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Plug a phone into a modern charger and the first 10 minutes are impressive. The next 20 are not. This is not a defect. It’s the connected device protecting itself. As temperature rises during charging, a smartphone’s battery management system reduces the current it will accept, because heat accelerates the chemical degradation that permanently reduces battery capacity. The charger may be capable of delivering more, but the device simply stops taking it. For anyone building products in the portable power category, this creates an uncomfortable gap between specification and experience. A device rated at 25 watts is accurate in the sense that it can deliver 25 watts. Whether it delivers 25 watts for the duration of a charge is a different question, and one the specification does not answer. The specification gap The gap matters commercially because it is invisible at the point of purchase and obvious in use. Consumers compare wattage figures on packaging. They don’t compare thermal curves, because thermal curves are not published publicly. The result is a category where products differentiate on a number that describes peak output rather than sustained output, and where the actual user experience of two products with identical specifications can diverge substantially. This is particularly acute in magnetic wireless charging. Inductive power transfer generates heat at both the transmitting and receiving coils, and the magnetic attachment that makes these products convenient also places the heat source in direct contact with the device it is charging. Convenience and thermal performance are working against each other by design. The industry’s response for the past several years has been materials science. Graphite sheets, thermal interface materials, conductive housings, and heat-spreading layers have all improved how efficiently accumulated heat moves away from the source. Each generation has been incrementally better than the last. But passive dissipation has a structural limitation: it can only move heat that has already been generated, and only as fast as the surrounding air will accept it. In a sealed, pocket-sized enclosure, that ceiling arrives quickly. Improving the materials slows the rate of temperature rise. It does not prevent the temperature rise. Moving from dissipation to removal The alternative is active thermal management, which is standard in stationary electronics and largely absent from portable ones for reasons that are easy to understand. Fans add volume, weight, moving parts, and noise. In a product category defined by portability, each of those is a meaningful cost. At Anker, which manufactures charging and power products, engineering teams spent the past several development cycles working on whether that tradeoff could be made acceptable rather than eliminated. The approach involves several interacting systems: a micro centrifugal fan, dual airflow channels routed to avoid interference with the magnetic array, a three-layer graphene heat-spreading layer, and a control algorithm that modulates fan speed based on real-time temperature and battery state rather than running at a fixed rate. The result is that the Anker MagGo Power Bank 2 Pro has become the world’s fastest and coolest wireless power bank. In internal testing, at 77 °F (25 °C) ambient, the back of the power bank stays below 96.8 °F (36 °C) throughout wireless charging, 21.6 °F (12 °C) below the international standard limit of 118.4 °F (48 °C), for a comfortable grip. Comparable magnetic power banks in the same testing typically reached 113 °F (45 °C) or higher within 20 minutes. The functional consequence is that the connected device does not reach the threshold at which it begins reducing charge acceptance, so 25 watts of Qi2.2 magnetic wireless charging is delivered as a working rate rather than an opening rate. In practice, an iPhone 17 Pro reaches 50% charge in 25 minutes. The Anker MagGo Power Bank 2 Pro’s premium performance in both charging speed and thermal management is certified by SGS, an independent testing and certification company. The same principle applies in reverse. Recharging a power bank generates heat too, which is why devices in this category are often slow to recharge, leaving users with an empty accessory at the moment they need it. Active cooling during input allows the unit to accept 45 watts and reach 80% in 52 minutes. What this suggests about the category There is a broader pattern here worth naming, because it is not unique to charging. When a category improves along a single axis for long enough, the constraint usually migrates somewhere else. Charging spent a decade optimizing power delivery. Power delivery is now, for most practical purposes, solved: the electronics can supply more energy than the receiving device is willing to accept. The binding constraint moved to thermal management, and the industry continued optimizing the axis it had always optimized, because that is the axis the specifications describe. Recognizing when a constraint has moved is difficult precisely because the old metric keeps improving. Wattage figures have continued to climb. Products have continued to get faster on paper. The measurement stayed valid while quietly ceasing to describe the thing users experience. For product organizations, the practical question is whether their specifications still measure the constraint or merely measure the capability. The two align until the constraint shifts and specifications rarely shift with it. The transparency problem A second implication follows from the first. If sustained performance differs meaningfully from peak performance, and if only peak performance is disclosed, then buyers cannot evaluate the products in front of them. This is one reason Anker is adding displays on charging products. The Anker MagGo Power Bank 2 Pro shows real-time power, temperature, battery level, and estimated time remaining. Some of that is user convenience. But some of it is a Anker stating a deliberate position—this category deserves to have the complete and accurate data made transparent to all. Anker expects independent reviewers to test these claims and considers our internal numbers to be the correct outcome. The gap between specification and experience closes faster when the experience is measurable. The Anker MagGo Power Bank 2 Pro will be available in the U.S. on September

In this article, you will learn how to build, track, compare, and register scikit-learn pipelines that integrate large language models using Scikit-LLM and MLflow. Topics…

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. What OpenAI’s latest controversy tells us about the future of math OpenAI says its agents have solved one of the most important open problems in mathematics. Under normal circumstances, that would be a huge milestone. But the announcement has been overshadowed by accusations that OpenAI failed to credit researchers whose AI-assisted work influenced its solution. Whether those accusations are true or not, the episode may mark a turning point in the history of mathematics. AI models now seem essential for making progress on the field’s most important problems, but solving them may demand resources available only to a couple of frontier AI companies. If that’s the future we are headed for, it is unclear how human mathematicians will fit into it. Read on to see how AI could reshape mathematics. —Grace Huckins Batteries just broke another record in the US Battery installations hit a new record in the US in the second quarter of 2026, with 20.2 gigawatt-hours of new capacity coming online. That’s enough to supply the daily electricity needs of 600,000 homes. The surge puts the country on track for another record year, driven by cheaper batteries and an urgent need for more energy storage as renewables are added to the grid. But the boom looks different for grid-scale and residential batteries. Take a closer look at the forces reshaping the US battery market. —Casey Crownhart This entrepreneur is developing agents that can plan ahead Danijar Hafner’s office in San Francisco’s SoMa district sits mostly empty, but what it lacks in decor, it makes up for in robots. Humanoids of various shapes and sizes hang like marionettes from racks that run down the center of the wide-open space. Hafner won’t say too much about his new venture just yet, but describes it as a continuation of his longtime work to enable AI to navigate environments it has not encountered in training. Over the years, the 31-year-old has honed his approach by pitting agents trained within his world models against popular video games. More recently, he’s begun migrating his agents out of the virtual world and into physical reality. Learn more about Hafner’s work teaching AI about our world. —Mat Honan Danijar Hafner is one of the artificial intelligence honorees on our 35 Innovators Under 35 list for 2026. Meet the rest of them here, or explore the full list across the biotechnology, AI, computing and robotics, and climate and energy categories. MIT Technology Review Narrated: data from drones in Ukraine is fueling a new Wild West marketplace Battlefields in Ukraine are littered with the remnants of drones. But behind all that wreckage, there’s a new gold mine for the defense sector: the data those drones generate. Ukraine has begun making millions of data points gathered during tens of thousands of drone flights available to military contractors and commercial companies. It’s a quick way to attract funding and partnerships, but it turns the front line into a model training site, using the chaos of war to create conditions that AI companies struggle to reproduce. As this new industry takes shape, we need a regulatory system that ensures battlefield data isn’t treated like ordinary commercial material. This is our latest article to become an MIT Technology Review Narrated podcast, which we publish each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 OpenAI says it cracked a 90-year-old maths problem in 88 hoursIt used 10,000 AI agents to tackle the Navier-Stokes equations. (CNBC)+ OpenAI claims it’s the first major math problem solved by AI. (Nature)+ But the breakthrough has been overshadowed by a credit controversy. (Axios)+ OpenAI spent millions to win the $1 million math contest. (Quanta) 2 The US has accused six Chinese AI firms of “industrial-scale” theftThey include DeepSeek, Moonshot AI, Alibaba, and Z.AI. (CNN)+ Officials accuse them of stealing America’s AI trade secrets. (Reuters $)+ They allegedly used model distillation to train their systems. (WSJ $)+ Targeting Claude, ChatGPT, Gemini, and Grok, among others. (NBC News) 3 The Pentagon asked OpenAI for an AI model that rarely says noThe military wanted it to have “minimal refusal rates.” (Intercept)+ The Pentagon says US allies can’t keep pace on AI. (Guardian)+ AI firms may soon train on classified military data. (MIT Technology Review) 4 Apple is expected to unveil a $2,000 folding smartphone todayIt would be the iPhone’s biggest design change since its 2007 launch. (Guardian)+ And the first big test for new CEO John Ternus. (NYT $)+ Xiaomi and Huawei launched their own new foldables before the event. (CNBC) 5 Meta’s new AI agent can access apps to send emails and make paymentsMuse autonomously uses apps and websites on people’s behalf. (CNBC)+ Internal tests found it could expose sensitive personal data. (Reuters $)+ AI agents are not your “coworkers.” (MIT Technology Review) 6 Google says it’s “degrading” search in Europe to comply with EU rulesNew results will give more prominence to comparison sites. (Reuters $)+ The changes follow a €460 million EU antitrust fine. (Quartz) 7 Meta ads pushed AI apps that nudified real teensResearchers found 332 ads containing CSAM this year. (BBC)+ They identified several AI-manipulated photos of real children (Ars Technica)+ Apple and Google have missed the UK’s deadline to block child nudity. (Wired $) 8 Border Patrol is using financial data to target Americans for stopsThe predictive-policing program feeds intelligence to local police. (404 Media) 9 New paints could cool buildings on the cheap without electricityThey reflect sunlight and radiate heat back into space. (Economist $) 10 The creepy first trailer for the Sam Altman biopic just dropped Luca Guadagnino’s Artificial will be released in the US on December 25. (Variety)+ Amazon had dropped the film

Throughout the history of the computer chip, engineers have treated waste heat as an inevitable cost of a calculation. Hannah Earley, however, thinks it’s a design choice. Earley, 31, is cofounder and chief technology officer of Vaire Computing, a startup building chips that recycle energy usually thrown away as heat—a strategy known as reversible computing. Ultimately, she thinks, this approach could help make data centers (and our laptops and phones) much more energy efficient.  When conventional computer chips perform calculations, they erase the information they no longer need along the way, dissipating energy as heat in the process. Earley compares the approach to racing through a city only to pump the brakes at every intersection: The car loses momentum and must burn more fuel to accelerate again. Reversible computing aims to keep the momentum going—instead of erasing information from the intermediate steps in a calculation, the circuit retains it, making it possible to run the computation backward and recover some of the energy. While the idea was first proposed more than 50 years ago, it proved impractical to implement with existing transistors and circuits. Earley, though, has completely rethought the hardware needed to make energy recovery work. She designed a patent-pending type of resonator—a microscopic chip component that stores recovered energy for later reuse. “It’s really a glorified pendulum,” she says. Last year, Vaire announced a key breakthrough: a chip with a resonator that recovered more energy than it lost, even after the energy needed to power the component was taken into account. For a subfield that has existed mostly in theory, the result was proof of life. “It’s clear they have something interesting,” says Igor Markov, a researcher in electronic design automation and a former professor at the University of Michigan, Ann Arbor. Still, he says, the technology is quite early stage; the company will need “a series of increasingly realistic and convincing demonstrations to attract the industry support needed for commercialization.”  She gradually became convinced that the connection between information, energy, and heat could change computers forever. Earley’s journey into chip design started sooner than most. She began programming around the age of nine, starting with high-level coding for the web before digging into other programming languages like Perl and Java. She continued progressing to more and more abstract layers of computing, until she got all the way down to transistors. She eventually enrolled in a PhD program at the University of Cambridge under the computational biologist Gos Micklem. She started out studying how materials such as DNA could be used to perform calculations, but a few months in, Micklem sent her the 1999 PhD thesis of Michael Frank, a pioneer in reversible computing. Earley read it once, felt skeptical, read it again, and sat with it for a few weeks. She gradually became convinced that the connection between information, energy, and heat could change computers forever. The fascination completely redirected her PhD work. Earley studied the physical limits of computation and built software that could turn ordinary programs into reversible ones. “Eventually I wouldn’t let her put my name on any of her papers, because I felt that I couldn’t really stand up and give a proper talk about them,” Micklem recalls. “It was her stuff.” After completing her degree in 2021, Earley met Rodolfo Rosini, a technology entrepreneur and investor. The pair cofounded Vaire that same year, and the company has since raised more than $12 million, hired Frank as a senior scientist, and begun turning the vision of reversible computing into real hardware. Innovation, however, doesn’t happen overnight. During the winter of 2022 in Grinnell, Iowa, Earley spent weeks in her now-wife’s basement apartment as the wind chill outside reached roughly −40 °F, covering a whiteboard over and over again with schematics for the core piece of circuitry needed to make reversible logic work. By the time the design finally came together, after the couple had escaped the cold for Las Vegas, it felt less like an aha moment and more like a gradual wave of relief. “I’m not completely out of my depth,” she remembers feeling.  Earley and her colleagues’ next challenge is making their drastically different chip fit into familiar devices and manufacturing systems. She believes that’s where the future lies—not in further refining existing chips but in rebuilding them from the ground up with an eye toward reversibility. “I want to tackle every part of how computers are built,” Earley says, “and rethink it in these terms.” 

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Introducing our 35 Innovators Under 35 list for 2026 What will the next generation of science and technology look like? Our latest Innovators Under 35 list offers a glimpse. Every year, we recognize 35 people from around the world who are doing groundbreaking scientific work and building clever technical fixes for sticky problems. By finding the top young innovators globally and learning what they’re focused on, we aim to give readers a sense of the advances to expect in the years to come. This year’s honorees were selected from 550 nominations, with 44 expert judges helping our editors evaluate the finalists. Each works in one of four categories: biotechnology, AI, computing and robotics, and climate and energy—and has already made clear progress toward their goals. Meet our 35 Innovators Under 35 shaping the future of science and technology. Welcome to the spiderverse, a world measured through webs Counting the creatures around us is critical for conservation, but it’s often a laborious, costly process that still leaves gaps. Environmental DNA, or eDNA, offers a promising alternative by analyzing genetic material shed by living things.  Recently, spiderwebs have emerged as an eDNA goldmine, as they trap material from their arachnid creators, their prey, and bio-detritus like saliva and pollen from nearby plants and animals. Studies found no passive tool matched spiderwebs’ ability to ID vertebrates.  Find out how spiderwebs are unlocking better ways to measure nature. —Stephen Ornes This story is from our latest print magazine, which is all about kids. Subscribe now to receive every issue as soon as it lands. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 How a blacklisted Chinese company kept buying Nvidia’s best AI chipsIts US subsidiary shipped them to firms serving China from elsewhere. (NYT $)+ Belgium has arrested a man accused of stealing chip tech for China. (WSJ $)+ IBM’s new chip tech could extend Moore’s Law. (MIT Technology Review) 2 Mistral has raised a European record of $3.5 billion It’s the biggest equity round for a private European tech firm. (CNBC)+ Mistral is betting on open models while US rivals keep theirs closed. (Reuters $)+ It’s also shifting strategy to focus more on AI infrastructure. (NYT $)+ But its pivot to data centers and services has drawn criticism. (Le Monde) 3 Anthropic formalized proof of Fermat’s last theorem in just 11 daysClaude produced a computer-verified 13-million-line proof. (Nature)+ AI is starting to discover new mathematics. (MIT Technology Review) 4 Europe’s biggest carriers are in talks to build a Starlink rivalThe consortium would create a satellite-to-mobile venture. (Bloomberg $)+ It includes Deutsche Telekom, Orange, Vodafone, and Telefonica. (Reuters $) 5 Tech companies are exploring Patagonia for giant AI data centersDue to its cool temperatures, abundant energy, and new reforms. (Reuters $)+ AI data centers are learning to flex their power use. (MIT Technology Review) 6 Australia plans to let users switch off social media algorithmsA proposed law would impose penalties on platforms that refuse. (BBC)+ Social media is distorting AI progress. (MIT Technology Review) 7 A laser experiment could finally reveal the quantum vacuumIt aims to expose the hidden structure of a vacuum. (New Scientist $) 8 Spacecraft are getting a new type of armorNew lightweight materials could protect satellites from debris. (Economist $) 9 NASA’s “quiet supersonic” jet is set for acoustic testing this yearThe tests will determine whether it produces a sonic thump, not boom. (Gizmodo) 10 The largest-ever map of space has arrived—and you can play with itThe 5.6-trillion-pixel map covers about three-quarters of the sky. (Wired $) Quote of the day “The people who have developed AI are very, very smart, but they’re high IQ, stupid people. They’re terrible marketers.”  —Sen. John Kennedy (R-La.) tells NBC’s “Meet the Press” that the AI industry has work to do to rebuild momentum among voters. One more thing We did the math on AI’s energy footprint. Here’s the story you haven’t heard. AI’s integration into our lives is the most significant shift in online life in more than a decade. Hundreds of millions of people now regularly turn to chatbots for help with homework, research, coding, or to create images and videos. But what’s powering all of that? To find out, we spoke to two dozen experts, evaluated different AI systems and prompts, pored over hundreds of pages of projections and reports, and questioned top model makers about their plans. The result is an unprecedented comprehensive look at how much energy the AI industry uses. Our analysis reveals what AI’s carbon footprint looks like now and where it’s headed as adoption skyrockets. It also shows that the common understanding of AI’s energy consumption is full of holes. Here’s what we discovered about AI’s energy demands—and what’s coming next. —James O’Donnell and Casey Crownhart We can still have nice things A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.) + A long-lost coral reef that “defeated time” has been rediscovered off Benin’s coast.+ Cookware captains Le Creuset have launched a stellar limited-edition Star Trek collection.+ An 11–year-old boy has won a Guinness World Record for being the youngest museum curator.+ The trailer for Nathan Fielder’s secrecy-shrouded Elizabeth Holmes documentary just dropped, and I still can’t quite believe it’s not a parody.

In this article, you will learn the key differences between Chain of Thought and Tree of Thoughts prompting, and how each reasoning framework is applied…

Google DeepMind has released AlphaGenome Atlas, a catalogue of precomputed predictions for the molecular effects of every possible single-nucleotide variant in the human genome. That is roughly 9 billion single-letter changes. The release also introduces the AlphaGenome Variant Impact (AVI) score, a single number that ranks variants by predicted impact, plus per-variant feature attributions and a genome-wide motif collection. The resource ships as a free web portal for academic use, through the AlphaGenome API, and as a skill in Google Antigravity. Is it deployable? Partially. The Atlas is queryable today for non-commercial research via the portal and API, and commercial access on Google Cloud is listed as “coming soon”. The underlying AlphaGenome model is already available for academic use on GitHub and for commercial use on Model Garden on Google Cloud. From one model to a genome-wide map AlphaGenome, released in June 2025, predicts how a DNA variant changes molecular processes such as gene expression and RNA splicing. It has been used widely, but always one variant or one region at a time. The Atlas changes the unit of work. DeepMind team ran AlphaGenome across all 9 billion single-nucleotide variants and stored the outputs, producing a 1-petabyte dataset. This is more than 30 times larger than the AlphaFold Database, which holds over 200 million protein structure predictions. Testing 9 billion mutations in a lab is not feasible, and running a large model on demand for each candidate variant is slow for genome-scale studies. A lookup table with attached interpretation removes both bottlenecks. What is inside the Atlas The Atlas exposes 4 linked resources: Molecular effect predictions: thousands of predictions per variant, covering multiple aspects of gene regulation across hundreds of human and mouse cell types and tissues. AVI score: a single impact number per variant. It combines AlphaGenome’s regulatory predictions with AlphaMissense, DeepMind’s model for protein-altering variants, so it works in both coding regions (about 2% of the genome) and non-coding regions (the other 98%). AVI feature attributions: each score is decomposed into additive contributions from interpretable categories such as chromatin accessibility, splicing, and conservation, so a researcher can see which process a variant is predicted to disrupt. DNA sequence motifs: a compendium of over 2,500 recurrent short sequences, with genomic locations, including transcription factor binding sites. DeepMind team reports that the AVI score delivers best-in-class performance across many variant pathogenicity and rare disease benchmarks. The technical report carries the benchmark details. AlphaGenome Atlas explainer Tap a DNA letter. See what AlphaGenome Atlas does with it. The Atlas already holds a precomputed prediction for every one of the 9 billion single-letter changes in the human genome. This demo shows what a single lookup returns. 1. Mutate one base Click any letter to swap it. Coding bases get an AlphaMissense protein term; non-coding bases rely on AlphaGenome alone. coding (2%)non-coding (98%) AlphaGenome Variant Impact (AVI) 0.00 No variant selected. RNA splicing 0.00 Gene expression 0.00 Chromatin accessibility 0.00 Conservation 0.00 Protein (AlphaMissense) 0.00 Illustrative numbers. The bars mimic how the Atlas splits one AVI score into additive feature attributions. Real values come from the Atlas portal, not this widget. 2. How the Atlas is built Step 1Precompute effects Step 2Collapse to AVI Step 3Attribute the score Step 4Map the motifs 9B variants→ AlphaGenome→ thousands of molecular effects→ AVI score→ attributions + 2,500+ motifs 0single-nucleotide variants scored 0dataset size, 30x the AlphaFold Database 0more non-coding associations found in 54,000+ UK Biobank genomes 0recurrent DNA motifs catalogued Source: Google DeepMind, AlphaGenome Atlas announcement, Sept 8, 2026. Not for clinical use.Built by Marktechpost Early results from external collaborators Three research groups used the Atlas before launch, and their results anchor the announcement: Rare disease: Working with the GREGoR Consortium, Laura Covill and Anne O’Donnell-Luria at the Broad Institute used the AVI score to reprioritize variants that earlier analyses had overlooked. The score surfaced a variant in DNM1, a gene strongly linked to epileptic encephalopathy. The underlying AlphaGenome predictions showed the mechanism: the variant created an incorrect splice site that abnormally extended the resulting protein. Experimental screens validated the prediction and found nearby variants with similar effects. Population genetics: Gareth Hawkes, a Medical Research Council fellow at the University of Exeter, applied the Atlas to whole-genome data from over 54,000 UK Biobank participants. Grouping rare variants by predicted molecular effect uncovered 22% more non-coding associations than would otherwise be detectable, pinpointing regulatory variants that drive circulating levels of proteins such as PLA2G7 and EGLN1. Filtering to the 1% of non-coding variants that the Atlas rates most impactful, Hawkes identified 19 genomic regions associated with body mass index. Regulatory grammar: Julia Zeitlinger and Melanie Weilert at the Stowers Institute for Medical Research used the motif resource to separate transcription factors that only change DNA accessibility from those that also switch genes on and off. Key Takeaways AlphaGenome Atlas precomputes molecular effects for all 9 billion human single-nucleotide variants in a 1-PB dataset. The AVI score merges AlphaGenome and AlphaMissense into 1 rankable number for coding and non-coding variants. Feature attributions and 2,500+ motifs explain why a variant scores high, not just that it does. Collaborators found a validated DNM1 splice variant and 22% more non-coding associations in 54,000+ UK Biobank genomes. Free portal and API for academic use today; Google Cloud commercial access is coming soon; no clinical approval. Check out the Paper, DeepMind announcement, the Google Technical Post, and the Atlas Portal. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post Google DeepMind Releases AlphaGenome Atlas With Precomputed Molecular Effect Predictions and AVI Scores for 9 Billion Human DNA Variants appeared first on MarkTechPost.

NVIDIA has announced CUDA Rust, a push to make Rust a first-class language for writing GPU kernels. Rust code could already launch CUDA kernels, but the kernel body usually had to be written elsewhere. CUDA Rust closes that gap with two NVlabs open-source projects: cuda-oxide for the SIMT model and cutile-rs for the newer Tile model. Both compile Rust kernels natively and use Rust’s ownership rules to reject aliasing bugs at compile time. Is it deployable? Partially. cutile-rs is published on crates.io, runs on stable Rust 1.89+, and is already used in Hugging Face’s Grout inference engine and in mistral.rs. cuda-oxide is early alpha. The both projects are in alpha phase and not confirmed for production. Why Rust for the GPU Kernel The systems layer of AI, from inference engines to drivers and agent runtimes, is increasingly written in Rust. NVIDIA’s Nova Linux driver is in Rust, NVIDIA Dynamo has a Rust core, and NVTX has Rust bindings. The GPU kernel was the exception. The two tracks mirror the two programming models CUDA already offers. SIMT is the model used in CUDA C++ and numba-cuda: you describe what one thread does and launch thousands of them. Tile is the newer model, also available in C++ and Python: you describe what one tile of data does, and the Tile IR compiler handles thread mapping and memory layout. NVIDIA recommends Tile first, with SIMT for explicit thread and memory control. Planned inter-language interop means choosing Rust will not lock developers out of C++ or Python. The SIMT Track: cuda-oxide cuda-oxide is a custom rustc codegen backend. It routes #[kernel] functions through Rust MIR, the community Pliron IR framework, and LLVM IR down to PTX, then hands everything else to the standard backend. NVIDIA wrote the GPU dialects on top of Pliron. Requirements: Linux, a GPU with compute capability 8.0 or later, CUDA 12.x or newer, clang with libclang, and a pinned nightly toolchain (nightly-2026-04-03). cargo oxide doctor checks the setup and cargo oxide new scaffolds a vector addition program, with host and device code in one file. The safety argument sits in the kernel signature. Inputs a and b are ordinary shared slices. The output c is a DisjointSlice<f32>, a type that gives each thread exclusive access to its own element. A plain &mut [f32] would need every thread to hold the same mutable borrow, which Rust refuses. c.get_mut(idx) returns an Option, so out-of-bounds access becomes a handled branch. A #[launch_contract] attribute declares the block shape, and the generated prepare_vecadd method validates the launch configuration against it before the safe launch runs. The Tile Track: cutile-rs cutile-rs works one level higher. Each tile block runs the kernel body once as a single logical thread over one sub-tensor, and the compiler decides how many real GPU threads back it. The #[cutile::module] macro embeds the kernel’s AST in the host binary and JIT-compiles it through CUDA Tile IR when the kernel is first launched. Requirements are lighter: compute capability 8.0 or later, CUDA 13.3, stable Rust 1.89 or newer, and Linux, with no nightly and no custom LLVM. Setup is cargo new, then cargo add cutile. The host-side .partition([128]) call does 3 jobs. It gives each tile exclusive ownership of its 128-element chunk, fixes the grid at 1,024 / 128 = 8 tiles, and supplies the const tile width B. Input tensors use -1 as a dynamic dimension resolved at launch. The generated launcher takes ownership of all tensors and returns them when the GPU finishes. Nothing executes until .sync_on(&stream); everything before it is a lazy description recorded in one chain. What the Compiler Catches Passing the SIMT kernel’s output buffer as one of its own inputs fails with error[E0502]: cannot borrow c_dev as mutable because it is also borrowed as immutable. The same aliasing on the Tile side fails with error[E0382]: use of moved value: z. cuda-oxide checks each launch call; cutile-rs’s ownership follows tensors across the launch boundary, which NVIDIA calls the stronger guarantee. Tile exposes no shared memory or thread indexing to misuse. SIMT keeps that control, but shared memory in cuda-oxide currently requires unsafe. Key Takeaways CUDA Rust adds 2 native GPU kernel tracks in Rust: cuda-oxide (SIMT) and cutile-rs (Tile). cuda-oxide compiles Rust MIR through Pliron and LLVM to PTX; it needs a pinned nightly. cutile-rs runs on stable Rust 1.89+ with CUDA 13.3 and JIT-compiles via CUDA Tile IR. Both reject buffer aliasing at compile time using Rust’s borrow checker and ownership. cutile-rs already powers Grout and mistral.rs; neither project is production-ready yet. Check out the Technical details here. Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter. Wait! are you on telegram? now you can join us on telegram as well. Need to partner with us for promoting your GitHub Repo OR Hugging Face Page OR Product Release OR Webinar etc.? Connect with us The post NVIDIA Announces CUDA Rust with cuda-oxide (SIMT) and cutile-rs (Tile) for Compile-Time-Safe GPU Kernels appeared first on MarkTechPost.