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Decentralized AI Training Turns Homes Into Data Hubs

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Artificial intelligence harbors an enormous energy appetite. Such constant cravings are evident in the hefty carbon footprint of the data centers behind the AI boom and the steady increase over time of carbon emissions from training frontier AI models.

No wonder big tech companies are warming up to nuclear energy, envisioning a future fueled by reliable, carbon-free sources. But while nuclear-powered data centers might still be years away, some in the research and industry spheres are taking action right now to curb AI’s growing energy demands. They’re tackling training as one of the most energy-intensive phases in a model’s life cycle, focusing their efforts on decentralization.

Decentralization allocates model training across a network of independent nodes rather than relying on one platform or provider. It allows compute to go where the energy is—be it a dormant server sitting in a research lab or a computer in a solar-powered home. Instead of constructing more data centers that require electric grids to scale up their infrastructure and capacity, decentralization harnesses energy from existing sources, avoiding adding more power into the mix.

Hardware in harmony

Training AI models is a huge data center sport, synchronized across clusters of closely connected GPUs. But as hardware improvements struggle to keep up with the swift rise in the size of large language models, even massive single data centers are no longer cutting it.

Tech firms are turning to the pooled power of multiple data centers—no matter their location. Nvidia, for instance, launched the Spectrum-XGS Ethernet for scale-across networking, which “can deliver the performance needed for large-scale single job AI training and inference across geographically separated data centers.” Similarly, Cisco introduced its 8223 router designed to “connect geographically dispersed AI clusters.”

Other companies are harvesting idle compute in servers, sparking the emergence of a GPU-as-a-Service business model. Take Akash Network, a peer-to-peer cloud computing marketplace that bills itself as the “Airbnb for data centers.” Those with unused or underused GPUs in offices and smaller data centers register as providers, while those in need of computing power are considered as tenants who can choose among providers and rent their GPUs.

“If you look at [AI] training today, it’s very dependent on the latest and greatest GPUs,” says Akash cofounder and CEO Greg Osuri. “The world is transitioning, fortunately, from only relying on large, high-density GPUs to now considering smaller GPUs.”

Software in sync

In addition to orchestrating the hardware, decentralized AI training also requires algorithmic changes on the software side. This is where federated learning, a form of distributed machine learning, comes in.

It starts with an initial version of a global AI model housed in a trusted entity such as a central server. The server distributes the model to participating organizations, which train it locally on their data and share only the model weights with the trusted entity, explains Lalana Kagal, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) who leads the Decentralized Information Group. The trusted entity then aggregates the weights, often by averaging them, integrates them into the global model, and sends the updated model back to the participants. This collaborative training cycle repeats until the model is considered fully trained.

But there are drawbacks to distributing both data and computation. The constant back-and-forth exchanges of model weights, for instance, result in high communication costs. Fault tolerance is another issue.

“A big thing about AI is that every training step is not fault-tolerant,” Osuri says. “That means if one node goes down, you have to restore the whole batch again.”

To overcome these hurdles, researchers at Google DeepMind developed DiLoCo, a distributed low-communication optimization algorithm. DiLoCo forms what Google DeepMind research scientist Arthur Douillard calls “islands of compute,” where each island consists of a group of chips. Every island holds a different chip type, but chips within an island must be of the same type. Islands are decoupled from each other, and synchronizing knowledge between them happens once in a while. This decoupling means islands can perform training steps independently without communicating as often, and chips can fail without having to interrupt the remaining healthy chips. However, the team’s experiments found diminishing performance after eight islands.

An improved version, dubbed Streaming DiLoCo, further reduces the bandwidth requirement by synchronizing knowledge “in a streaming fashion across several steps and without stopping for communicating,” says Douillard. The mechanism is akin to watching a video even if it hasn’t been fully downloaded yet. “In Streaming DiLoCo, as you do computational work, the knowledge is being synchronized gradually in the background,” he adds.

AI development platform Prime Intellect implemented a variant of the DiLoCo algorithm as a vital component of its 10-billion-parameter INTELLECT-1 model trained across five countries spanning three continents. Upping the ante, 0G Labs, makers of a decentralized AI operating system, adapted DiLoCo to train a 107-billion-parameter foundation model under a network of segregated clusters with limited bandwidth. Meanwhile, popular open-source deep learning framework PyTorch included DiLoCo in its repository of fault-tolerance techniques.

“A lot of engineering has been done by the community to take our DiLoCo paper and integrate it in a system learning over consumer-grade internet,” Douillard says. “I’m very excited to see my research being useful.”

A more energy-efficient way to train AI

With hardware and software enhancements in place, decentralized AI training is primed to help solve AI’s energy problem. This approach offers the option of training models “in a cheaper, more resource-efficient, more energy-efficient way,” says MIT CSAIL’s Kagal.

And while Douillard admits that “training methods like DiLoCo are arguably more complex, they provide an interesting trade-off of system efficiency.” For instance, you can now use data centers across far apart locations without needing to build ultrafast bandwidth in between. Douillard adds that fault tolerance is baked in because “the blast radius of a chip failing is limited to its island of compute.”

Even better, companies can take advantage of existing underutilized processing capacity rather than continuously building new energy-hungry data centers. Betting big on such an opportunity, Akash created its Starcluster program. One of the program’s aims involves tapping into solar-powered homes and employing the desktops and laptops within them to train AI models. “We want to convert your home into a fully functional data center,” Osuri says.

Osuri acknowledges that participating in Starcluster will not be trivial. Beyond solar panels and devices equipped with consumer-grade GPUs, participants would also need to invest in batteries for backup power and redundant internet to prevent downtime. The Starcluster program is figuring out ways to package all these aspects together and make it easier for homeowners, including collaborating with industry partners to subsidize battery costs.

Back-end work is already underway to enable homes to participate as providers in the Akash Network, and the team hopes to reach its target by 2027. The Starcluster program also envisions expanding into other solar-powered locations, such as schools and local community sites.

Decentralized AI training holds much promise to steer AI toward a more environmentally sustainable future. For Osuri, such potential lies in moving AI “to where the energy is instead of moving the energy to where AI is.”

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Waymo is scaling fast. Here’s what the fleet data shows.

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Waymo’s commercial robotaxi ramp-up looks expansive, both in geographic reach and in ridership. And by almost every measure, it is — until you pay attention to where the bulk of those robotaxis are actually showing up.

The numbers over the past two years reflect the kind of commercial rollout you’d expect from a deep-pocketed company like Waymo, which spun out of Google and still counts Alphabet as its majority owner. In September 2024, Waymo was operating in just three cities — Phoenix, Los Angeles, and San Francisco. Today, it offers robotaxi service in 15 U.S. cities, with most of those commercial launches occurring in the past year. Ridership has skyrocketed, too with Waymo now averaging 500,000 paid robotaxi rides every week.

But a closer look at its fleet shows a company concentrating its efforts in just two states. About 80% of Waymo’s roughly 4,000 robotaxis are in California and Texas, and Texas is where the action is now: Waymo’s fleet there has grown by nearly half in the past three weeks, fueled by a new Chinese-built minivan the company is betting will help it scale, even as tariffs drive up its costs.

The other 800 or so vehicles are spread across cities in other states, including Arizona and Florida, another burgeoning hotspot. Most are the familiar white Jaguar I-Pace electric SUVs, but a growing share are that new minivan — a modified Zeekr RT that Waymo has branded “Ojai.”

Waymo’s focus on California is no surprise. It is headquartered in Silicon Valley, and much of its early testing and development work was conducted there. Plus, a segment of the population there is inclined to adopt tech at its earliest stages.

The recent growth in Texas is more interesting. Waymo has increased its Texas fleet by 49% in the past three weeks, according to state vehicle registrations and data from the Texas Autonomous Vehicle Fleet Tracker. As of September 24, Waymo had 1,102 autonomous vehicles registered in the state.

Waymo first launched in commercial service in Austin through a partnership with Uber in March 2025, letting riders hail its robotaxis through the uber app. Since then, the company has expanded its robotaxi services in Dallas, Houston, and San Antonio.

Waymo’s Texas fleet remained relatively static for most of the summer, inching up from about 600 vehicles in June to more than 700 by the end of August. Then came September, when the he fleet surged, driven by an influx of new Ojai minivans, which now make up about a third of Waymo’s Texas fleet.

Expect that share to grow.

The Ojai robotaxi, equipped with Waymo’s sixth-generation self-driving system, is supposed to help Waymo reach mass scale. Its interior is built to withstand heavy use, and it comes with an upgraded rider interface and Google’s Gemini AI, which acts as an in-car assistant for riders.

Strip away that technology, though, and the Ojai is a minivan made by Zeekr, a brand owned by China’s Geely Holding Group (which also owns Volvo). The Ojai is built on Zeekr’s SEA-M platform, a shared vehicle platform designed for uses like robotaxis and delivery vans. The base Zeekr vehicles are shipped to the U.S. without any Chinese connected-car technology on board. Once they arrive, the vehicles are sent to Waymo’s Arizona factory, where they are outfitted with Waymo’s self-driving system.

The Ojai is supposed to drive down costs and ultimately help Waymo reach profitability. For now, though, tariffs are cutting any savings. Under current U.S. trade policy, vehicles built in China face steep import tariffs, raising Waymo’s costs for every Ojai it brings into the country.

Waymo appears willing to absorb that cost. New York-based research firm MoffettNathanson, which tracks Ojai imports using detailed shipping records, said in its September report that Waymo is on track to bring 5,100 of the vehicles into the U.S. by the end of the year.

Where will all those Ojai vehicles go? Texas is certainly on the list. But Florida, where Waymo operates in three cities, and newer markets like Las Vegas will likely see an influx as well.

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Nexterity wants to automate the hard, dangerous part of pipefitting

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Lindsey Elliott is big into bolts. At last year’s Bolting Symposium — the 13th annual — she said the highlight was playing “Bolting Bingo” against the many self-proclaimed “torque dorks” who were in the room.

A former engineer and planner at ExxonMobil, Elliott has spent years thinking about how to improve the infrastructure that moves oil, gas, and petrochemicals. Bolts are what she landed on. Specifically, the bolts that connect sections of pipe (technically called “bolted flange joints”). These bolts require hard, physical work to loosen and tighten, and are the source of many pipefitters’ injuries. Like many trade industries, there’s also a labor shortage.

“Those people get really tired when they’re asked to work 12 hours a day for three months in a row,” she told TechCrunch. “I’ve talked to pipefitters across the U.S. across Canada, and just repeatedly have been told North American pipefitting productivity is notoriously low.”

The solution Elliott came up with at her startup Nexterity, which is one of the Startup Battlefield 200 selected to participate in TechCrunch Disrupt, is a remote-controlled robot that can handle this part of the job. It’s the kind of idea that could fundamentally change this particular blue collar job if widely adopted, making the workers safer and more productive.

Think: more dork, less torque.

The robot comes in two main pieces that fit around a pipe. Powered by batteries, the robot can slide across the pipe once it’s attached and quickly loosen and tighten four bolts at a time.

Elliott said Nexterity has developed a few different configurations of the robot to fit different standard pipe sizes, but they’re all small enough to fit in a Pelican case and be carried by a single worker. That makes them easy to deploy to new sites — flexibility that is crucial to Nexterity’s business model of treating the robot like rental construction equipment.

Elliott said she arrived at this particular design as a result of conversations she’s had over the last few years — not just at the Bolting Symposium, but also with members of the Pressure Vessels & Piping Division of the American Society of Mechanical Engineers.

“What I learned from the people, the torque dorks per se,” she said, “is that 80% of our pipes are between two to eight inches in diameter, or what they call NPS2 to NPS8. And so when you have that much repeatability, you have a fantastic candidate for automation.”

It’s a pretty straightforward idea, but one that Elliott believes has a lot of upside.

“I think it would shock a lot of people just how big this market is,” she said. “I mean, day to day, most of us don’t think about piping infrastructure, but even water, wastewater, water treatment, food and beverage, mining, nuclear, any kind of green and sustainable manufacturing facility — they all use the same kind of piping.”

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EPICS in IEEE Team Builds Portable Educational Platform

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In Guadalajara, Mexico, many high schools have motivated teachers and talented students with an interest in science, technology, engineering, and mathematics, but they lack access to advanced tools such as robotics laboratories. The resources shortfall limits the students’ opportunities for hands-on learning on cutting-edge applications.

A team from ITESO, Universidad Jesuita de Guadalajara, is working to change that. Through the EPICS in IEEE initiative, a multidisciplinary group of 15 engineering students, faculty advisors, and IEEE Guadalajara Section volunteers developed RoboMeshA. The portable, self-contained educational platform brings robotics and AI experiences into classrooms.

EPICS is administered by IEEE Educational Activities and funded by the IEEE Robotics and Automation Society.

A mobile laboratory

Rather than requiring a school to build a dedicated computer lab or install complex software, RoboMeshA operates as an all-in-one mobile learning network.

“RoboMeshA brings robotics and AI to students who don’t have access to specialized facilities or preinstalled software,” says team member Fernando Vidal Luna, an IEEE student member and a mechatronics engineering major at ITESO.

Students connect directly to the platform from a user-friendly web browser. They can interact with the robot manually or use its control modes to watch it move and detect and avoid obstacles.

“The project combines mechanical design, embedded systems, control engineering, computer vision, and AI into a single robotic system that functions as a mobile learning laboratory,” says faculty advisor Jorge A. Lizarraga.

The team says young students are interested in technology, programming, and robotics but don’t have an opportunity to work with systems that combine mechanics, electronics, software, and control.

“RoboMeshA allows students to see how all these disciplines work together in a tangible and understandable way,” says team member José S. González, who also is studying mechatronics engineering.

The team has built two units and is developing a modular coupling framework to expand the system’s capabilities for research and classroom demonstrations. The structured system design approach connects independent software components while minimizing internal dependencies, enabling four RobotMeshA robots to operate together.

Overcoming design challenges

The team faced significant hurdles while designing the project.

“One key challenge involved the robot’s structural design,” Luna says. “It wasn’t only about making a chassis where all the components fit and the design had sufficient stability, rigidity, and weight distribution. It was also about ensuring that the electronics were protected while still being accessible for maintenance, testing, and modifications.”

“It was also challenging to design a platform that could be used by students with different levels of experience,” González adds.

“When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful.” —Luis Fernando Luque-Vega

The team partnered with the CETI Colomos and Prepa ITESO high schools to validate the platform in classroom settings.

“We wanted the first interactions with the robot to be simple and intuitive,” González says, “such that students could simply power the robot, connect to its network, and begin interacting with it, rather than having to deal with software installation, extensive configuration, or troubleshooting.”

Engineering with social impact

Many of the students who participated were from ITESO’s applied professional projects program. The experience offered them practical training in project management, system integration, and user-centered design.

The team also presented a research paper and a project poster in May at the Engineering Congress of the Jesuit University System.

“Seeing a design move from a digital model to a physical system was invaluable,” González says. “Working with students from different backgrounds taught us to listen to end users and design for their actual needs.”

Project lead Luis Fernando Luque-Vega, an IEEE member, says he’d like the venture to serve as a blueprint for engineering education.

“I hope RoboMeshA is adopted by schools, universities, and IEEE student branches across Mexico and internationally as a model for integrating technical innovation with community engagement,” Luque-Vega says.

By pairing engineering talent with community service, initiatives such as EPICS in IEEE demonstrate how targeted support can turn academic concepts into real-world solutions.

“When students realize the technology they develop can inspire others and improve lives, engineering becomes far more meaningful,” Luque-Vega says.

For more information on service-learning opportunities, visit the EPICS website.

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