise to a possible process that could compete with the 2nm UVE photolithography used, for example, by TSMC. The important word there is “could.” Edge Placement Error (EPE). EPE is the error that occurs when a pattern on a chip is not placed exactly where it was intended by the chip design. The closer that metal spacing is, the smaller the EPE margin must be to prevent the lines from touching and causing a short circuit. At this scale it is incredibly complex to solve this problem, and Huawei’s patent precisely proposes a way to achieve it. Supervitaminizing “old” lithography. What makes this method possible is that UVP photolithography, less powerful and advanced than UVE, can be used to compete with it. This method would allow “jumping” the limits that this process now faces, and which normally had many difficulties in going beyond 21 nm. A double hard mask process of two materials and a special patterning scheme are introduced that theoretically allow us to go below 21 nm. and even 5 nm which are already very complicated to achieve with EUV. In short: China could achieve advanced chips without the need for use the most advanced ASML machinesto which you do not have access. But. Although the technique is apparently striking, there are two big problems here. The first and most important is that this is just a patent and that does not mean that the process can be transferred to reality. The difficulties in doing so are enormous, and that leads us to the second problem: the effectiveness of production would probably be very low and the yield (process success rate) would be greatly affected. That is to say: of all the chips theoretically produced with this technique, only a small part would be valid, which would waste a huge part of the investment. In Xataka | In its race to make advanced chips, China has tried to copy ASML. It’s going wrong

The US has insisted that TSMC manufacture chips in Arizona. The reality: it is a disastrous idea

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TSMC, the world’s largest semiconductor maker, has long been pushing for unprecedented expansion outside Taiwan. The initiative includes large projects in the United States, Japan and Germany, but does not respond to market demand, but rather to geopolitical pressure and a chip war that wants to try to “repatriate” this type of process. It’s a terrible idea. Morris Chang knows it’s a mistake. Despite the political urgency, the economic viability of these factories abroad has been questioned by TSMC founder Dr. Morris Chang. He already had the previous experience with the WafertTech factory in the US in 1996, and has qualified Arizona initiative as “a very expensive exercise in futility” Everything one hour away. Chang’s skepticism is based on the belief that TSMC’s operations and profitability are intrinsically dependent on its ecosystem, which is entirely concentrated in Taiwan. The Hsinchu Science Park “cluster” allows hundreds of technology partners to operate within a “one-hour” radius, facilitating problem resolution and providing ultra-fast logistics and unparalleled coordination. TSMC is still 90% Taiwanese. Despite that global expansion, TSMC remains deeply Taiwanese, with more than 90% of its manufacturing capacity and nearly 90% of its employees on the island. That’s where your massive, highly trained and qualified engineering talent base is. That is again a key factor in its competitive advantage, and in fact the company has already warned its employees in the US that they should adhere to the work culture of the Taiwanese company. Arizona produces, but it is more expensive. That attempt to replicate Taiwanese efficiency in Arizona has revealed something important: although TSMC has achieved competitive performance in its first production runs with 4nm photolithography, the cost of the wafers is significantly higher. The local supply of raw materials and equipment remains insufficient, making the factory dependent on Asia and is a bottleneck for the efficiency of the production cycle. Skilled labor shortages and permitting and bureaucracy, which further slow things down, add considerable operational costs. Japan and Germany, next objectives. TSMC has two major expansion projects in Japan (JASM) and Germany (ESMC). These locations will focus on much less advanced photolithographic nodes (28/16 nm) and will focus on meeting the demand of some specialized customers such as Sony for image sensors in Japan or Bosch in Europe. The scale of these investments is less than that of Arizona, which aims to be the world’s largest advanced chip factory… if planned future phases are completed. A double edged sword. TSMC’s expansion has two sides. On the one hand, TSMC consolidates its technological leadership and its strategic role as a “silicon shield” against China. On the other hand, it generates internal anxiety about the possible “leakage” of advanced technology and talent that could weaken national sovereignty in the long term. US pressure even extended to veto the possibility of establishing a TSMC factory in the United Arab Emirates. TSMC does not expand by pleasure, but by pressure. Traditionally, TSMC only builds new factories in response to real demand from its customers. Here the reason has been very different, and geopolitical pressure has forced moves that the company would probably never have made otherwise. Here the different subsidy programs (CHIPS Act in the US, European Chip Law) try to repatriate part of the manufacturing and thus mitigate Asian dependence, but it’s not clear at all that they achieve it. Image | TSMC In Xataka | Japan is rapidly reconquering the chip industry. It has just successfully manufactured its first 2nm transistor

An investment of 2,350 million will make Extremadura a global supplier of diamonds for chips

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Trujillo will be a world center for the production of synthetic diamonds. A factory will be created there with a budget of 2.77 billion dollars (almost 2.4 billion euros) in which the Spanish Society for Technological Transformation will participate (SETT), with 753 million, and the American company Diamond Foundry (DF). And those diamonds will not be used for jewelry, but for especially powerful chips. The silicon problem. Current silicon chips have hit a “thermal wall.” By making them faster and more powerful, they get so hot that they lose efficiency or burn out. This slows down the progress of these chips and their application in fields such as artificial intelligence or automotive. Alternatives have been sought for a long timeand the diamond is precisely one of the most striking. The evolution of Trujillo. The Diamond Foundry factory will not make jewelry, but the synthetic diamond wafers it first produced two years ago. The diamond has a thermal conductivity much higher than that of silicon, with values ​​ranging between 1,000 to 2,200 W/mK compared to 153 W/mK for silicon. Or what is the same: it allows us to guarantee that, as they highlighted on IEE Spectrumthe chips of the future will remain “fresh.” The impact. By using diamond as the base or substrate for these chips, it is possible to run them at extreme speeds without overheating. This will position Spain as the world center of this critical technology. The North American company It already had two plants in Trujillo in which monocrystalline diamond (SCD) ingots were produced. The factories are also powered by solar energy, which is abundant in the Extremadura region. Zaragoza as a great ally. Those responsible for Diamond Foundry they explain in the official statement that the new factory is already underway with two construction shifts to accelerate the works. The ingots (the “raw” form of the material) will then go through a singling or cutting process that “slices” them into very thin sheets. These sheets, which are initially rough, are polished at a microscopic level and packaged in a sterile environment. Precisely this “post-processing” phase of production will be carried out in Zaragoza. The investment. The total budget they talk about in DF is 2,770 million dollars, about 2,392 million euros at the exchange rate. Of that amount, the SETT—which groups together previous investments such as PERTE Chip—, will contribute 753 million euros according to DF. It is expected that in the first ten years of the project the contribution to the Spanish GDP will be around 2,150 million euros, and it is expected to generate around 500 direct jobs and more than 1,600 indirect jobs. How to produce synthetic diamonds. While natural diamonds they take time to produce between 1,000 and 3,300 million years old, in Trujillo they are manufactured in approximately one month. To achieve this, DF uses 20 plasma reactors that exceed 1,000 degrees in temperature and generate conditions similar to those found in nature. The process starts with a 20.0 x 20.0 x 0.2 mm diamond “seed” that, when subjected to a combination of gases and a microwave process, grows until it reaches the optimal dimensions for use. Di Caprio, among investors. A curiosity: the San Francisco-based company was founded in 2012 by Martin Roscheisen and Jeremy Scholz, but what is surprising is its list of investors. Among them are iPod co-creator Tony Fadeel, Twitter founder Evan Williams and actor Leonardo di Caprio. The water problem. Diamond Foundry’s plants in Trujillo have faced significant problems related to their water supply. It is estimated that the plants need at least 730,000 cubic meters of water per year, which exceeds the annual drinking water consumption of the entire population of Trujillo. Various platforms such as Save El Berrocal and Ecologistas en Acción have warned of that danger, although Diamond Foundry has defended that its plan is based on the reuse of water from the Trujillo Wastewater Treatment Plant (WWTP). The Extremadura Government gave the green light to some modifications to the original DF project and considered that the factories would not produce significant adverse effects on the environment. In Xataka | China defies geology: it manufactures in a week what the Earth takes a billion years to do

Chinese electric car manufacturers opted to develop their own chips. He already plans to sell them to others.

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In 2024, Nio advertisement the world’s first 5nm chip for autonomous driving, being an important step towards technological independence from a Chinese manufacturer of such caliber. A year and a half after its announcement, the company is now beginning the external marketing of that chip, according to they count from Latepost. In this way, Nio is on the eve of transforming one of its most expensive investments into a potential source of income. Just like point The electric vehicle maker has already begun providing technology licenses to an automotive chip company. A multimillion-dollar project that seeks profitability. The development of Shenji NX9031 It has involved an investment of billions of yuan. William Li (Li Bin), CEO of Nio, revealed that the R&D expenditure on this chip was equivalent to the cost of building 1,000 battery exchange stations, which would place the investment above 140 million dollars. The project, started in 2021, has involved more than 600 professionals covering front and back design, verification and testing. What makes this chip special. Made with automotive-grade 5-nanometer technology, the Shenji NX9031 promises approximately four times the computing power of Nvidia’s Orin-X. Zhang Danyu, head of Nio’s chip division, pointed out in May that in some of their specifications they even surpass industry-standard chips and that their mass production began several months before Nvidia’s latest smart driving chip, the Thor-U. It is currently integrated into models such as the ET9, ES6 2025 and EC6. How much does a technology license cost?. According to share From Latepost, the value of these license agreements varies significantly depending on the type of authorization. An individual intellectual property license could be worth several million dollars, while a technical authorization at the system-on-chip (SoC) level could reach hundreds of millions of dollars. A new source of income. That the Nio chip begins to be marketed externally comes at a great time for the company, especially now that the manufacturer faces pressure significant from investors and has promised to become profitable in the fourth quarter. The company has intensified its efforts this year to reduce expenses and explore new sources of income. In March, Li Bin already advertisement publicly at the China EV 100 Forum that Nio chips and operating systems would be open to the industry. “If they want to buy the best chips, they can contact Nio,” he said then. What it means for the future of Nio. According to Li Bin, the chip provides a cost optimization of approximately 10,000 yuan ($1,400) per vehicle in the brand’s own models. Now, with the external license, Nio not only recovers part of its investment, but also positions itself as a technology provider for other manufacturers in the automotive sector. In Xataka | The longest straight road in the world is a mental challenge: 240 km without curves, in the middle of the desert and with truck traffic

The US vetoed NVIDIA’s most powerful chips in China. I didn’t count on an unexpected problem: Indonesia

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NVIDIA is at the center of the technological war between China and the United States. After the blockadethe US allowed the company sell a version of its H20 chips specific for the Chinese market, but the most powerful chips, The Blackwells are still banned in China. Or so we believed. What is happening. Donald Trump made it clear that he does not want China to have access to Blackwell chips, but despite the blockade, an investigation by the Wall Street Journal shows how there are Chinese companies benefiting from the computing power of these chips using legal shortcuts. The process. The investigation details the process that NVIDIA’s Blackwell chips go through until INF Tech, a Shanghai-based startup, uses the computing power. NVIDIA sells its chips to Aivres: Aivres is a Silicon Valley company partially owned by Inspur, a Chinese company that is on the US blacklist. NVIDIA could not do business with Inspur or its partners, but the blockade does not affect partners based in the US, as is the case with Aivres. Aivres sells the chips to Indonesia: specifically to an Indonesian communications provider called Indosat Ooredo Hutchison. The agreement includes the sale of 32 NVIDIA GB200 racks with 72 Blackwell chips each; more than 2,300 chips worth $100 million. Indonesia sells computing power to China: The end customer for this cloud computing power is INF Tech, which will use it to train AI in financial and medical research applications. This point is key as we will see later. Why it is important. The investigation calls into question the true effectiveness of US blockades and regulations. Using intermediaries in other countries, Chinese companies can manage to circumvent the restrictions and access the most powerful chips, all without violating the restrictions. Cracks. According to the Trump administration’s controls, the deal is legal as long as INF Tech does not use the chips to help the government with military intelligence applications or to develop weapons. However, it is difficult to know what it is actually being used for and in fact in the US there are suspicions that The Chinese government is leaning on the private sector to improve its military technology. Disagreement. If there is a crack, the logical thing would be to cover it. The Biden administration tried to tighten these rules to prevent chips from being sold to countries that are not close allies of the United States. This would have prevented the sale to the Indonesian company, but when Trump returned to power he decided not to go ahead with these new rules. Instead of the government controlling it, it should be the companies themselves. Interests. The US blockades seek to take advantage of China in the AI ​​technological race, all for reasons of “national security.” It is contradictory that they leave these cracks open through which these chips end up sneaking in legally. The one who thinks it’s great is NVIDIA. Speaking to the Wall Street Journal, a company spokesperson came out in favor of Trump’s decision, saying that “Biden’s controls cost taxpayers tens of billions, paralyzed innovation and ceded ground to foreign rivals.” Image | NVIDIA, Pexels In Xataka | The Chinese government has taken a definitive step to break NVIDIA’s dominance in China: prioritize “national” chips

The industry became obsessed with training AI models, while Google prepared its masterstroke: inference chips

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In recent years, what was truly relevant was training AI models to make them better. Now that they have matured and training it no longer scales as noticeablywhat matters most is inference: that when we use AI chatbots they work quickly and efficiently. Google realized this change in focus, and has chips precisely prepared for it. Ironwood. This is the name of the new chips from Google’s famous family of Tensor Processing Units (TPUs). The company, which began developing them in 2015 and launched the first ones in 2018now obtains especially interesting fruits from all that effort: some really promising chips not for training AI models, but for us to use them faster and more efficiently than ever. Inference, inference, inference. These “TPUv7” will be available in the coming weeks and can be used to train AI models, but they are especially aimed at “serving” these models to users so that they can use them. It is the other big leg of AI chips, the really visible one: one thing is to train the models and quite another to “execute” them so that they respond to user requests. Efficiency and power by flag. The advance in the performance of these AI chips is enormous, at least according to Google. The company claims that Ironwood offers four times the performance of the previous generation in both training and inference, and is “the most powerful and energy-efficient custom silicon to date.” Google has already reached an agreement with Anthropic so that the latter has access up to one million TPUs to run Claude and serve it to its users. Google’s AI supercomputersand. These chips are the key components of the so-called AI Hypercomputer, an integrated supercomputing system that according to Google allows customers to reduce IT costs by 28% and a ROI of 353% in three years. Or what is the same: they promise that if you use these chips, the return on investment will be multiplied by more than four in that period. Almost 10,000 interconnected chips. The new Ironwoods are also equipped with the ability to be part of joining forces in a big way. It is possible to combine up to 9,216 of them in a single node or pod, which theoretically makes the bottlenecks of the most demanding models disappear. The size of this type of cluster is enormous, and allows for up to 1.77 Petabytes of shared HBM memory while these chips communicate with a bandwidth of 9.6 Tbps thanks to the so-called Inter-Chip Interconnect (ICI). More FLOPS than anyone. The company also claims that an “Ironwood pod” (a cluster with those 9,216 Ironwood TPUs) offers 118x more ExaFLOPS FP8 than its best competitor. FLOPS measure how many floating-point math operations these chips can solve per second, ensuring that basically any AI workload is going to run in record times. NVIDIA has more and more competition (and that’s a good thing). Google chips are a demonstration of the clear vocation of companies to avoid too many dependencies on third parties. Google has all the ingredients to do it, and its TPUv7 is proof of this. It’s not the only oneand many other AI companies have long sought to create their own chips. NVIDIA’s dominance remains clearbut the company has a small problem. In inference CUDA is no longer so vital. Once the AI ​​model has been trained, inference operates under different game rules than training. CUDA support remains a relevant factorbut its importance in inference is much less. Inference focuses on obtaining the fastest possible answer. Here the models are “compiled” and can run optimally on the target hardware. This may cause NVIDIA to lose relevance to alternatives like Google. In Xataka | When you’re OpenAI and you can’t buy enough GPUs, the solution is obvious: make your own

The secret of Chinese AI companies to compete without Nvidia chips: electricity subsidized by Beijing

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Everywhere we look, there is artificial intelligence. Everyone talks about it, but what is its fuel? It’s not the data or the chips: it’s the electricity. While in the West technology companies are looking for how to power their data centers —increasingly energy hungry—, China has decided to take a different step. Beijing has designed an energy subsidy for its technology sector with a clear objective: to make the energy that powers the digital brains of its next generation of chips cheaper. Energy subsidy. Since September, the Chinese Government banned large national technology companies —including Alibaba, ByteDance and Tencent—acquire artificial intelligence chips from the American Nvidia, in an attempt to strengthen local production. However, the consequence was immediate: national processors consume more electricity. According to The Chosun Dailygenerating the same number of tokens with Chinese chips requires 30% to 50% more energy than with Nvidia’s H20, which sent electricity bills skyrocketing and led companies to complain to regulators. To make up for that gap, local governments introduced grants that cover up to a full year of operating costs, according to the Hong Kong media on.cc. In those provinces, industrial electricity was already 30% cheaper than in the developed coastal areas of the east, but with the new incentives the price could fall to 0.4 yuan per kilowatt-hour, a record figure for the Chinese technology industry. ¿How does the energy plan work? The scheme is relatively simple, but strategic. Local governments offer electricity discounts of up to half to data centers that use chips produced within the country. Operators that use foreign processors – such as those from Nvidia or AMD – are excluded from the program. In addition, the energy provinces receive direct support from the State to finance the discounts, with the aim of reducing dependence on technological imports and compensating for the increased consumption of local chips. According to the Financial TimesChinese data centers that rely on domestic semiconductors are, for now, less energy efficient, but the subsidy seeks to bring their costs in line with those of more advanced foreign chips. These regions—Guizhou, Gansu, and Inner Mongolia—have become hotbeds for data center clusters, thanks to their abundance of hydropower and coal. There, companies like Alibaba or Tencent are building new facilities to house their generative AI models, taking advantage of lower energy costs and tax incentives. This policy combines three strategic priorities: making energy cheaper, promoting domestic chips and reinforcing technological sovereignty. In a context of United States restrictions, each subsidized kilowatt is also a political statement. An industrial policy with a geopolitical charge. Behind the energy plan is a long-range political commitment. The Chinese Government intends for its technology companies to progressively replace imported chips with domestic processors, even if this implies higher costs in the short term. The electricity subsidy acts as a temporary bridge for national giants to adopt local chips without losing competitiveness. This measure is included in a broader national strategy of technological self-sufficiency. As the Financial Times explains in its series The State of AIChina is using its “society-wide mobilization capacity” to accelerate the development of artificial intelligence. The country already leads the number of patents and scientific publications in AI, and although the United States maintains an advantage in chips and talent, the gap narrows every year. Analyst Dan Wang, quoted by the same media, points out: “China has achieved a unique balance between engineering capacity, state control and massive industrial deployment, allowing it to advance faster than other countries in the practical application of AI.” Meanwhile, in the West… China’s decision contrasts with the energy challenges of the United States. Microsoft CEO Satya Nadella warned that the real bottleneck of AI It is no longer the chips, but the energy. In fact, he explained that many companies accumulate chips that they cannot connect due to lack of power supply. Both Microsoft and Google are already studying building modular nuclear reactors to power their future data centers, a sign of the enormous energy consumption that artificial intelligence requires. While Silicon Valley seeks electricity, China subsidizes it. This asymmetry reflects two different models: one guided by state intervention and the other by market competition. Both pursue the same goal—sustaining the artificial intelligence revolution—but with opposite philosophies. A future plugged into the State. The Chinese subsidy not only alleviates costs: it redefines the relationship between the State and the private sector in the age of AI. As analyst Arnaud Bertrand observed, US restrictions pushed China towards a different model: more efficient, more open and more collective. “By operating under hardware limitations, Chinese companies have learned to optimize resources and share open models like Qwen or DeepSeek,” wrote Bertrand on the social network That strategy, based on efficiency and diffusion, could give China a long-term advantage in global adoption, since any company in the world can download and adapt its models. The country that controls the plug. China isn’t just making the chips that power its artificial intelligence. It is also building the electrical grid that makes them possible. In a world where data is the new oil, Beijing has decided to subsidize the fuel of the digital brain. While the West debates how to connect its supercomputers, China plugs them in at a reduced price. And in this race, whoever controls the plug could end up controlling the future. Image | FreePik and FreePik Xataka | The world of AI has a problem: there is no energy for so many chips

there is no power for so many chips

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Microsoft CEO Satya Nadella recently participated in an interview and in it Nadella explained that the real problem that the AI ​​segment has is not that there is excessive production of chips, but that we do not have enough energy to power all of them. It is confirmation of something that we have been seeing coming for a long time. Too many chips for so little power. Both Nadella and Sam Altman, the CEO of OpenAI, participated in the interview. During it, the Microsoft CEO explained that “the biggest problem we have now is not excess computing capacity, but energy. It’s something like the ability to build (data centers) close enough to energy sources.” Chips in the drawer. Nadella went on to highlight that “if you can’t do something like that (supply enough power), you’re going to have a bunch of chips sitting around in inventory that you can’t plug in. In fact, that’s my problem right now: It’s not that I don’t have a sufficient supply of chips: it’s actually the fact that I don’t have places to plug them in.” A problem that was seen coming. Microsoft, like other companies that have opted for this segment, have been trying to prepare for this energy demand for some time. Two years ago, in autumn 2023, they were already looking for experts to lead its nuclear program. The objective: bet on the new SMR reactors which could be a good solution to power future data centers. Google was clear about exactly the same thing a year later, and reached an agreement with Kairos Power to build seven of those reactors from now to 2030. I stew it, I eat it. Large technology companies that are dedicating billions of dollars to creating new data centers in the US have discovered that the current electrical grid may be insufficient for their needs. Their solution is to build their own plantssomething they hope can balance the demand and consumption imposed by these gigantic computing factories in which tens of thousands of AI accelerator GPUs work to serve current (and future) users of AI functions. Growing needs. A report from the International Energy Agency (IEA) estimated that in 2022 between 240 and 340 TWh of energy will be used for data centers (excluding cryptocurrencies). This represents an increase of between 20 and 70% compared to 2015 consumption. Already in April 2024, that same organization warned that several countries will multiply this consumption significantly. Triple energy? ARM CEO Rene Haas then pointed out that energy needs would triplebut he probably couldn’t know how events would develop. Since then, AI companies have announced mammoth projects —with Stargate at the helm—and they will dedicate huge amounts of money in an uncertain bet: that AI will be the great revolution that will drive our daily lives. In Xataka | NVIDIA and OpenAI have just made a masterstroke. One that strengthens them and weakens everyone else

Everyone is developing chips that compete with NVIDIA’s. They are in the wrong race

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Qualcomm advertisement on Monday that it is working on AI accelerator chips, which means there will be new competition for NVIDIA. The company that dominates the AI ​​hardware landscape is seeing a large group of competitors try to erode that position, but the problem for all of these companies is not the chips, but something else. A CUDA call. what has happened. Qualcomm has announced the AI200 chip, which will begin selling in 2026, and the AI250, which will do so in 2027. Both will be able to work in rack-type systems that have liquid cooling. Qualcomm servers may have up to 72 chips based on the Hexagon NPUs of the company’s Snapdragon SoCs. Inference yes, training no. The company has revealed that its chips focus on inference (the execution of AI models) and not training. Their rack-based systems will have lower operating costs than cloud system providers, Qualcomm says. Each rack consumes 160 kW, a figure comparable to the consumption of some racks based on NVIDIA GPUs. There are no details about the price of these chips, the cards or the racks that will integrate them, nor about how many NPUs can be offered in each rack. What we do know is that Qualcomm’s accelerator cards will support up to 768 GB of memory, more than what NVIDIA or AMD offer in their current models. according to CNBC. Chips for third parties. The other important point is that Qualcomm will sell its AI chips and other components separately, allowing large AI companies to “customize” their own racks based on Qualcomm chips. It is an identical philosophy to the one they have adopted in the world of their mobile SoCs. Investors viewed the news with exceptional optimism, and Qualcomm shares rose 11% in Monday’s session. NVIDIA dominates with an iron fist. In the AI ​​chip segment, the king is NVIDIA. The company is the absolute protagonist of this market and according to CNBC it maintains a 90% market share, which has allowed it to skyrocket its valuation to 4.5 trillion dollars. That dominance could now be threatened by the avalanche of chips that are arriving from various manufacturers. All against NVIDIA. AMD has its excellent Instinct, Google has your TPUsAmazon their TrainiumMicrosoft their Maia and Huawei has your Ascend. All of them make really striking proposals for NVIDIA chips, and little by little these solutions are being integrated into more and more data centers. But the real problem is not in the hardware, but in the software. The great challenge is to defeat CUDA. The de facto standard in the AI ​​industry that developers use It’s CUDAa platform that allows you to take full advantage of the capabilities of NVIDIA chips in the field of artificial intelligence. This hardware+software combination is much more mature than that of its competitors, who have the hardware part resolved (or are on the right track) but do not have a platform comparable to CUDA. AMD has ROCmwhich is especially interesting because it is Open Source, but at the moment its features still do not reach those of CUDA. Reinvent the wheel? CUDA has been on the market for almost two decades, which means that the majority of academic research and pioneering models—such as ImageNet—were written for CUDA. It is not a language, it is a vast collection of libraries, optimized frameworks (like cuDNN), debugging tools and a huge community. Developing a competitor is basically like reinventing the wheel, and migrations are expensive and companies and startups will not have an easy time assuming it. China is also in the fight. And of course, if there is another great protagonist in this race, it is China. The Asian giant, previously dependent on NVIDIA, is seeking to get rid of this manufacturer, and along with the development of advanced AI chips they are also trying to have its own AI software to surpass CUDA. In Xataka | AI is the best thing happening to nuclear fusion. The construction of ITER is already accelerating

they use Huawei and DeepSeek chips

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China’s race to get become technologically independent from the United States It is reaching the military sector. The military is accelerating the integration of artificial intelligence into its operations and most importantly: they are favoring national technologies. In the software, DeepSeek. In hardware, Huawei chips. what’s happening. the chinese army is using AI to support strategic decision making and target detection. According to an analysis of Reutersseveral studies and patents suggest that they are also applying it in new vehicles such as robot dogs and autonomous drones, all prioritizing the use of national technologies, both in software and hardware. Why is it important. China has already given steps to stop depending on Nvidiathe maker of the most powerful AI chips. This is one more step towards technological independence, but in a critical sector such as the military. The objective is to eliminate foreign influence in its defense infrastructure, just like the United States does. Huawei chips. Speaking to Reuters, the defense policy expert Sunny Cheungassures that since the beginning of this year the Chinese military has increased the number of contractors that exclusively use national hardware. That is to say, AI chips made by Huawei. Although the military still uses Nvidia chips (it is not known if they were imported before or after of the blockade), there is a movement towards the use of own chips. DeepSeek. At the beginning of the year, military experts in China assured that the military was testing DeepSeek integration. In May, researchers from Xi’an University showed a system based on DeepSeek capable of creating and analyzing 10,000 combat scenarios in just 48 seconds. Reuters analyzed several tenders awarded to various companies by the Chinese military and at least a dozen mentioned DeepSeek, while only one referenced Alibaba’s Qwen. It is clear which is the preferred model for the Chinese army. Robot dogs and drones. The documents analyzed by Reuters also suggest that the Chinese military is integrating AI into autonomous vehicles such as robot dogs. It is no secret, in 2024 the army itself published a video promoting robot dogs who moved in packs to eliminate explosives and other threats. The robots in the video were from the Chinese company Unitree, but there are also other national companies dedicated to the manufacturing of these vehicles such as Norinco, which confirmed in a technical report that they use Huawei chips. On the other hand, Deepseek is also being integrated into drones to give them the ability to recognize and follow targets with hardly any human intervention. Image | Wikipedia, Flickr In Xataka | Europe already has the future of war drones within its reach. And it is offered by a country accustomed to them: Israel

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