Lucid’s European robotaxi deal now has a driver. Bolt announced today that its fleet of at least 25,000 autonomous Lucid vehicles will run Nvidia’s Alpamayo Stack self-driving software, starting with a data-collection program in Madrid in 2027.
That gives Lucid two robotaxi customers on two different autonomy systems: Nuro’s software for Uber’s Gravity robotaxis in the US, and now Nvidia’s for Bolt in Europe.
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Germany-based robotics firm Agile Robots reportedly plans to produce up to 2,000 humanoid robots next year through a fresh partnership with Nvidia, targeting universities and corporate research labs.
The collaboration was announced at Nvidia’s GTC conference in Berlin. Agile CEO Zhaopeng Chen told Reuters the pair are set to begin production of the Agile ONE S and ONE SN humanoid robot models by the end of 2026, expecting to manufacture between 1,000 and 2,000 units. Stanford University and ETH Zurich are among potential customers.
The Agile ONE SN, with the N standing for Nvidia to denote the chipmaker-specific configuration, combines Agile’s humanoid hardware with Nvidia’s Jetson Thor onboard computing platform and Isaac GR00T robotics AI model. It features five-fingered hands, cameras and other sensors to support dexterous manipulation and interaction with the physical environment.
Standing 1.4 metres tall and weighing around 40 kilograms, the robot is designed as a smaller alternative to full-sized humanoids. Nvidia said the robot intends to simplify the integration of hardware, simulation, training and deployment tools on a single platform, “cutting setup time from weeks to days”.
Spencer Huang, Nvidia’s product director for robotics, told Reuters the standardised configuration could save researchers two or three months of work procuring separately purchased components. “If we can bring researchers something that’s already 80% of the way there, it accelerates their development,” he said.
Nvidia said Agile’s ONE SN is scheduled to become available in early 2027, as competition intensifies among European humanoid developers.
ChatGPT moment
Alongside the partnership, the chipmaker also revealed its expanded robotics development platform, Nvidia Isaac, equipped with new tools for training and deploying AI-powered machines. The expanded platform includes Isaac OS, an open-source runtime framework, and Isaac Lab 3.0, a robot learning framework designed to train and refine robot skills. The company also introduced new reference workflows and simulation resources to speed up development.
“General-purpose robotics is advancing at extraordinary speed — the ChatGPT moment for robotics is fast approaching,” noted Jensen Huang, CEO of Nvidia, adding the platform would give developers “the models, data, simulation tools and computing systems to bring physical AI to the world’s largest industries”.
The chipmaker added European robotics players including UK-based Humanoid, French start-up Hugging Face and Swedish firm ABB Robotics are applying its technologies including Nvidia Isaac to industrial autonomous machines.
The post Agile Robots, Nvidia target up to 2,000 humanoids in 2027 appeared first on Mobile World Live.
Chi gioca su PC conosce bene quella fastidiosa situazione in cui un videogioco appena installato richiede diversi minuti per preparare gli shader, oppure presenta improvvisi scatti anche quando la scheda grafica sarebbe perfettamente in grado di gestirlo. Il problema riguarda il modo in cui vengono compilati gli shader, piccoli programmi che la GPU utilizza per elaborare luci, ombre, materiali ed effetti visivi, e come vi avevamo anticipato qualche giorno fa, ora è disponibile una novità che va a risolvere proprio questo problema.
Con l'arrivo dei nuovi driver NVIDIA GeForce 621.01, questo inconveniente potrebbe diventare molto meno frequente grazie al supporto per Advanced Shader Delivery, una tecnologia sviluppata da Microsoft che cambia il modo in cui gli shader vengono preparati e distribuiti ai computer.
Normalmente, ogni PC deve compilare gli shader in base alla propria configurazione hardware e software, e questa è un'operazione che richiede tempo e che, quando avviene durante una partita, può provocare improvvisi picchi di utilizzo della CPU e fastidiosi rallentamenti, conosciuti come lo stuttering. Advanced Shader Delivery sposta buona parte di questo lavoro nel cloud.
INTERVIEW: Nokia chief technology and AI officer Pallavi Mahajan and Nvidia SVP of telco Ronnie Vasishta told the Mobile World Live Podcast their AI-RAN partnership is showing tangible progress, as they announced close to 20 operators are lined up for lab and commercial trials of the technology.
In an exclusive interview to outline progress of their efforts, Vasishta and Mahajan highlighted the fact it had only been 12 months since the two companies announced a joint AI-RAN partnership, which included a $1 billion investment from Nvidia into the Finnish vendor.
A year on, today (21 October) at Nvidia’s GTC 2026 event in Berlin the duo announced almost 20 operators globally have committed to AI-RAN collaboration, including 10 from EMEA. Mahajan said these operators are at different stages, lined up for both lab and commercial trials.
In 2027 trials are expected from BT Group, Deutsche Telekom and Elisa, with the latter preparing for commercial deployment.
In the US, T-Mobile has completed what was claimed as the world’s first AI-RAN call on a live network, while in France Orange is introducing new voice and intelligence services for enterprises based on the technology.
Speed of light
Vasishta told MWL it was hard to overhype development based on the progress it had seen over the past year, and the number of trials and deployment is only set to grow.
“A year ago, or two years ago for sure, I think people would have scoffed at the idea. Nokia talking about operator commercialisation with AI-RAN, I think, that would have been perhaps seen as somewhat sceptical.”
Mahajan concurred, describing the pace of development as occurring “at the speed of light” with the game changer centred on being able to outline the true “value of what AI brings in terms of making RAN efficient” such as significant spectrum efficiency gains.
“In the next few years, the distinction between the networks and computing infrastructure will just blur,” she said, inevitably opening up a host of operator opportunities across applications beyond 5G and 6G.
Vasishta added Nvidia had started on the journey of software-defined RAN eight years ago and the inclusion of Nokia in 2025 had accelerated this dramatically.
“We see all operators around the world are created differently. They service different markets, they have different DNA, but overall, all of them have gravitated towards this concept now of AI RAN.”
Click here to listen to the full interview.
The post Nvidia, Nokia claim AI-RAN hype is justified as momentum grows appeared first on Mobile World Live.
With Nvidia’s GTC Berlin event in full swing, the company’s telecoms boss Ronnie Vasishta and Nokia chief technology and AI officer Pallavi Mahajan sit down with the Mobile World Live Podcast in an exclusive interview to reveal the next stage of the pair’s AI-RAN journey, as they team up with European heavyweights to turn networks into distributed intelligence platforms.
Listen on Spotify
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00:00 — AI-RAN explained
7:20 — Operator trials
11:00 — Beyond edge hype
15:05 — AI-RAN economics
17:45 — Sovereign AI
21:10 — Commercialisation and 6G
The post Podcast: Exclusive – Nokia, Nvidia advance AI-RAN at GTC appeared first on Mobile World Live.
RTX Spark is NVIDIA's new local AI-focused Windows PC platform, combining Blackwell RTX graphics and Grace CPU technology into a single processor that brings secure LLMs, high-end creative workflows, and gaming performance to laptops and desktops. With that, NVIDIA positions RTX Spark as a new generation of Windows PCs, with Surface Laptop Ultra as the flagship device.
OEMs are also on board, with RTX Spark laptops launching alongside Microsoft's Surface from brands like HP, Dell, ASUS, and MSI. Our team at Windows Central has followed NVIDIA's efforts closely since its inception and has since gone hands-on with its wave of launch devices. This is everything we know so far about RTX Spark and what we expect to see in the future.
NVIDIA officially entered the Windows PC race this week, and its arrival brings a formidable new competitor to the premium AI PC market. With its RTX Spark platform, NVIDIA is bringing its Grace Arm CPU architecture, Blackwell GPU technology and extensive software ecosystem together in a new generation of Windows laptops. Microsoft’s Surface Laptop Ultra is among the first systems to showcase this collaboration, leveraging NVIDIA’s tech and putting it squarely in competition with Intel, AMD, Qualcomm and even Apple for high-performance local AI and creation-optimized laptops.
For Microsoft, the partnership expands the capabilities of Windows on Arm considerably, particularly for local AI development, content creation and other demanding professional workloads. Starting at $2,599.99, Surface Laptop Ultra is clearly a premium offering, but its combination of processing horsepower, large, unified memory capacity, and premium industrial design gives it an interesting position in the market.
My personal testing suggests NVIDIA and Microsoft have delivered a compelling platform with strong AI throughput, graphics performance and competitive CPU compute. Just as importantly, the Surface Laptop Ultra offers a glimpse of how AI development and local AI agent workflows could become more practical on a portable Windows PC.
At the heart of Surface Laptop Ultra is NVIDIA’s RTX Spark N1X SoC, combining up to 20 Grace Arm CPU cores with a Blackwell GPU featuring up to 6,144 CUDA cores. Configurations support up to 128GB of unified memory, shared between CPU and GPU. The Surface Laptop Ultra model I tested has this full-fat configuration with an eye-watering $5,899.99 price tag (thanks, memory crunch), though there are lower-cost base configs as noted previously, offering an 18-core CPU with a 5,120-core GPU and 24GB of unified memory.
NVIDIA and Microsoft
Regardless, this architecture is particularly powerful for running on-device AI. Rather than being constrained by the dedicated graphics memory found in conventional laptops with discrete GPUs, RTX Spark can make a much larger pool of memory available to GPU-accelerated applications. This allows larger language models, longer context windows and demanding inference workloads to run locally. It’s similar in design to AMD’s Ryzen AI Max series, aka Strix Halo, in that regard, but with a more potent GPU engine on board.
NVIDIA’s CUDA ecosystem is another important advantage. Developers can leverage familiar tools including TensorRT, PyTorch and llama.cpp, with a base software foundation that also extends into NVIDIA-powered workstations and data center infrastructure.
For organizations building AI applications, this offers a solid development path. Models and workflows can be prototyped locally before moving to larger systems, reducing cloud compute and token expense while allowing sensitive data to remain on-device. (See: Around the corner: Agentic AI PCs that cut token costs.)
Microsoft has also optimized Windows 11 for RTX Spark, with improvements to workload scheduling, power management, memory allocation and application compatibility. These platform-level investments were essential to making NVIDIA’s Arm architecture practical for everyday Windows computing, and I was able to put some of them to the test in the few days I’ve had the machine on hand.
Microsoft has given RTX Spark an appropriately premium home with this machine. The Surface Laptop Ultra features a 15-inch PixelSense Ultra mini-LED touch display with up to 2,000 nits of claimed peak HDR brightness, a 120Hz refresh rate and excellent potential for creative work requiring high accuracy and wide gamut coverage.
Dave Altavilla
The machine’s aluminum chassis is less than 18 mm thick, and it weighs in at under 4.5 pounds. It’s not an ultraportable per se, but considering the horsepower on board, Microsoft has struck a reasonable balance between portability and performance.
The design is refreshingly understated, with clean lines and rigid aluminum construction. My initial hands-on impressions have been particularly positive, thanks to the keyboard’s tactile response, smooth and responsive haptic touchpad, and bright, high-contrast display.
The larger touchpad should be especially welcome for users navigating complex applications, while the touchscreen adds flexibility for reviewing documents, annotating designs, and interacting directly with creative content.
Connectivity options are also very good, relative to other premium laptops. Microsoft includes three USB-C ports, HDMI, USB-A, an SD card reader and a headphone jack. One USB-C connection features Microsoft’s new magnetic charging mechanism, combining the convenience and safety of a breakaway power connector with the versatility of a standard USB-C port.
Microsoft has also designed the cooling system with large fans and significant thermal capacity, while making the machine’s internal SSD removable and offering good internal serviceability in general. These are practical considerations for professional users and IT departments concerned with reliability, maintenance and long-term ownership.
Battery life and sustained performance under prolonged workloads are important aspects Microsoft focused on for the overall experience, particularly for a machine designed to deliver workstation-class capabilities away from a desk. More on this shortly.
RTX Spark also supports locally running autonomous AI agents, including one that I tested out called Hermes, which can execute multi-step workflows, access files and interact with applications. Its potential impact spans software development, research, document analysis, business automation and many other disciplines. Hermes provides the agent framework, while supported models leverage RTX Spark’s CUDA-accelerated inference and unified memory.
For businesses, local agents can keep sensitive data on-device and reduce cloud inference costs, provided they’re configured appropriately. However, granting agents access to files, code execution and corporate systems introduces security risks. Microsoft’s Execution Containers and NVIDIA’s OpenShell aim to provide isolation and access controls, though their effectiveness depends on proper enterprise policy implementation.
Hermes running Qwen3.8-27B locally on Surface Laptop Ultra, generating a benchmark report.
Dave Altavilla
To test this capability, I ran Hermes with the Qwen3.8-27B language model locally on the Surface Laptop Ultra. I asked it to extract benchmark charts from an Excel spreadsheet and create a formatted Word report with performance commentary. Hermes autonomously generated and executed Python scripts, processed the spreadsheet and assembled the document in approximately five minutes. Color me impressed.
That’s a respectable turnaround for a task requiring several manual steps. My results demonstrate how local AI agents can streamline reporting and analysis without cloud-hosted model inference. Human review remains essential, but this was a promising demonstration of practical, real-world agentic AI on a Windows laptop.
My team’s preliminary AI benchmarks highlight the advantages of NVIDIA’s RTX Spark architecture. However, we’re still digging deeper into AI-enabled use cases and performance.
In UL Procyon’s AI Text Generation benchmark using Phi 3.5, a family of lightweight, open-source small language models (SLMs) released by Microsoft, the Surface Laptop Ultra scored 2,703, compared with 2,027 for Lenovo’s Intel Core Ultra X9 388H-powered IdeaPad Pro 5, an advantage of approximately 33%.
Dave Altavilla
The difference was even more substantial in MLPerf’s Flux 2 Klein 4B image generation workload, where the Surface Laptop Ultra delivered 26.97 images per minute versus 9.96 for the Lenovo machine, or roughly 2.7 times the throughput.
These results demonstrate the platform’s potential for accelerating local inference, image generation, agentic workflows and other AI workloads. Actual application performance will depend on model selection, software optimization and available system memory, though NVIDIA does provide a nice memory partitioning tool in the NVIDIA App for Windows.
RTX Spark’s Blackwell GPU delivers impressive graphics performance, extending the Surface Laptop Ultra’s capabilities beyond AI development and everyday creation or productivity tasks.
Dave Altavilla
In 3DMark Steel Nomad Lite, the Surface Laptop Ultra achieved 98.29 FPS, more than double the 46.07 FPS of Lenovo’s Core Ultra X9-powered IdeaPad Pro 5. In the ray-tracing-focused 3DMark Speed Way test, it delivered 30.31 FPS versus 19.72 FPS for the ASUS ROG Flow Z13, powered by AMD’s Ryzen AI Max+ 395 and Radeon 8060S graphics.
Dave Altavilla
Actual gaming performance was similarly strong. In Cyberpunk 2077, the Surface Laptop Ultra averaged 71.05 FPS, ahead of the ROG Flow Z13’s 63.76 FPS. However, the AMD system narrowly led in The Talos Principle 2, delivering 89.5 FPS versus the Surface’s 84.5 FPS at 1080p medium settings.
CPU performance for tasks like 3D rendering also holds up well. In Cinebench 2026, the Surface scored 6,787 in the multi-threaded test, edging out the AMD-powered ROG Flow Z13’s 6,546. Notably, the Surface retained 99% of its multi-threaded performance on battery power as well.
For creative professionals, native Arm applications including Blender, DaVinci Resolve and Adobe Photoshop offer opportunities to leverage RTX Spark’s potent GPU. However, Windows on Arm compatibility remains a consideration. Microsoft’s Prism translation supports many x86 applications, but specialized software, drivers and certain games (like EA anti-cheat-supported titles currently) may still encounter limitations.
Dave Altavilla
Battery endurance was respectable for the Surface Laptop Ultra, at 14 hours and 35 minutes in my PCMark video playback testing, though competing mainstream thin-and-light laptops lasted considerably longer. The Surface Laptop Ultra and RTX Spark operate within a different power envelope, however, more akin to a beefier gaming laptop, rather than a svelte 2 – 3-lb business machine.
The Surface Laptop Ultra marks an important milestone for NVIDIA’s expansion into Windows PCs and Microsoft’s ambitions for high-performance Arm-based computing.
My initial testing shows the RTX Spark-powered Surface Laptop Ultra delivers exceptionally strong AI acceleration, competitive multi-threaded CPU performance and impressive graphics horsepower. Combined with Microsoft’s premium industrial design, extensive connectivity and I/O options, and Windows platform integration, RTX Spark makes a convincing first impression.
Starting at $2,600, this is a specialized, premium system, and mainstream business users will find more economical alternatives to be sure. However, for developers (even vibe coders), creative professionals and organizations evaluating local AI workflows, the Surface Laptop Ultra offers a compelling combination of performance, beautiful industrial design, portability and local AI capability, especially with its higher-tier memory configurations.
NVIDIA has arrived in the Windows PC market with serious technology, and Microsoft’s Surface Laptop Ultra is an impressive first showcase of what this new partnership can deliver.
Rapid AI adoption left workers cleaning up a flood of errors and low-quality work
Employers around the globe are grappling with the fallout from rushing to adopt fast-changing artificial intelligence tools.
Meta has scaled back plans to axe staff and use more AI agents after the company found employees were spending more and more time cleaning up technical and security errors, Reuters reported. And after thinning its workforce, especially middle managers, in a 2023 efficiency push, the company is expanding its managerial ranks again, LinkedIn posts from workers show.
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© Composite: The Guardian/Getty Images
© Composite: The Guardian/Getty Images
© Composite: The Guardian/Getty Images
Mercedes-Benz has signed a definitive production agreement with British self-driving startup Wayve to build its “AI Driver” into future Mercedes vehicles, with integration starting within the next two years.
It’s the first time Wayve’s end-to-end driving AI is going into a production car in the premium segment, and it deepens a relationship that already includes Mercedes investing in Wayve’s $1.5 billion Series D round earlier this year.
more…
As ações da Apple [$AAPL] encerraram o pregão de hoje na Nasdaq cotadas a US$343,99, um novo recorde histórico de fechamento. O recorde anterior havia sido registrado na última sexta-feira (16/10).
Hoje, os papéis da empresa subiram 1,32% e, com isso, o valor de mercado da Apple chegou a US$5,02 trilhões.
Vale notar que o recorde de hoje é apenas o de fechamento. A máxima intradiária (o maior valor atingido durante o pregão) não foi superada nesta sessão, pois permanece a registrada em outro dia.
A Apple segue na segunda posição entre as empresas mais valiosas do mundo. A líder continua sendo a NVIDIA [$NVDA], cujas ações subiram 2,54% hoje, cotadas a US$221,97, com valor de mercado de US$5,36 trilhões. Em terceiro lugar, a uma distância considerável, está a Alphabet [$GOOG], que recuou 1,62%, com ações a US$330,95 e market cap de US$4,047 trilhões.
NVIDIA's RTX Spark is almost here. Laptops powered by the chip launch on October 16, 2026, but reviews of the chip are already out.
Reviews of the first wave of RTX Spark laptops work a bit differently than some other product launches. Today, those who were given access to RTX Spark laptops can share their experience with the chip itself, but thoughts on specific RTX Spark laptops cannot be shared until October 16, 2026.
While that is different than what we normally see, it's not that surprising. RTX Spark is not just a new chip akin to a new processor from AMD, Intel, or Qualcomm. It is a new platform for NVIDIA, a major addition to Windows on Arm computing, and the mark of a new silicon partner for Windows.
Note that reviewers of the RTX Spark were limited to sharing details about the performance and benchmarks of the processor. Dedicated GPU performance varies based on the laptop it's paired with, so some reviewer experiences may differ depending on the hardware used to test RTX Spark.
Early impressions of RTX Spark are largely positive. Across multiple reviews, experts praised the processor's strong GPU performance, AI capabilities, and surprisingly polished first-generation experience.
At the same time, several reviewers cautioned against viewing RTX Spark through the lens of traditional gaming benchmarks, reminding readers that the platform is designed for AI workloads, creators, and developers.
The RTX Spark is not meant to power gaming laptops. It is aimed at developers and high-end creators. The processor is especially focused on local AI performance, an area many expect to be the next major shift in personal computing.
That being said, the RTX Spark is capable of gaming. It may show the future of Windows on Arm gaming, but that's yet to be seen. Over the coming weeks I am sure we will see countless reviewers test games on RTX Spark laptops. I think they'll be surprised by how well the processor does, but gaming-focused reviews still won't be the best way to judge those PCs.
The first wave of RTX Spark laptops are already available for preorder and hit shelves tomorrow. Our Cale Hunt rounded up all the RTX Spark laptops you can buy, which you can also find below.
Built around the NVIDIA RTX Spark platform with an N1X CPU, RTX GPU, and up to 128GB of unified memory, the Surface Laptop Ultra hits new performance heights for power users. It's all contained in a gorgeous chassis with a mini-LED display and tons of ports. Prices start at $2,599.99, and units will begin shipping on October 16, 2026.
ProArt P14/P16:
The new ASUS ProArt P14 and P16 duo gives users more size options while retaining the same great RTX Spark performance. The P14 is the lightest ProArt laptop ever, while the P16 is the way to go for a larger OLED display. Prices start at $2,599.99 for the P14 and $2,799.99 for the P16. They're expected to begin shipping on October 16, 2026.
Dell's new XPS 16 with RTX Spark features a 3.2K OLED display, lattice-free keyboard, and the usual premium XPS styling. Prices start at $3,799.99 and units are expected to begin shipping on October 16, 2026.
The OmniBook X 14 is the smaller of the two new PCs, but it features the same RTX Spark options for maximum performance. Models start at $2,999.99 and are expected to ship beginning November 4, 2026.
OmniBook Ultra 16":
The larger of the two new HP RTX Spark laptops delivers a gorgeous 16-inch OLED display with 3K resolution and, of course, your choice of RTX Spark chips. Prices start at $3,199.99, and shipments are expected to begin on November 4, 2026.
Lenovo's Yoga 9n should be the right choice for those who value a versatile convertible design and premium build quality. Prices start at $3,099.99, and models are expected to begin shipping on October 16, 2026.
MSI's Prestige N16 Flip AI+ is another versatile convertible featuring the RTX Spark platform and a Tandem OLED display. It appears that preorders might kick off on October 8 for this laptop, with prices starting at $3,299.99.
Join us on Reddit at r/WindowsCentral to share your insights and discuss our latest news, reviews, and more.
RTX Spark is finally here. After years of rumors, NVIDIA has re-entered the Windows silicon game with the MediaTek N1X, a tailor-made 18/20-core SoC with 5,120 or 6,144 GPU cores, up to 128GB of unified memory, and a heck of a lot of power for apps, games, and AI experiences under the hood.
It's not every day that Windows gains a new silicon partner, which is why everyone seems to be involved with ensuring this launch goes as smoothly as possible. Microsoft has worked closely with NVIDIA on optimizing the Windows platform, especially for RTX Spark, and third-party app vendors like Adobe, Blender, anti-cheat software, and more have mobilized to optimize their products and frameworks for the new chipset.
But the big play with RTX Spark is on-device AI. LLMs and agentic workflows have become all the craze over the last year, and NVIDIA believes the RTX Spark is the first chipset for Windows that has been designed from the ground up for running local AI experiences locally and securely, without sacrificing performance.
NVIDIA has 6 hardware partners lined up to ship RTX Spark hardware starting this week, and all of them are aimed at the high-end content creator or developer crowd. NVIDIA has set its sights on competing head-to-head with the MacBook Pro, and is eager to win over AI developers who flocked to that platform thanks to Apple Silicon.
I've been testing out the top configuration of RTX Spark over the last week on a Surface Laptop Ultra, and here's what I discovered it's good at, and what it's not so good at. FYI, today's embargo only allows us to detail the RTX Spark's performance and benchmarks, not the Surface Laptop Ultra itself. So battery life, thermal performance, and laptop-specific variables are not included in this piece. That information will be shared in the full laptop review, coming soon.
If you're looking for a quick rundown of what RTX Spark is and how it compares, here's what you need to know:
RTX Spark is a powerful SoC with good CPU performance and great GPU performance. Its CPU is comparable to the Apple M4 Pro and Qualcomm's Snapdragon X2 Elite, with multi-thread performance being its stronger asset in the CPU department, though it trails behind the Apple M4 Max and Snapdragon X2 Elite Extreme.
The GPU is the star of the show, with great performance thanks to its 6,144 GPU cores that deliver smooth framerates (with help from DLSS and framegen), faster rendering times, and very capable AI performance. Its quantized INT8 tensor execution, unified memory sharing, and computer vision acceleration easily leapfrog mobile discrete GPUs in its power class for AI workflows, which it excels at.
I don't believe RTX Spark is for everyone. It's very good at key things, and shines primarily for those who are knee-deep in local AI workflows and experiences. If you're not a mobile content creator, software developer, or utilize local LLMs often, RTX Spark probably isn't for you.
Mobile efficiency varies depending on power profile. The GPU is hungry and will drain power fast when at full pelt, but in day-to-day usage that only occasionally leans on the GPU, it should last the workday. The good news is, you should see the same performance regardless of plugged-in state.
Compared with most integrated Snapdragon, Intel, and AMD systems, RTX Spark is substantially faster in AI image generation and local inference, while matching or approaching dedicated RTX 4070/5060/5070 class GPU performance in most workloads outside of AI.
The latest generation of high-end Apple Silicon retains an advantage in peak single-thread CPU performance, but RTX Spark’s strength is its GPU, with TensorRT execution and high-speed memory providing advantages in diffusion, quantized models, and sequential data access.
Specification | RTX Spark N1X (5120-core GPU) | RTX Spark N1X (6144-core GPU) |
|---|---|---|
CPU Configuration | 18 cores (9x Cortex-X925 + 9x Cortex-A725) | 20 cores (10x Cortex-X925 + 10x Cortex-A725) |
Max Performance Core Clock | 3.9 GHz | 4.0–4.1 GHz |
Max Efficiency Core Clock | 2.8 GHz | 2.8 GHz |
GPU Architecture | Blackwell RTX | Blackwell RTX |
CUDA Cores | 5,120 | 6,144 |
Streaming Multiprocessors (SMs) | 40 | 48 |
RT Cores | 4th Generation | 4th Generation |
Tensor Cores | 5th Generation | 5th Generation |
Unified Memory Type | LPDDR5X | LPDDR5X |
Memory Interface | 256-bit / 16-channel | 256-bit / 16-channel |
Memory Speed | Up to 9,400 Mbps | Up to 9,400 Mbps |
Peak Memory Bandwidth | Up to 300 GB/s | Up to 300 GB/s |
NVLink-C2C Bandwidth | 600 GB/s bidirectional | 600 GB/s bidirectional |
Display Support | HDMI 2.1a, DisplayPort 2.0 over USB4 | HDMI 2.1a, DisplayPort 2.0 over USB4 |
Process Technology | TSMC 3nm (CPU & GPU dies) | TSMC 3nm (CPU & GPU dies) |
Configurable SoC TDP | 45–80W | 45–140W |
The RTX Spark CPU was co-designed by NVIDIA and MediaTek. The N1X, as it's known, comes in either 18- or 20-core configurations and is relatively efficient while delivering good performance. With that said, it's not leading in its class, leaving something to be desired when it comes to raw benchmarks.
In Cinebench 2026, the 20-core N1X achieved a single-core score of 558 and a multi-core score of 6,885. To put that into perspective, its single-core score beats the first-gen Snapdragon X Elite X1E-80-100 at 519, but falls short compared to Qualcomm's Snapdragon X2 Elite Extreme, which achieves a score of 662 for single-core. Multi-core fares better, with a score of 6,885, which is only just behind the X2 Elite Extreme and Apple M4 Max.
Cinebench 2026 Test | Plugged In Result | Unplugged Result |
|---|---|---|
CPU (Multi Core) | 6,885 pts | 6,844 pts |
CPU (Single Core) | 558 pts | 554 pts |
MP Ratio | 12.33 x | 12.35 x |
These scores do place it ahead of Intel's and AMD's latest flagship mobile workstation chips, but the chipset simply doesn't compete with Apple's latest Max chips, consistently trailing behind the Apple M4 Max and the M3 Ultra. This is a little disappointing given that the RTX Spark is directly targeting Apple Silicon.
It's a similar story with our Geekbench 7 results, achieving a single-core score of 2,718 and a multi-core score of 23,571. It's clear that multi-core performance is the N1X's stronger asset, competing with the likes of the Apple M4 Pro or Intel Core Ultra 9 285K, and is well ahead of the last-gen Snapdragon X Elite and Intel Lunar Lake, as it should be.
Geekbench 7.1 | Plugged In Result | Unplugged Result |
|---|---|---|
CPU (Multi Core) | 23,571 | 23,879 |
CPU (Single Core) | 2,710 | 2024 |
Outside of benchmarks, in day-to-day use the N1X feels fine. This isn't a slow CPU by any means, and will chew through even complex workflows with no sweat. Most day-to -day apps are Arm native these days, but for the few that do require Microsoft's Prism emulation layer, you won't even notice it. RTX Spark makes it so emulated apps feel just like native ones.
All our benchmarks were run with the Best Performance setting enabled, and in our tests we ran them a number of times in both plugged-in and unplugged states to see if there's any performance drop when away from a wall outlet. The good news is the RTX Spark delivers pretty much the same power regardless of plugged-in state, which mimics Apple Silicon.
The star of the show with RTX Spark is the Blackwell RTX GPU with either 5,120 or 6,144 cores. We benchmarked the top-end, 6,144-core configuration for these benchmarks, and it does impress, outputting performance in workflows like video editing, model rendering, and gaming similar to an RTX 4070 or 5060, but in an efficient package with all-day battery life.
For what is essentially an integrated GPU, it outputs dedicated GPU performance in pretty much all workflows. But it really excels at AI workflows, especially when optimized for tensor and CUDA.
The 128GB configuration is set to split that memory evenly between system RAM and the GPU, meaning in our default configuration and for how we ran most of our benchmarks, the GPU had 64GB of video RAM to make use of. In this configuration, it's more than capable for most tasks, with the only exception being running AI models that surpass this size. In that situation, you can allocate more memory to the GPU so that it will fit.
In our 3DMark tests, we found that the RTX Spark performs similarly to an RTX 4070. It's very capable at 1440p gaming with high settings in most titles, though more demanding games will need to lean on DLSS and frame generation for smooth 60+ FPS gaming. In most titles we benchmarked, games ran without a hitch and looked great.
Benchmark | Target Resolution & API | Overall Score | Graphics Score | CPU Score | Test Framerates |
|---|---|---|---|---|---|
3DMark Time Spy | 1440p (DirectX 12) | 12,771 | 12,368 | 15,669 | GT1: 81.68 FPS, GT2: 70.11 FPS, CPU: 52.64 FPS |
3DMark Time Spy Extreme | 4K UHD (DirectX 12) | 5,857 | 5,972 | 5,281 | GT1: 37.77 FPS, GT2: 35.19 FPS, CPU: 66.30 FPS |
3DMark Steel Nomad | 4K UHD (DirectX 12 Heavy) | 3,064 | 3,064 | N/A | Graphics Test: 30.65 FPS |
3DMark Speed Way (Run 1) | 1440p (DirectX 12 Ultimate / DXR) | 3,011 | 3,011 | N/A | Graphics Test: 30.12 FPS |
3DMark Speed Way (Run 2) | 1440p (DirectX 12 Ultimate / DXR) | 2,985 | 2,985 | N/A | Graphics Test: 29.85 FPS |
Gaming is not the main reason to buy an RTX Spark laptop, but it is an additional benefit. It's the first Windows on Arm platform that does gaming relatively well, supporting the usual breadth of gaming technologies and frameworks such as:
Most games run under emulation on RTX Spark, as the platform is Arm64, and most games are compiled for x86. However, in most titles, this isn't a problem. Most mainstream anti-cheat software is now compatible with Windows on Arm, and the Prism emulation layer has been tuned specifically for RTX Spark to ensure most games run without a hitch, though there are exceptions.
Some games, such as Red Dead Redemption 2, will require you to change settings to get them to run successfully. For example, by default Red Dead Redemption 2 was set to run with Vulkan, which caused it to crash at launch. Switching it to DirectX12 fixed this for me. Most games run without needing to change any settings first, however.
I tried Cyberpunk 2077, which runs pretty well right out of the box. The game analyzed my system and decided that Ray Traced Ultra settings were a perfect fit for the RTX Spark. Running at 1440p with frame gen enabled, the game was easily able to maintain 60fps while looking good doing it with these graphics settings enabled.
Shadow of the Tomb Raider also works great, but it takes quite a while to actually load up. But once the game is running and you're in-game, it looks great and runs well above 60fps at high settings at 1440p. Marvel's Spider-Man 2 and The Witcher 3 Remastered ran without a hitch, reaching close to 90fps in high settings at 1440p.
For content creators, RTX Spark is a great choice. Whether you are editing photos or rendering videos using apps like Adobe Photoshop and Premiere Pro, or rendering 3D environments in Blender, the RTX Spark is well suited for those tasks.
Adobe and NVIDIA say the Adobe Creative Cloud Suite has been optimized specifically for RTX Spark. These optimizations include:
When it comes to video rendering times, the RTX Spark trails the current-gen RTX 50 series GPUs. We benchmarked it against three other laptops with an RTX 5060, 5070 Ti, and 5080, in which it trailed the RTX 5060 in video rendering performance, finishing a few seconds later in most instances. In this benchmark, we ran Premiere Pro Beta, as that version includes necessary optimizations for RTX Spark.
Export Profile (Seconds ↓) | Surface Laptop Ultra | Lenovo Yoga Pro 9 | ASUS Zephyrus G14 | Lenovo LEGION 9 |
|---|---|---|---|---|
H.264 Export (1 GPU) | 42.002 s | 32.111 s | 39.403 s | 20.218 s |
H.264 Export (Pass 2) | 34.415 s | 26.003 s | 33.532 s | 18.401 s |
H.264 UHD Export (1 GPU) | 81.419 s | 64.140 s | 71.918 s | 40.235 s |
H.264 UHD Export (Pass 2) | 66.496 s | 52.301 s | 59.536 s | 34.253 s |
Dedicated GPU performance can vary depending on laptop chassis size, cooling solution, and thermal envelope, which is why the 5070 Ti frequently trailed the 5060 in our testing.
The RTX Spark GPU is very performant and is capable of pretty much handling anything you'd want to throw at it on a mobile workstation. Where it really shines, however, is running local AI models directly on it. It's the reason NVIDIA built this platform to begin with, and it does this very well.
We ran several Procyon AI benchmarks to see how well the GPU performs when running on-device AI workflows. Text generation LLMs ran very fast, with Spark delivering responsive time-to-first-token results, including 0.27 seconds on Phi and 0.39 seconds on LLaMA3, but its sustained decoding rates trail higher-wattage discrete GPUs.
Model | Overall Score | Tokens / Second | Time to First Token (s) | Model Load Time (s) |
|---|---|---|---|---|
Phi-3.5-mini | 2,615 | 72.21 | 0.27 | 0.66 |
Mistral 7B | 2,276 | 53.07] | 0.47 | 0.96 |
Llama 3.1 8B | 2,251 | 49.44 | 0.40 | 1.50 |
Llama 2 13B | 2,108 | 27.44 | 0.84 | 1.69 |
But quantized AI performance is where RTX Spark truly leads. In Stable Diffusion 1.5 INT8, the RTX Spark scored 11,294 and generated images in 2.77 seconds, substantially outperforming the tested RTX 5080, RTX 5070 Ti, and RTX 5060, on which we ran the same tests.
Model Profile & Metric | Surface Laptop Ultra | Lenovo Yoga Pro 9 | ASUS Zephyrus G14 | Lenovo LEGION 9 |
|---|---|---|---|---|
SD 1.5 FP16 - Total Duration | 67.55 s | 69.70 s | 107.73 s | 44.59 s |
SD 1.5 FP16 - Speed | 4.222 img/s | 4.356 img/s | 6.733 img/s | 2.787 img/s |
SD 1.5 FP16 - Average UNet | 23.986 it/s | 23.265 it/s | 15.070 it/s | 36.426 it/s |
SD 1.5 INT8 - Total Duration | 22.14 s | 138.74 s | 101.83 s | 440.19 s |
SD 1.5 INT8 - Speed | 2.767 img/s | 17.342 s/img | 12.729 img/s | 55.024 s/img |
SD 1.5 INT8 - Average UNet | 19.158 it/s | 2.949 it/s | 4.043 it/s | 0.927 it/s |
SDXL FP16 - Total Duration | 400.82 s | 499.37 s | 735.47 s | 303.68 s |
SDXL FP16 - Speed | 25.051 s/img | 31.211 s/img | 45.967 s/img | 18.980 s/img |
SDXL FP16 - Average UNet | 4.028 it/s | 3.331 it/s | 2.196 it/s | 5.321 it/s |
The GPU is best suited for running large on-device AI models such as Qwen 3.8 27B, Gemma 4 31B, or even 120B-parameter models such as Nemotron or GPT-OSS.
NVIDIA is touting local agentic capabilities with this GPU, leaning on apps like Hermes Agent and OpenClaw to achieve this. We tested Hermes Agent, as OpenClaw failed to connect to the gateway once setup was complete.
The selling point of running LLMs and agents locally is that you are no longer constrained by usage limits or the amount of tokens you have available. When the AI runs locally, it's yours. You can run it for as long and as hard as you like.
It's also good for security and privacy reasons. You may have confidential documents, audio recordings, intellectual property that you need summarized or explained to you, and feeding that data to a cloud-based LLM or agent is giving that data away to the host.
With RTX Spark, you can feed whatever data or task you want to your local AI running off the GPU, and that data will never leave the device. You can build up your own personal agent that is capable of managing your local files, creating documents, making edits, and in some cases even controlling your PC.
Hermes Agent is a great tool for beginners, as it offers local AI compute experiences and makes setting it up a breeze. You open the app, choose to run AI locally, and it recommends the best model for the specs you have. Once downloaded, you're good to start chatting with your own AI locally.
Hermes Agent is particularly good at automating tasks. The first thing I did was tell it to clean up and organize my large, cluttered downloads folder. It moved images to my photos library, deleting any old executables, and organizing and categorizing PDFs and Word documents appropriately. It did all this without sending any of my data to the cloud, all processed by the GPU.
I also asked it to build me a basic website that included all my benchmark results so that I could refer back to them more easily. It ran that process entirely off my GPU, and came up with a pretty nice looking webpage design in the process. Zero cloud-based tokens used:
NVIDIA has even built MCP support into NVIDIA Broadcast and NVIDIA Control Panel, allowing you to hook it up to your local LLM and issue commands that allow it to configure your NVIDIA PC's settings.
In addition to raw CPU and GPU performance, Microsoft has worked closely with NVIDIA to optimize RTX Spark for Windows 11. This partnership runs deep, and there are a lot of things going on behind the scenes that will aid the performance of Windows and support for apps on RTX Spark over the coming months and years.
For those unboxing a new RTX Spark laptop this fall, the Get Started app on Windows 11 will present a step that makes downloading and installing apps that can handle AI agents super easy. Hermes Agent will even go a step further and recommend a suitable model to download once up and running, meaning the user has to do zero research to get started.
With that said, I still find these recommended apps quite complex and convoluted. Many beginners will feel overwhelmed by these apps, especially OpenClaw, which I could not get working properly after it failed to open a gateway to allow the AI model to communicate with the OpenClaw chat interface.
Microsoft has optimized the system scheduler specifically for RTX Spark to squeeze out as much performance across Windows, menu flyouts, app launches, and more as possible. It has also made changes and enhancements to how Windows handles unified memory so that it's faster and more reliable at feeding that memory to where you need it.
TensorRT is now supported by Windows ML, and DirectX 12 has neural rendering support. The Prism emulation layer has also been tuned for RTX Spark, which results in emulated apps that simply do not feel like they are being emulated.
Microsoft is also keen to position Windows as the platform for local AI and agentic workflows. It has built safeguards and security into the Windows platform to ensure the local agents you run on Windows aren't capable of making severe changes or causing damage to your files, apps, and more.
This is a direct attempt at combating the sudden rise in local AI developers flocking to the Mac. Apple has made little optimizations to macOS to adopt and safeguard local AI and agentic workflows on that platform, whereas Microsoft is moving to make agents a native extension of the Windows platform.
RTX Spark is a Copilot+ PC certified chip, but you won't find any Copilot+ PC features at launch. Microsoft says it's working with NVIDIA to bring features like Windows Recall, Click To Do, and on-device Semantic Search to the RTX Spark platform in the coming months, which will run off the RTX Spark's 50 TOPS NPU.
RTX Spark is a super interesting chipset. It's forward leaning in a lot of ways, built on the Arm64 architecture that delivers serious performance and great efficiency. While its N1X CPU is just okay, its Blackwell RTX GPU really shines.
This is the first time we've seen RTX 4070-5070 level performance in an SoC that is this small and efficient. It's optimized for AI workloads, which is its strongest workflow by far. Quantized AI models run great, and with up to 128GB of unified memory, most mainstream models will fit entirely on the GPU without a hitch.
It's also great for content creators, and is very capable at gaming also. But at the prices that these devices are launching at, I think most people would be better served getting an Intel or AMD laptop with a dedicated NVIDIA GPU instead, as these often cost less than the cheapest RTX Spark laptops right now.
But if your workflow involves running AI models locally all day, software development, or complex data crunching and source code management, the RTX Spark is one of the better choices in the Windows space right now, especially for laptops that are thin-ish and (relatively) lightweight.
These RTX Spark laptops are more efficient and portable than typical laptops with a dedicated NVIDIA GPU, which is one of the key benefits of an SoC like this. The power this GPU outputs, for what is essentially integrated graphics, should not be understated.
Of course, just how efficient RTX Spark is will depend entirely on the hardware you choose to buy, as OEMs are free to feed the GPU more or less power depending on the chassis and thermal envelope. Keep that in mind.
All benchmarks ran on RTX Spark 20-core CPU configuration with 128GB RAM and 6,144 GPU cores. All tests ran connected to power with Best Performance profile enabled, unless stated otherwise. We are still running benchmarks and new tests, and will update accordingly.
Geekbench 7.1.0 Pro (AArch64) Workload | Surface Laptop Ultra Score | ASUS ProArt P16 |
|---|---|---|
Single-Core Score | 2,710 | 2,717 |
Multi-Core Score | 23,571 | 23,833 |
Geekbench 7.1 GPU | CUDA Score |
|---|---|
Surface Laptop Ultra | 186098 |
ASUS ProArt P16 | 187547 |
Cinebench 2026 Test | Surface Laptop Ultra | ASUS ProArt P16 |
|---|---|---|
CPU (Multi Core) | 6,885 pts | 6,742 pts |
CPU (Single Core) | 558 pts | 554 pts |
Procyon Office Productivity Application | Score |
|---|---|
Office Productivity Overall Score | 7,024 |
Microsoft Word Score | 7,160 |
Microsoft Excel Score | 7,440 |
Microsoft PowerPoint Score | 7,904 |
Microsoft Outlook Score | 4,761 |
Procyon Essentials Workload | Score |
|---|---|
Essentials Overall Score | 4,863 |
App Startup Score | 5,382 |
Web Browsing Score | 4,346 |
Browser Tabs Score | 6,130 |
File Operations Score | 5,013 |
Video Conferencing Score | 5,015 |
Procyon Photo Editing Subtest | Score |
|---|---|
Photo Editing Overall Score | 6,801 |
Image Retouching Score | 9,398 |
Batch Processing Score | 4,922 |
Procyon Video Editing v1.5 Export Test | Time / Score |
|---|---|
Video Editing Overall Score | 29,595 |
Export H.264 Full HD (Test 1 - GPU Effects) | 42.00 s |
Export H.264 Full HD (Test 2) | 34.42 s |
Export H.264 UHD 4K (Test 1 - GPU Effects) | 81.42 s |
Export H.264 UHD 4K (Test 2) | 66.50 s |
Model | Overall Score | Tokens / Second | Time to First Token (s) | Model Load Time (s) |
|---|---|---|---|---|
Phi-3.5-mini | 2,615 | 72.21 | 0.27 | 0.66 |
Mistral 7B | 2,276 | 53.07 | 0.47 | 0.96 |
Llama 3.1 8B | 2,251 | 49.44 | 0.40 | 1.50 |
Llama 2 13B | 2,108 | 27.44 | 0.84 | 1.69 |
Model & Precision | Score | Speed (s / image) | Total Time Taken (s) |
|---|---|---|---|
Stable Diffusion 1.5 (FP16) | 1,480 | 4.22 | 67.55 |
Stable Diffusion 1.5 (INT8) | 11,294 | 2.77 | 22.14 |
Stable Diffusion XL (FP16) | 1,496 | 25.05 | 400.82 |
Vision Model Workload | Throughput / Inferences per Second |
|---|---|
Overall Score | 3,997 |
ConvNeXt Overall Throughput | 1,901.71 inf/s |
Segment Anything 2.1 Decoder | 806.86 inf/s |
Segment Anything 2.1 Encoder | 58.90 inf/s |
BLIP Encoder | 421.58 inf/s |
BLIP Decoder | 409.50 inf/s |
DETR | 225.04 inf/s |
Real-ESRGAN | 16.58 inf/s |
Benchmark Suite / Test | Surface Laptop Ultra | Lenovo Yoga Pro 9 | ASUS Zephyrus G14 | Lenovo LEGION 9 |
|---|---|---|---|---|
Video Editing (v15 Score) | 29,595 | 38,262 | 32,096 | 58,497 |
AI Computer Vision 2 Overall | 3,997 | 2,690 | 2,578 | 4,946 |
AI Image Gen: SD 1.5 FP16 | 1,480 | 1,434 | 928 | 2,242 |
AI Image Gen: SD 1.5 INT8 | 11,294 | 1,801 | 2,454 | 567 |
AI Image Gen: SDXL FP16 | 1,496 | 1,201 | 815 | 1,975 |
AI Text Gen: Phi | 2,615 | 2,759 | 1,783 | 4,005 |
AI Text Gen: Mistral | 2,276 | 2,482 | 1,516 | 3,498 |
AI Text Gen: LLaMA 3 | 2,251 | 2,407 | 1,457 | 3,472 |
AI Text Gen: LLaMA 2 | 2,108 | 249 | 1,350 | 3,360 |
Essentials (Overall Score) | 4,863 | 4,931 | 4,128 | 4,754 |
According to The Information, Apple is planning to get back into the server game and might just pair up with Nvidia to make it happen. Apple retired its Xserve line in 2011 and has largely left enterprise machines to other manufacturers since. But the growing demand for compute power as the AI industry continues to expand has apparently led the company to believe there is an opening for its powerful but efficient ARM-based M processors. Its Mac Mini and Mac Studio have proven popular with AI developers, which has led to shortages.
The server product, whether it's called Xserve or something else, likely won't debut until 2029. Word is that i …
Le NVIDIA GeForce RTX 5090 sono sempre state un po’ un unicorno per noi semplici appassionati di videogiochi, soprattutto in virtù prezzi esorbitanti, ma anche gli oltre 2.000 euro di listino sembrano “economici” rispetto alla situazione attuale, in cui sono praticamente irreperibili ovunque e quando ne salta fuori qualcuna bisogna sborsare anche più di 5.000 euro. Colpa della crisi dei chip? Forse, un po’, ma a quanto pare il problema è soprattutto il contrabbando cinese.
L’account Twitter noto come HKEPC ha pubblicato nelle scorse ore alcune foto di interi bancali delle schede “gaming” di NVIDIA nuove, ancora imballate, pronte per essere montate in qualche (relativamente) piccolo datacenter o server AI. Insomma, siamo tornati allo scenario di qualche anno fa nel bel mezzo della frenesia da criptovalute, anche se questa volta si parla di AI. Ai tempi, NVIDIA introdusse successivamente le varianti LHR (Lite Hash Rate) di alcune RTX 30, limitando le prestazioni nel mining senza modificare sostanzialmente quelle nei videogiochi, ma si trattava di semplici limitazioni software, che a furia di insistere furono infine bucate dopo qualche tempo.
中國很多廠商會在各地收購 RTX 5090 並組裝成 AI 工作站出售。 pic.twitter.com/AssaXEylJ0
Il miglior smartphone pieghevole del momento? Samsung Galaxy Z Flip 7, compralo al miglior prezzo da Rimediashop.it a 691 euro.CLICCA QUI PER CONTINUARE A LEGGERE
Agility Robotics has debuted its first humanoid robot engineered to work safely near humans without risking harm to flesh-and-blood coworkers. Such safety features could unlock many more opportunities to use such robots inside warehouses and automotive factories—all without requiring isolated robot work cells and physical separation barriers.
When Agility’s new Digit 5 robot detects a person at a distance, it can autonomously take precautions, like moving to avoid the person or standing still so the person can pass by without getting closer. If a person is getting into close proximity with Digit 5, the robot can even choose to squat and assume a seated position.
“The robot was designed with a complex safe motion system that can take a variety of different mitigations depending on exactly what sort of human presence is detected,” Pras Velagapudi, chief technology officer at Agility, told Ars.
© Agility Robotics
Dopo un agosto particolarmente ricco, Nvidia sembra aver rallentato leggermente il ritmo per la sua piattaforma di streaming di videogiochi GeForce NOW, che il mese oltre a nuovi giochi aveva ricevuto un’applicazione nativa per Linux, il supporto a Firefox e la sincronizzazione con GOG. Ma l'appuntamento del giovedì sera con i nuovi titoli della settimana non poteva mancare, ecco cosa è uscito:
Non è nuovo, ma da segnalare anche che Valheim è arrivato alla versione 1.0 stabile ed è quindi uscito dall'accesso anticipato.
Boots on the ground, this #GFNThursday. 👢@WARDOGS, Valheim and other games have been added to the GeForce NOW Library.
See which games 👉 https://t.co/SwIwlP56Ga pic.twitter.com/ZuifO0kbSsPiccolo prezzo per un ottimo smartphone? Honor 400 Lite, compralo al miglior prezzo da Amazon Marketplace a 130 euro.CLICCA QUI PER CONTINUARE A LEGGERE
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