Tag Archives: Nvidia

Every Robot You’ll Ever Own Has 3 Separate Brains: Nvidia VP explains how AI Thinks at BEYOND Expo 2026

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A robot on a factory floor may look self-contained, but Deepu Talla says its intelligence is distributed across a hidden chain of machines. At BEYOND Expo 2026, the NVIDIA executive broke robotics down into a deceptively simple formula: three computers. One handles the heavy lifting of training the robot brain, another tests that brain in simulation, and a third lives inside the physical robot, making decisions in real time.

It is a framework that helps explain why robotics has moved so slowly, and why the field suddenly feels ready to accelerate. In language that cut through the usual keynote fog, Talla argued that AI in the physical world plays by harsher rules than chatbots or image tools. A text model can be 95 percent right and still be useful. A robot moving through a warehouse, a street, or a hospital has to perform with a completely different standard. In human terms, it is a little like splitting intelligence into learning, dreaming, and reacting, then assigning each function to a different machine.

That first machine is where the robot’s intelligence is forged. Talla described it as the computer used to train the robot brain, the heavy compute layer where models absorb data, patterns, and behaviors at massive scale. This is where a machine learns how the physical world works, long before it ever enters one. If that sounds abstract, the second computer makes it easier to picture. This is the simulation layer, the place where a robot rehearses reality in a safer, faster, cheaper environment, running through scenarios again and again until its behavior becomes reliable enough to trust.

The third computer is the one that actually lives inside the robot. It is the real-time brain, the system that has to perceive the world, make sense of it, and respond instantly. This is where Talla’s argument becomes especially sharp. In digital AI, a model can get close and still be useful because a human can smooth over the rough edges. In robotics, the rough edges are where accidents happen. A machine moving through a factory, a roadway, or a hospital has to work with a far tighter tolerance for error, because the physical world offers fewer second chances.

That is also why NVIDIA sees robotics as far bigger than a niche category. Talla pointed out that almost 80 percent of the world’s GDP sits in physical industries like manufacturing, logistics, retail, and transportation. These are sectors where intelligence has to leave the screen and interact with objects, spaces, and people. NVIDIA’s role, in his telling, is to provide the underlying architecture for that shift. The company may not build robots itself, but it wants to supply the stack beneath them, from training infrastructure and simulation tools to the compute that powers action on the edge.

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Microsoft RTX Dev Box Has 1,000 Holes, All of Them Intentional

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The economics of AI development have been quietly changing how developers think about their hardware. Cloud GPU bills compound fast when you’re iterating through a model dozens of times a day, and every fine-tuning run or inference loop on a remote server adds to a cost that has no natural ceiling. The push toward local AI compute isn’t just about performance. It’s about moving from a metered relationship with infrastructure to one you own outright and sit in front of.

Microsoft announced the Surface RTX Spark Dev Box at Build 2026 as its answer to that shift. It’s a compact mini PC powered by the NVIDIA RTX Spark superchip, the same ARM-based silicon debuting in the Surface Laptop Ultra, and it arrives on a developer’s desk already configured and ready to run serious AI workloads without touching a cloud endpoint.

Designer: Microsoft

The device’s most distinctive quality isn’t anything in the spec sheet. It’s the body itself, a 3D-printed anodized aluminum chassis perforated with exactly 1,000 vents arranged across its surface in a precise grid. Those vents are functional, the aluminum chassis doubles as the passive heatsink, managing a 100W sustained thermal envelope without a traditional cooling tower. They’re also a deliberate reference: 1,000 vents for 1,000 teraflops, or 1 petaflop, of AI compute. It’s a design that’s equally a statement and an engineering solution, and nothing else on a desk looks remotely like it.

That petaflop is delivered by NVIDIA’s RTX Spark, which combines a 20-core Grace CPU with a Blackwell RTX GPU carrying 6,144 CUDA cores, connected via NVLink-C2C. The 128GB of unified memory shared dynamically between the processor and GPU is what separates this from a high-end gaming box. That memory ceiling is what makes loading a 120-billion-parameter model possible without partitioning it or shunting inference work to the cloud.

The software side ships pre-configured and aimed precisely at the developer who doesn’t want to spend time on setup. WSL2 with native GPU passthrough and full CUDA support comes pre-installed and ready to use, alongside Visual Studio Code, GitHub Copilot, and PowerShell 7. Windows settings are tuned specifically for development work rather than general consumer use, a small but meaningful distinction when your machine runs long overnight training jobs and needs stability rather than a live tiles grid.

Connectivity covers HDMI, Ethernet, USB-C, USB-A, and a headphone jack, nothing exotic, but a port set that covers what a desk-based development machine actually uses. The machine runs under 100W during intensive workloads, which means it can sustain training jobs and agentic pipelines without the kind of thermal throttling that eventually frustrates sustained use.

For a machine announced without a price, the Surface RTX Spark Dev Box is already doing a specific kind of work. It positions local AI inference as a fixed cost rather than a running expense, and it makes that argument in a chassis that doesn’t look like any other mini PC on the market. A 3D-printed aluminum grid covered in a thousand deliberate holes is an odd form for a developer tool, but it makes the machine’s purpose unmistakably legible from across the room. Availability is expected later in 2026 in the US through Microsoft’s online store.

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ASUS ProArt Just Closed the Gap Between a Laptop and a Workstation

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Creative professionals have been carrying a compromise for years. The laptop powerful enough for serious work tends to be too heavy or too loud, and the one thin and light enough for a day bag can’t handle the work. Purpose-built workstations solve the performance side but solve nothing about portability. The gap between the two has been a persistent frustration, not a deliberate choice most people would make.

ASUS is addressing that directly at Computex 2026, where the ProArt P16, ProArt P14, and ProArt Mini PC were unveiled as the first ASUS devices powered by NVIDIA’s RTX Spark superchip. The same ARM-based chip combining a 20-core Grace CPU with a Blackwell RTX GPU and up to 128GB of unified memory runs across all three products, making the performance difference between a laptop and a desktop largely a matter of form factor rather than capability.

Designer: ASUS

The ProArt P16 and P14 are the portable entries, and they arrive 13% thinner and 16% lighter than the previous P16 generation. The P16 weighs 1.77kg at 12.9mm, and the P14 comes in at 1.48kg and 13.9mm. Both are CNC-manufactured in Nano Black and Neo White finishes, and carry 99.9Wh batteries for all-day runtime, a detail that matters when the work is intensive enough to drain power quickly. The machines don’t sacrifice weight for performance or performance for weight.

The display on both laptops is ASUS Lumina Pro OLED, calibrated to Delta E < 1 color accuracy, Pantone Validated, and certified for VESA DisplayHDR True Black 1000. Peak HDR brightness reaches 1,600 nits, which is more than three times what the previous ProArt generation could manage. A 120Hz variable refresh rate, 0.2ms response time, and an anti-reflection coating that cuts glare by 65% complete a panel that keeps color decisions accurate regardless of the lighting conditions a shoot or edit session happens to land in.

Under the hood, RTX Spark’s 1 petaflop of AI compute and unified memory pool change what locally processed work looks like. Rendering a 90GB-plus 3D scene, editing 12K 4:2:2 video, generating 4K AI video, or running a 120-billion-parameter language model locally are tasks that previously needed significantly bigger machines. Adobe is rebuilding Photoshop and Premiere specifically for RTX Spark to deliver 2x faster AI and graphics performance, and a three-month Creative Cloud subscription ships with the ProArt laptops.

The ProArt Mini PC extends the same logic to the desk. At 150 × 150 × 51mm, it fits anywhere a small speaker would and carries up to 128GB of unified memory, 10GbE wired networking, M.2 PCIe Gen 5 expansion, and up to 140W of thermal headroom for sustained demanding workloads. A single RTX Spark-powered box of that size, running AI renders or local large language models around the clock, is a genuinely different proposition for a small studio or home setup than what was available previously.

All three products sit within a broader ASUS ProArt ecosystem that integrates displays, peripherals, creator apps, and AI workflow software into a connected end-to-end experience. ProArt P16, P14, and Mini PC are expected to be available in fall 2026 in select regions, with additional configurations announced closer to launch.

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The Surface Laptop Ultra Just Got NVIDIA’s Answer to Apple Silicon

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The laptop has always been a machine of compromises. Workstation-class performance typically arrived in thick chassis with short battery life and fan noise audible from across a room. Getting genuine power in a form factor thin and light enough to carry without a second thought has been largely Apple’s territory, a problem it’s been solving with its own ARM-based chips while Windows machines played catch-up.

NVIDIA is changing that calculus for Windows with RTX Spark, an ARM-based superchip that fuses a 20-core Grace CPU with a Blackwell RTX GPU carrying 6,144 CUDA cores, connected by NVIDIA’s NVLink-C2C chip-to-chip interconnect. Microsoft built the Surface Laptop Ultra around it from the silicon up, designing the machine and the chip in concert, producing what it describes as the most powerful Surface Laptop ever built.

Designer: Microsoft, NVIDIA

The reason ARM architecture matters for laptop design is power efficiency. Compared to x86 chips, ARM-based designs deliver significantly more performance per watt, and that ratio determines what’s physically possible in a chassis. RTX Spark laptops are engineered to be as slim as 14mm and as light as 3 pounds, proportions that previously excluded any serious dedicated GPU from the equation entirely.

The Surface Laptop Ultra lands at under 18mm thick and under 4.5 pounds, housed in CNC-machined aluminum in Platinum and Nightfall finishes. The 15-inch mini-LED PixelSense Ultra touchscreen reaches up to 2,000 nits of peak HDR brightness with a 3:2 aspect ratio and 262 pixels per inch, making it the brightest display Microsoft has ever shipped on a Surface. A full port set, including HDMI, USB-C, USB-A, SD card, and headphone jack, rounds out a machine designed for professional use.

RTX Spark’s most defining architectural choice is unified memory, where up to 128GB of RAM is shared dynamically between the CPU and GPU. A 3D rendering job, a video edit, and a locally running AI model can all draw from that same pool simultaneously, without the bottlenecks discrete memory architectures create. That arrangement enables 1 petaflop of AI compute, enough to run 120-billion-parameter models entirely on the device.

The full CUDA software stack runs natively on RTX Spark, which matters directly for creative professionals. Adobe is rebuilding Photoshop and Premiere from the ground up for the chip, targeting 2x faster AI and graphics performance. On the creative side, RTX Spark handles 12K video editing, renders 90GB-plus 3D scenes using NVIDIA OptiX, and generates 4K AI video, tasks that previously required a dedicated workstation to complete without serious compromise.

NVIDIA describes RTX Spark as the most efficient PC chip ever built, a statement aimed squarely at Apple Silicon’s grip on the high-end creative laptop market. That efficiency is also what allows the Surface Laptop Ultra’s all-new thermal system to sustain heavy workloads without the throttling and fan noise that defined previous Windows machines in this tier. Microsoft’s own engineers worked across mechanical, thermal, materials, and industrial design disciplines simultaneously, treating the chassis and the chip as a single system.

All-day battery life holds even while running on battery power, and the compact charger is small enough to fit in a jacket pocket. The Surface Laptop Ultra and additional RTX Spark-powered devices from ASUS, Dell, HP, Lenovo, and MSI are expected in fall 2026. For a platform that has long asked users to choose between portability and capability, the arrival of an ARM PC chip in NVIDIA’s hands changes the terms of that conversation considerably.

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Nvidia wants robots to learn before executing tasks by watching 44,000 hours of human video

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CES 2026 was crowded with humanoids doing simple household tasks such as folding laundry or stacking up the dishwasher. One thing I was sure of seeing this influx of robots at the world’s biggest tech event, was that such service bots are going to be the next big thing invading our households in the near future.

Staying with that thought, the robotics industry, for now, faces the biggest challenge in teaching robots to operate in the messy real world. The unstructured environment means robots need massive amounts of data to learn. Gathering and structuring that data is the costliest thing in robotics and perhaps the biggest impediment, slowing the entire development process.

Designer: DreamDojo

NVIDIA believes it has created a workaround. The company has released DreamDojo, an open-source “world model,” which intends to help robots learn intuitive physics to interact in the physical world by seeing humans do it first. So, instead of relying on painstaking programming or teleoperating robots, Nvidia DreamDojo would allow robots to train on 44,000 hours of egocentric human video, which shows humans handling tools, assembling objects, and doing laundry.

NVIDIA terms this open-source world model as the “largest dataset to date for world model training.” The dataset is called DreamDojo-HV (Human Video) and comprises exactly 44,711 hours of footage, which includes 6,015 unique tasks and more than a million trajectories. This works in two independent phases and is billed by Nvidia to be 15 times larger and about 96 times more skill-packed. It is also believed to include 2000 times more scenes than ever seen in the previous largest datasets for world model training.

Two-phase robotic course for being human

Of course, collecting robot-specific data is the biggest bottleneck in the industry. By simplifying that with abundant human video, Nvidia is trying to make learning convenient and cheaper for robotic companies betting on humanoids. For me, this possibility of learning through seeing before touching physical objects is compelling. And for its execution is divided into two phases: Pre-Training and Post-Training.

Firstly, it pre-trains on large-scale human video using what Nvidia says is “latent actions.” Since human videos do not provide joint torque labels or motor commands, Nvidia has trained a “700-million-parameter spatiotemporal Transformer” to extract “proxy actions” from visual changes between frames, allowing the model to “treat any human video as if it came with motor commands attached.” Secondly, it post-trains on a specific robot body with “continuous robot actions.” The idea is to separate physical understanding from hardware control, so that the robot learns the rules of the physical world first and then adapts them to need and limb requirements.

Real-time dreaming

With its world model designed to teach robots to watch humans first, Nvidia is suggesting to us that the best and fastest way to scale humanoids isn’t more robot data. It is probably their exposure to more human experience. Considering this, it’s imperative to note that this is not the first world model. Many have been devised before, but they have been considerably slower at achieving the outcome. NVIDIA has been able to clock up the pace by distilling DreamDojo to run at 10.81 frames per second in real time for over a minute. DreamDojo HV has been demonstrated across humanoid platforms like GR-1, G1, AgiBot, and YAM robots, the company says, and has shown what it calls “realistic action-conditioned rollouts” across diverse environments and object interactions.

From what I see, if DreamDojo can work as the press information reveals, it could make life easier for startups and robotic teams with limited resources to collect a large robot-specific dataset and use it to teach their robots. But before more use case scenarios trained on the Nvidia world model show up, I am skeptical how they will perform in every changing real-world condition, which are not absolutely the same at any two moments.

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OpenAI is building their own AI Chips to take on Nvidia’s Chip Dominance

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In a strategic move that feels like it’s straight from an Aaron Sorkin movie, OpenAI has started crafting its own AI chip, a custom creation designed to tackle the heavy demands of running its advanced models. The company, known for developing ChatGPT, has partnered with Broadcom and Taiwan Semiconductor Manufacturing Company (TSMC) to roll out its first in-house chip by 2026, Reuters reports. While many giants might build factories to keep all chip manufacturing in-house, OpenAI opted to shelve that multi-billion-dollar venture. It’s instead using industry muscle in a way that’s both practical and quietly rebellious.

Why bother with the usual suppliers? OpenAI is already a massive buyer of Nvidia’s GPUs, essential for training and inference—the magic that turns data into meaningful responses. But here’s the twist: Nvidia’s prices are soaring, and OpenAI wants to diversify. AMD’s new MI300X chips add to the mix, showing OpenAI’s resourcefulness in navigating a GPU market often plagued by shortages. Adding AMD into this lineup might look like a mere “supply chain insurance,” but it’s more than that—this move exhibits OpenAI’s reluctance to put all its eggs in one pricey basket. Sort of like Apple developing its own Apple Intelligence while leaning on ChatGPT whenever necessary.

Broadcom is helping OpenAI shape the chip, along with a data transfer capability that’s critical for OpenAI’s needs, where endless rows of chips work in synchrony. Securing TSMC, the world’s largest contract chipmaker, to produce these chips highlights OpenAI’s knack for creative problem-solving. TSMC brings a powerhouse reputation to the table, which gives OpenAI’s experimental chip a significant production edge—key to scaling its infrastructure to meet ever-growing AI workloads.

OpenAI’s venture into custom chips isn’t just about technical specs or saving money; it’s a tactical play to gain full control over its tech (something we’ve seen with Apple before). By tailoring chips specifically for inference—the part of AI that applies what’s learned to make decisions—OpenAI aims for real-time processing at a speed essential for tools like ChatGPT. This quest for optimization is about more than efficiency; it’s the kind of forward-thinking move that positions OpenAI as an innovator who wants to carve its own path in an industry where Google and Meta have already done so.

The strategy here is fascinating because it doesn’t pit OpenAI against its big suppliers. Even as it pursues its custom chip, OpenAI remains close to Nvidia, preserving access to Nvidia’s newest, most advanced Blackwell GPUs while avoiding potential friction. It’s like staying friendly with the popular kid even while building your own brand. This partnership-heavy approach provides access to top-tier hardware without burning any bridges—a balancing act that OpenAI is managing with surprising finesse.

(Representational images generated using AI)

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Lenovo Legion Pro 7i Gen 9 (2024) Laptop Review: Uncompromising Power at a Fair Price

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PROS:


  • Unbeatable performance for its price tag

  • Beautiful and vibrant 16-inch 2K 240Hz screen

  • A wide variety of ports

CONS:


  • Bulky and aggressive design

  • A bit pricey without discounts

RATINGS:

AESTHETICS
ERGONOMICS
PERFORMANCE
SUSTAINABILITY / REPAIRABILITY
VALUE FOR MONEY

EDITOR'S QUOTE:

Although a heavyweight in actual weight and price, the Lenovo Legion 7i Gen 9 delivers almost everything gamers and content creators need without breaking the bank.

Microsoft has been very aggressive with its push of AI on new Windows computers, represented by its now omnipresent CoPilot key. New laptops have just been launched touting AI capabilities that revolve largely around the usual culprits like content generation or summarizing content, but they’re not the only new kids on the block either. A new breed of gaming laptops is also on the rise, advertising some AI tricks to optimize their performance. That also means a refresh of popular models that promise even smoother performance and pack more power, but those always come at some cost. Rarely will you find a design that delivers the power that gamers need at a more affordable price point, which is the proposition that the 2024 Lenovo Legion Pro 7i Gen 9 (16IRX9H) is making, so we naturally had to put it to the test to see if it holds up in practice.

Designer: Lenovo

Aesthetics

Common gaming laptops often look like tanks, and the Legion Pro 7i (2024) is sadly no different. It’s not rugged by any means, sporting a sleek and sharp appearance, but it’s thick, heavy, and sharp at the edges. It has an aggressive look to it, though coupled with some RGB lights, it does have a bit of a cyberpunk flair. While it doesn’t shout to the world that it’s a gaming laptop, it doesn’t try to deny its identity either.

It’s also not that different from its Gen 8 predecessor, so there’s practically nothing that sets it apart visually. On the one hand, it establishes a familiarity with the Legion Pro line, so buyers will know what to expect. On the other hand, however, it also feels like it’s lagging behind when it comes to aesthetics, especially when Lenovo has quite a few interesting and distinctive designs available.

Overall, the Lenovo Legion Pro 7i Gen 9 looks pretty plain on the outside. It isn’t as obnoxious as other gaming laptops that show off all their kaleidoscopic lighting, but it isn’t subtle either. You might feel a bit conscious bringing it to the workplace or meeting (unless you work at a game studio), but it will probably only get a few passing looks. Fortunately, most gamers will be willing to overlook this aspect if they’re getting the performance that they’re actually paying for.

Ergonomics

At 4.93 lbs (2.24kg) and 17.6mm (0.69in), there is no mistaking the Legion Pro 7i Gen 9 for a thin and lightweight notebook. Again, this is your expected dimensions for a gaming laptop, so many gamers won’t be so bothered by it. But if you’re a creator and a gamer who find yourself moving around a lot, you best prepare your back and shoulders for some workout.

Fortunately, actually using the laptop turned out to be a more enjoyable experience, from the bright and vibrant screen to the responsive and comfortable keyboard. That keyboard is a bit notable in how it sufficiently spaces out the keys and still has room for a numeric keypad as well as a regular T-shaped cursor key arrangement. The latter actually extends a bit lower than the rest of the keys, which has the effect of pushing the touchpad to the left just a little. Definitely not enough to make the button-less surface painful to use.

As we’ll get to later, the Legion Pro 7i has a wide selection of ports, and they’re placed in a way that really takes into account how most people use laptops these days. The left side has a USB-A and a USB-C port, while the opposite side gets a USB-A along with a 3.5mm headphone/mic jack. This gives easy access to accessories you’ll connect and disconnect often, like a gaming mouse or your phone. The back has connections like two USB-A ports, one USB-C port, HDMI, and Ethernet, practically the ones you’ll use to “dock” the laptop to more stationary peripherals, making cable management a bit easier. Whether it’s gaming on the go or maybe even working in the office, the Lenovo Legion Pro 7i Gen 9 is designed with ease of use and comfort in mind.

Performance

As a somewhat high-end gaming laptop, the 2024 Legion Pro 7i carries some of the best hardware in the market today. Sure, it might not have an NPU-toting processor, but the Intel Core i9-14900HX is definitely as or even more capable than an “Ultra” chip. The NVIDIA GeForce RTX 4080 is just one step lower than the top-of-the-line card, but it’s more than enough for both gaming and content creation. Our review unit came with 32GB of DDR5 RAM and 1TB of storage, which is to say it’s not wanting in any aspect. With both benchmarks and actual usage alike, the Legion Pro 7i Gen 9 performed impressively, yielding consistent high frame rates in games even high settings. It’s also a testament to the laptop’s cooling system that it’s able to squeeze out as much performance consistently, though it naturally did get warm to the touch and the fans were quite audible.

Visual quality is important for gamers, but it is an even more critical aspect for content creators who need color accuracy. This is one area where the Legion Pro 7i Gen 9 surpasses the previous generation, adding support for NTSC, Adobe RGB, and DCI-P3 color gamuts. Not only does this make colors really pop, it also means that the 16-inch 2K screen is now able to support the needs of creatives, making the gaming laptop more of an all-around high-performance tool. And with extremely thin bezels, you experience a better immersion into that colorful world, especially when the decent bottom-firing speakers complete the audiovisual experience.

This heavy laptop carries a 99.99Whr battery, the largest allowable on planes, and its uptime is as much as you’d expect. Normal use, which is a mix of multimedia and browsing, can net you around 7 hours, but heavy gaming makes that figure nosedive to just two or less. Lenovo compensates for this with a “Super Rapid Charge” technology that can fully charge the Legion Pro 7i in around 80 minutes, presuming you’re using the hefty 330W power brick with a proprietary connector. You can also charge the laptop via USB-C if you have a 140W charger, but Lenovo doesn’t ship one in the box.

All in all, the Lenovo Legion Pro 7i Gen 9 performs so well that we have very little to complain about it. There’s very little bloatware aside from Lenovo’s own tools, as well as those from Nahimic for audio and Tobii for the webcam. Yes, it’s heavy and it burns through a battery quickly, but that’s also expected from high-end gaming laptops. It’s a rather powerful package made even more enticing by its bang-for-buck value.

Sustainability

Before we get to that, however, a word has to be said about Lenovo’s actions to leave a more positive impact on the planet. Though the Legion Pro 7i Gen 9 might look plain to the naked eye, it actually uses quite a number of sustainable materials for its body. It uses recycled aluminum as well as magnesium, giving the laptop both durability as well as a premium touch. The bottom, in particular, is made with 50% recycled aluminum, while the cover frame utilizes 30% post-consumer recycled polymers.

The laptop is also designed to be upgradable, at least as far as RAM is concerned. Repairs and other upgrades, however, require a bit more work, so it comes up short of getting a high score. Given how gaming laptops are more likely to wear out components faster than regular laptops, repairability should be the next priority for Lenovo’s designers and engineers.

Value

There’s no getting around the fact that the Lenovo Legion 7i Gen 9 is a bit pricey, starting at around $2,420 all the way to $2,850 for the highest configuration. And that’s with discounts already! On its own, that price tag might feel quite burdensome, until you consider that many laptops on this tier ask for more and deliver less.

With this sub-$3000 gaming laptop, you’re getting a near-perfect configuration that is useful not just for gaming but even for content creation as well. It’s not perfect, of course, and we wished it had a longer battery life given its weight, but you’d also be hard-pressed to find a similar experience on designs that won’t require you to pay even more with not as much performance gains.

Verdict

With PC gaming on the rise again, the number and variety of gaming laptops have also seen an uptick. While the need for power has never changed, gamers have become more conscientious not just about price value but design value as well. Carrying the design DNA of its predecessor, the Lenovo Legion Pro 7i Gen 9 (16″, 2024) looks sleek yet deceptively simple, belying the power it carries inside. But more than just unrelenting performance, it brings a well-rounded set of features that give gamers, creators, and any other user a powerful tool for a relatively fair price.

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