Google unveiled major AI advancements at I/O 2025, including Gemini 2.5 with Deep Think, AI Mode in Search, Veo 3 for video with audio, and a $249 Ultra plan aimed at power users and enterprises.Read More
As NotebookLM matures, its emerging business-tier capabilities suggest growing alignment with the productivity and compliance…Read More
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AMD held a Computex press event to introduce its Radeon RX 9060 XT graphics cards and Threadripper CPUs for next-gen gaming and workstations.Read More
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When we set out to write a story on the best available estimates for AI’s energy and emissions burden, we knew there would be caveats and uncertainties to these numbers. But, we quickly discovered, the caveats are the story too. This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. Measuring the energy used by an AI model is not like evaluating a car’s fuel economy or an appliance’s energy rating. There’s no agreed-upon method or public database of values. There are no regulators who enforce standards, and consumers don’t get the chance to evaluate one model against another. Despite the fact that billions of dollars are being poured into reshaping energy infrastructure around the needs of AI, no one has settled on a way to quantify AI’s energy usage. Worse, companies are generally unwilling to disclose their own piece of the puzzle. There are also limitations to estimating the emissions associated with that energy demand, because the grid hosts a complicated, ever-changing mix of energy sources. It’s a big mess, basically. So, that said, here are the many variables, assumptions, and caveats that we used to calculate the consequences of an AI query. (You can see the full results of our investigation here.) Measuring the energy a model uses Companies like OpenAI, dealing in “closed-source” models, generally offer access to their systems through an interface where you input a question and receive an answer. What happens in between—which data center in the world processes your request, the energy it takes to do so, and the carbon intensity of the energy sources used—remains a secret, knowable only to the companies. There are few incentives for them to release this information, and so far, most have not. That’s why, for our analysis, we looked at open-source models. They serve as a very imperfect proxy but the best one we have. (OpenAI, Microsoft, and Google declined to share specifics on how much energy their closed-source models use.) The best resources for measuring the energy consumption of open-source AI models are AI Energy Score, ML.Energy, and MLPerf Power. The team behind ML.Energy assisted us with our text and image model calculations, and the team behind AI Energy Score helped with our video model calculations. Text models AI models use up energy in two phases: when they initially learn from vast amounts of data, called training, and when they respond to queries, called inference. When ChatGPT was launched a few years ago, training was the focus, as tech companies raced to keep up and build ever-bigger models. But now, inference is where the most energy is used. The most accurate way to understand how much energy an AI model uses in the inference stage is to directly measure the amount of electricity used by the server handling the request. Servers contain all sorts of components—powerful chips called GPUs that do the bulk of the computing, other chips called CPUs, fans to keep everything cool, and more. Researchers typically measure the amount of power the GPU draws and estimate the rest (more on this shortly). To do this, we turned to PhD candidate Jae-Won Chung and associate professor Mosharaf Chowdhury at the University of Michigan, who lead the ML.Energy project. Once we collected figures for different models’ GPU energy use from their team, we had to estimate how much energy is used for other processes, like cooling. We examined research literature, including a 2024 paper from Microsoft, to understand how much of a server’s total energy demand GPUs are responsible for. It turns out to be about half. So we took the team’s GPU energy estimate and doubled it to get a sense of total energy demands. The ML.Energy team uses a batch of 500 prompts from a larger dataset to test models. The hardware is kept the same throughout; the GPU is a popular Nvidia chip called the H100. We decided to focus on models of three sizes from the Meta Llama family: small (8 billion parameters), medium (70 billion), and large (405 billion). We also identified a selection of prompts to test. We compared these with the averages for the entire batch of 500 prompts. Image models Stable Diffusion 3 from Stability AI is one of the most commonly used open-source image-generating models, so we made it our focus. Though we tested multiple sizes of the text-based Meta Llama model, we focused on one of the most popular sizes of Stable Diffusion 3, with 2 billion parameters. The team uses a dataset of example prompts to test a model’s energy requirements. Though the energy used by large language models is determined partially by the prompt, this isn’t true for diffusion models. Diffusion models can be programmed to go through a prescribed number of “denoising steps” when they generate an image or video, with each step being an iteration of the algorithm that adds more detail to the image. For a given step count and model, all images generated have the same energy footprint. The more steps, the higher quality the end result—but the more energy used. Numbers of steps vary by model and application, but 25 is pretty common, and that’s what we used for our standard quality. For higher quality, we used 50 steps. We mentioned that GPUs are usually responsible for about half of the energy demands of large language model requests. There is not sufficient research to know how this changes for diffusion models that generate images and videos. In the absence of a better estimate, and after consulting with researchers, we opted to stick with this 50% rule of thumb for images and videos too. Video models Chung and Chowdhury do test video models, but only ones that generate short, low-quality GIFs. We don’t think the videos these models produce mirror the fidelity of the AI-generated video that many people are used to seeing. Instead, we turned to Sasha Luccioni,
Everything you need to know about estimating AI’s energy and emissions burden Lire l’article »
In the high desert east of Reno, Nevada, construction crews are flattening the golden foothills of the Virginia Range, laying the foundations of a data center city. Google, Tract, Switch, EdgeCore, Novva, Vantage, and PowerHouse are all operating, building, or expanding huge facilities within the Tahoe Reno Industrial Center, a business park bigger than the city of Detroit. This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. Meanwhile, Microsoft acquired more than 225 acres of undeveloped property within the center and an even larger plot in nearby Silver Springs, Nevada. Apple is expanding its data center, located just across the Truckee River from the industrial park. OpenAI has said it’s considering building a data center in Nevada as well. The corporate race to amass computing resources to train and run artificial intelligence models and store information in the cloud has sparked a data center boom in the desert—just far enough away from Nevada’s communities to elude wide notice and, some fear, adequate scrutiny. Switch, a data center company based in Las Vegas, says the full build-out of its campus at the Tahoe Reno Industrial Center could exceed seven million square feet.EMILY NAJERA The full scale and potential environmental impacts of the developments aren’t known, because the footprint, energy needs, and water requirements are often closely guarded corporate secrets. Most of the companies didn’t respond to inquiries from MIT Technology Review, or declined to provide additional information about the projects. But there’s “a whole lot of construction going on,” says Kris Thompson, who served as the longtime project manager for the industrial center before stepping down late last year. “The last number I heard was 13 million square feet under construction right now, which is massive.” Indeed, it’s the equivalent of almost five Empire State Buildings laid out flat. In addition, public filings from NV Energy, the state’s near-monopoly utility, reveal that a dozen data-center projects, mostly in this area, have requested nearly six gigawatts of electricity capacity within the next decade. That would make the greater Reno area—the biggest little city in the world—one of the largest data-center markets around the globe. It would also require expanding the state’s power sector by about 40%, all for a single industry in an explosive growth stage that may, or may not, prove sustainable. The energy needs, in turn, suggest those projects could consume billions of gallons of water per year, according to an analysis conducted for this story. Construction crews are busy building data centers throughout the Tahoe Reno Industrial Center.EMILY NAJERA The build-out of a dense cluster of energy and water-hungry data centers in a small stretch of the nation’s driest state, where climate change is driving up temperatures faster than anywhere else in the country, has begun to raise alarms among water experts, environmental groups, and residents. That includes members of the Pyramid Lake Paiute Tribe, whose namesake water body lies within their reservation and marks the end point of the Truckee River, the region’s main source of water. Much of Nevada has suffered through severe drought conditions for years, farmers and communities are drawing down many of the state’s groundwater reservoirs faster than they can be refilled, and global warming is sucking more and more moisture out of the region’s streams, shrubs, and soils. “Telling entities that they can come in and stick more straws in the ground for data centers is raising a lot of questions about sound management,” says Kyle Roerink, executive director of the Great Basin Water Network, a nonprofit that works to protect water resources throughout Nevada and Utah. “We just don’t want to be in a situation where the tail is wagging the dog,” he later added, “where this demand for data centers is driving water policy.” Luring data centers In the late 1850s, the mountains southeast of Reno began enticing prospectors from across the country, who hoped to strike silver or gold in the famed Comstock Lode. But Storey County had few residents or economic prospects by the late 1990s, around the time when Don Roger Norman, a media-shy real estate speculator, spotted a new opportunity in the sagebrush-covered hills. He began buying up tens of thousands of acres of land for tens of millions of dollars and lining up development approvals to lure industrial projects to what became the Tahoe Reno Industrial Center. His partners included Lance Gilman, a cowboy-hat-wearing real estate broker, who later bought the nearby Mustang Ranch brothel and won a seat as a county commissioner. In 1999, the county passed an ordinance that preapproves companies to develop most types of commercial and industrial projects across the business park, cutting months to years off the development process. That helped cinch deals with a flock of tenants looking to build big projects fast, including Walmart, Tesla, and Redwood Materials. Now the promise of fast permits is helping to draw data centers by the gigawatt. On a clear, cool January afternoon, Brian Armon, a commercial real estate broker who leads the industrial practices group at NAI Alliance, takes me on a tour of the projects around the region, which mostly entails driving around the business center. Lance Gilman, a local real estate broker, helped to develop the Tahoe Reno Industrial Center and land some of its largest tenants.GREGG SEGAL After pulling off Interstate 80 onto USA Parkway, he points out the cranes, earthmovers, and riprap foundations, where a variety of data centers are under construction. Deeper into the industrial park, Armon pulls up near Switch’s long, low, arched-roof facility, which sits on a terrace above cement walls and security gates. The Las Vegas–based company says the first phase of its data center campus encompasses more than a million square feet, and that the full build-out will cover seven times that space. Over the next hill, we turn around in Google’s parking lot. Cranes, tents, framing, and construction equipment extend behind the
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The day after his inauguration in January, President Donald Trump announced Stargate, a $500 billion initiative to build out AI infrastructure, backed by some of the biggest companies in tech. Stargate aims to accelerate the construction of massive data centers and electricity networks across the US to ensure it keeps its edge over China. This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. The whatever-it-takes approach to the race for worldwide AI dominance was the talk of Davos, says Raquel Urtasun, founder and CEO of the Canadian robotruck startup Waabi, referring to the World Economic Forum’s annual January meeting in Switzerland, which was held the same week as Trump’s announcement. “I’m pretty worried about where the industry is going,” Urtasun says. She’s not alone. “Dollars are being invested, GPUs are being burned, water is being evaporated—it’s just absolutely the wrong direction,” says Ali Farhadi, CEO of the Seattle-based nonprofit Allen Institute for AI. But sift through the talk of rocketing costs—and climate impact—and you’ll find reasons to be hopeful. There are innovations underway that could improve the efficiency of the software behind AI models, the computer chips those models run on, and the data centers where those chips hum around the clock. Here’s what you need to know about how energy use, and therefore carbon emissions, could be cut across all three of those domains, plus an added argument for cautious optimism: There are reasons to believe that the underlying business realities will ultimately bend toward more energy-efficient AI. 1/ More efficient models The most obvious place to start is with the models themselves—the way they’re created and the way they’re run. AI models are built by training neural networks on lots and lots of data. Large language models are trained on vast amounts of text, self-driving models are trained on vast amounts of driving data, and so on. But the way such data is collected is often indiscriminate. Large language models are trained on data sets that include text scraped from most of the internet and huge libraries of scanned books. The practice has been to grab everything that’s not nailed down, throw it into the mix, and see what comes out. This approach has certainly worked, but training a model on a massive data set over and over so it can extract relevant patterns by itself is a waste of time and energy. There might be a more efficient way. Children aren’t expected to learn just by reading everything that’s ever been written; they are given a focused curriculum. Urtasun thinks we should do something similar with AI, training models with more curated data tailored to specific tasks. (Waabi trains its robotrucks inside a superrealistic simulation that allows fine-grained control of the virtual data its models are presented with.) It’s not just Waabi. Writer, an AI startup that builds large language models for enterprise customers, claims that its models are cheaper to train and run in part because it trains them using synthetic data. Feeding its models bespoke data sets rather than larger but less curated ones makes the training process quicker (and therefore less expensive). For example, instead of simply downloading Wikipedia, the team at Writer takes individual Wikipedia pages and rewrites their contents in different formats—as a Q&A instead of a block of text, and so on—so that its models can learn more from less. Training is just the start of a model’s life cycle. As models have become bigger, they have become more expensive to run. So-called reasoning models that work through a query step by step before producing a response are especially power-hungry because they compute a series of intermediate subresponses for each response. The price tag of these new capabilities is eye-watering: OpenAI’s o3 reasoning model has been estimated to cost up to $30,000 per task to run. But this technology is only a few months old and still experimental. Farhadi expects that these costs will soon come down. For example, engineers will figure out how to stop reasoning models from going too far down a dead-end path before they determine it’s not viable. “The first time you do something it’s way more expensive, and then you figure out how to make it smaller and more efficient,” says Farhadi. “It’s a fairly consistent trend in technology.” One way to get performance gains without big jumps in energy consumption is to run inference steps (the computations a model makes to come up with its response) in parallel, he says. Parallel computing underpins much of today’s software, especially large language models (GPUs are parallel by design). Even so, the basic technique could be applied to a wider range of problems. By splitting up a task and running different parts of it at the same time, parallel computing can generate results more quickly. It can also save energy by making more efficient use of available hardware. But it requires clever new algorithms to coordinate the multiple subtasks and pull them together into a single result at the end. The largest, most powerful models won’t be used all the time, either. There is a lot of talk about small models, versions of large language models that have been distilled into pocket-size packages. In many cases, these more efficient models perform as well as larger ones, especially for specific use cases. As businesses figure out how large language models fit their needs (or not), this trend toward more efficient bespoke models is taking off. You don’t need an all-purpose LLM to manage inventory or to respond to niche customer queries. “There’s going to be a really, really large number of specialized models, not one God-given model that solves everything,” says Farhadi. Christina Shim, chief sustainability officer at IBM, is seeing this trend play out in the way her clients adopt the technology. She works with businesses to make sure they choose the smallest and least power-hungry models possible.
Four reasons to be optimistic about AI’s energy usage Lire l’article »
In the AI arms race, all the major players say they want to go nuclear. Over the past year, the likes of Meta, Amazon, Microsoft, and Google have sent out a flurry of announcements related to nuclear energy. Some are about agreements to purchase power from existing plants, while others are about investments looking to boost unproven advanced technologies. This story is a part of MIT Technology Review’s series “Power Hungry: AI and our energy future,” on the energy demands and carbon costs of the artificial-intelligence revolution. These somewhat unlikely partnerships could be a win for both the nuclear power industry and large tech companies. Tech giants need guaranteed sources of energy, and many are looking for low-emissions ones to hit their climate goals. For nuclear plant operators and nuclear technology developers, the financial support of massive established customers could help keep old nuclear power plants open and push new technologies forward. “There [are] a lot of advantages to nuclear,” says Michael Terrell, senior director of clean energy and carbon reduction at Google. Among them, he says, are that it’s “clean, firm, carbon-free, and can be sited just about anywhere.” (Firm energy sources are those that provide constant power.) But there’s one glaring potential roadblock: timing. “There are needs on different time scales,” says Patrick White, former research director at the Nuclear Innovation Alliance. Many of these tech companies will require large amounts of power in the next three to five years, White says, but building new nuclear plants can take close to a decade. Some next-generation nuclear technologies, especially small modular reactors, could take less time to build, but the companies promising speed have yet to build their first reactors—and in some cases they are still years away from even modestly sized demonstrations. This timing mismatch means that even as tech companies tout plans for nuclear power, they’ll actually be relying largely on fossil fuels, keeping coal plants open, and even building new natural gas plants that could stay open for decades. AI and nuclear could genuinely help each other grow, but the reality is that the growth could be much slower than headlines suggest. AI’s need for speed The US alone has roughly 3,000 data centers, and current projections say the AI boom could add thousands more by the end of the decade. The rush could increase global data center power demand by as much as 165% by 2030, according to one recent analysis from Goldman Sachs. In the US, estimates from industry and academia suggest energy demand for data centers could be as high as 400 terawatt-hours by 2030—up from fewer than 100 terawatt-hours in 2020 and higher than the total electricity demand from the entire country of Mexico. There are indications that the data center boom might be decelerating, with some companies slowing or pausing some projects in recent weeks. But even the most measured projections, in analyses like one recent report from the International Energy Agency, predict that energy demand will increase. The only question is by how much. Many of the same tech giants currently scrambling to build data centers have also set climate goals, vowing to reach net-zero emissions or carbon-free energy within the next couple of decades. So they have a vested interest in where that electricity comes from. Nuclear power has emerged as a strong candidate for companies looking to power data centers while cutting emissions. Unlike wind turbines and solar arrays that generate electricity intermittently, nuclear power plants typically put out a constant supply of energy to the grid, which aligns well with what data centers need. “Data center companies pretty much want to run full out, 24/7,” says Rob Gramlich, president of Grid Strategies, a consultancy focused on electricity and transmission. It also doesn’t hurt that, while renewables are increasingly politicized and under attack by the current administration in the US, nuclear has broad support on both sides of the aisle. The problem is how to build up nuclear capacity—existing facilities are limited, and new technologies will take time to build. In 2022, all the nuclear reactors in the US together provided around 800 terawatt-hours of electricity to the power grid, a number that’s been basically steady for the past two decades. To meet electricity demand from data centers expected in 2030 with nuclear power, we’d need to expand the fleet of reactors in the country by half. New nuclear news Some of the most exciting headlines regarding the burgeoning relationship between AI and nuclear technology involve large, established companies jumping in to support innovations that could bring nuclear power into the 21st century. In October 2024, Google signed a deal with Kairos Power, a next-generation nuclear company that recently received construction approval for two demonstration reactors from the US Nuclear Regulatory Commission (NRC). The company is working to build small, molten-salt-cooled reactors, which it says will be safer and more efficient than conventional technology. The Google deal is a long-term power-purchase agreement: The tech giant will buy up to 500 megawatts of electricity by 2035 from whatever plants Kairos manages to build, with the first one scheduled to come online by 2030. Amazon is also getting involved with next-generation nuclear technology with a direct investment in Maryland-based X-energy. The startup is among those working to create smaller, more-standardized reactors that can be built more quickly and with less expense. In October, Amazon signed a deal with Energy Northwest, a utility in Washington state, that will see Amazon fund the initial phase of a planned X-energy small modular reactor project in the state. The tech giant will have a right to buy electricity from one of the modules in the first project, which could generate 320 megawatts of electricity and be expanded to generate as much as 960 megawatts. Many new AI-focused data centers under construction will require 500 megawatts of power or more, so this project might be just large enough to power a single site. The project will help meet energy needs “beginning in the early
Can nuclear power really fuel the rise of AI? Lire l’article »
Nvidia is powering a supercomputer at Taiwan’s National Center for High-Performance Computing that’s set to deliver over eight times more AI performance than before.Read More
Nvidia-powered supercomputer to enable quantum leap for Taiwan’s research Lire l’article »
Nvidia and Foxconn announced they are working with the Taiwan government to build an AI factory supercomputer.Read More
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