The MLops company making it easier to run AI workloads across hybrid clouds

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There is no shortage of options for organizations seeking places in the cloud, or on-premises to deploy and run machine learning and artificial intelligence (AI) workloads. A key challenge for many though is figuring out how to orchestrate those workloads across multi-cloud and hybrid-cloud environments.

Today, AI compute orchestration vendor Run AI is announcing an update to its Atlas Platform that is designed to make it easier for data scientists to deploy, run and manage machine learning workloads across different deployment targets including cloud providers and on-premises environments.

In March, Run AI raised $75 million to help the company advance its technology and go-to-market efforts. At the foundation of the company’s platform is a technology that helps organizations manage and schedule resources on which to run machine learning. That technology is now getting enhanced to help with the challenge of hybrid cloud machine learning.

“It’s a given that IT organizations are going to have infrastructure in the cloud and some infrastructure on-premises,” Ronen Dar, cofounder and CTO of Run AI, told VentureBeat. “Companies are now strategizing around hybrid cloud and they are thinking about their workloads and about where is the right place for the workload to run.”


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The increasingly competitive landscape for hybrid MLops

The market for MLops services is increasingly competitive as vendors continue to ramp up their efforts.

A Forrester Research report, sponsored by Nvidia, found that hybrid support for AI workload development is something that two-thirds of IT decision-makers have already invested in. It’s a trend that is not lost on vendors.

Domino Data Lab announced its hybrid approach in June, which also aims to help organizations run in the cloud and on-premises. Anyscale, which is the leading commercial sponsor behind the open-source Ray AI scaling platform, has also been building out its technologies to help data scientists run across distributed hardware infrastructure.

Run AI is positioning itself as a platform that can integrate with other MLops platforms, such as Anyscale, Domino and Weights & Biases. Lior Balan, director of sales and cloud at Run AI, said that his company operates as a lower level solution in the stack than many other MLops platforms, since Run AI plugs directly into Kubernetes.

As such, what Run AI provides is an abstraction layer for optimizing Kubernetes resources. Run AI also provides capabilities to share and optimize GPU resources for machine learning that can then be used to benefit other MLops technologies.

 The complexity of multicloud and hybrid cloud deployments

A common approach today for organizations to manage multicloud and hybrid clouds is to use the Kubernetes container orchestration system.

If an organization is running Kubernetes in the public cloud or on-premises, then a workload could run anywhere that Kubernetes is running. The reality is a bit more complex, as different cloud providers have different configurations for Kubernetes and on-premises deployments have their own nuances. Run AI has created a layer that abstracts the underlying complexity and difference across public cloud and on-premises Kubernetes services to provide a unified operations layer.

Dar explained that Run AI has built its own proprietary scheduler and control plane for Kubernetes, which manages how workloads and resources are handled across the various types of Kubernetes deployments. The company has added a new approach to its Atlas Platform that allows data scientists and machine learning engineers to run workloads from a single user interface, across the different types of deployments. Prior to the update, data scientists had to use different interfaces to log into each type of deployment in order to manage a workload.

In addition to now being able to manage workloads from a single interface, it’s also easier to move workloads across different environments.

“So they can run and train workloads in the cloud, and then switch and deploy them on premises with just a single button,” Dar said.

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Intel’s Arc GPU issues run much deeper than performance

I’ve been excited for Intel’s Arc Alchemist GPUs — the first discrete gaming graphics cards Intel has ever released. But that hype has quickly faded over the last few months, as reports of subpar performance, broken drivers, and a pile of delays have plagued Intel’s entrance into the market.

It takes a lot to enter the pantheon of the best graphics cards, but Intel’s issues go well beyond performance and features. Driver bugs are rampaging through the Arc Alchemist stack, and it’s becoming clear that Intel doesn’t have a system in place for dealing with those issues when new drivers are put out, or even for identifying them months after the fact.

It may be hard to enter the GPU market, but Intel only has itself to blame for the state of Arc right now.

43 driver issues, all from YouTube

Worst We’ve Tested: Broken Intel Arc GPU Drivers

YouTube channel Gamers Nexus published a deep dive into Intel’s broken Arc drivers on August 1, following on a series of rumors that Arc’s future was in jeopardy. It wasn’t until August 19 when Intel’s Lisa Pearce wrote a blog post answering questions about Arc that we learned Intel actually found out about 43 driver issues from the Gamers Nexus video.

“We have received frank feedback from press during recent reviews, and we have taken it to heart. For example, we filed 43 issues with our engineering team from a review of the A380 by Gamers Nexus,” the blog post reads.

Although Intel Arc Alchemist doesn’t provide flagship performance, that never seemed like the goal. And frankly, that’s not the issue Intel is facing now. Gamers Nexus found that drivers simply wouldn’t work with some monitors, Intel Smooth Sync would cause visual glitches, and Intel Arc Control would break when overclocking — among dozens of other problems. That’s not to mention the issues Arc Alchemist has faced when it comes to older DirectX versions, as Intel only officially supports DirectX 11 and DirectX 12.

Intel pins the issues on the Arc Control “installer and how it downloaded unique components after the initial installation.” Basically, Intel says the drivers have a corrupted installation process where “unexpected failures are causing [the installation process] to be unreliable.” Intel knows about the issues and is working on them, but that’s not the main problem here.


The GPU that Gamers Nexus tested, the Arc Alchemist A380, was first rolled out on June 15. Considering that Arc Alchemist GPUs are exclusive to China right now, that presumably means buyers have been dealing with these driver issues for over two months, and yet it took a U.S.-based YouTube video where Gamers Nexus had to track down a GPU that isn’t even available in the states for Intel to address the problems. Keep in mind we’re not talking about minor issues, either. We’re talking about things that fundamentally break Arc Alchemist.

There have been previous examples of this, too, such as when a missing line of code resulted in a 100x drop in ray tracing performance on Linux. Intel may be doing press spots with channels like Linus Tech Tips and advertising the snot out of Arc Alchemist. But driver support is killing Arc right now, as Intel waits for tech press to uncover driver issues that the company should have discovered months earlier.

The pitfalls of promises

Intel announces new features of discrete Intel Arc GPUs.

The news about Intel discovering driver issues from a YouTube video gets at a larger point about Arc Alchemist — Intel overpromised. Unlike Nvidia and AMD, Intel likes to set out its road map early. We learned about Arc Alchemist in the middle of 2021, and Intel has been making promises, like saying over 50 Arc laptop designs would be available in 2022, since then.

Many of the issues with drivers are features Intel promised at launch — things like Smooth Sync and built-in overclocking, neither of which are necessary if the drivers have so many issues in the first place. We’re also waiting on Intel’s XeSS, which was supposed to launch on May 20. This is another feature we heard about in the middle of 2021 that Intel has yet to provide any satisfying updates on.

Even if Arc Alchemist delivered perfectly on every promise, there’s no doubt that Intel would be an underdog compared to the duopoly between AMD and Nvidia. And most of that comes down to drivers. By announcing Arc early and pushing it hard in advertising, Intel backed itself into a corner where the options were to either keep delaying Arc Alchemist or underdeliver on its many promises, and it looks like Intel did a little of both.

Still waiting

Two Intel Arc GPUs running side by side.
Linus Tech Tips

When Intel announced Arc Alchemist, it put out the idea that cards would be available in the first few months of 2022. We’ve since learned that the rollout is a little more complicated. You can technically buy one of Intel’s Arc A380 graphics cards now, but we still don’t have anywhere near the full lineup, much less availability for Arc around the world.

In the context of the driver issues, that staggered rollout doesn’t look great. At this point, Intel is learning on the fly as it offers its discrete graphics cards for sale while press continue to discover issues that should have been fixed well before you could buy an Arc GPU. Intel seems to know that in part, with Pearce writing that Intel is “continuing to learn what it will take for us to be successful.”

You can see the tension in real time, as pressure mounts for Intel to release more Arc GPUs and reports of broken driver support continue to circulate. That’s on Intel’s shoulders, though. It’s clear now that Intel jumped the gun with Arc Alchemist, and as press outlets continue to discover issues, Intel can only point the finger at itself. I hope Intel gets the situation under control, but any news about Arc Alchemist has been bad news for months now.

I’ve reached out to Intel and asked how it plans to change drivers and the state in which they will be released in the future, and I’ll update this story when I hear back.

Editors’ Choice

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Want to make robots run faster? Try letting AI take control

Quadrupedal robots are becoming a familiar sight, but engineers are still working out the full capabilities of these machines. Now, a group of researchers from MIT says one way to improve their functionality might be to use AI to help teach the bots how to walk and run.

Usually, when engineers are creating the software that controls the movement of legged robots, they write a set of rules about how the machine should respond to certain inputs. So, if a robot’s sensors detect x amount of force on leg y, it will respond by powering up motor a to exert torque b, and so on. Coding these parameters is complicated and time-consuming, but it gives researchers precise and predictable control over the robots.

An alternative approach is to use machine learning — specifically, a method known as reinforcement learning that functions through trial and error. This works by giving your AI model a goal known as a “reward function” (e.g., move as fast as you can) and then letting it loose to work out how to achieve that outcome from scratch. This takes a long time, but it helps if you let the AI experiment in a virtual environment where you can speed up time. It’s why reinforcement learning, or RL, is a popular way to develop AI that plays video games.

This is the technique that MIT’s engineers used, creating new software (known as a “controller”) for the university’s research quadruped, Mini Cheetah. Using reinforcement learning, they were able to achieve a new top-speed for the robot of 3.9m/s, or roughly 8.7mph. You can watch what that looks like in the video below:

As you can see, Mini Cheetah’s new running gait is a little ungainly. In fact, it looks like a puppy scrabbling to accelerate on a wooden floor. But, according to MIT PhD student Gabriel Margolis (a co-author of the research along with postdoc fellow Ge Yang), this is because the AI isn’t optimizing for anything but speed.

“RL finds one way to run fast, but given an underspecified reward function, it has no reason to prefer a gait that is ‘natural-looking’ or preferred by humans,” Margolis tells The Verge over email. He says the model could certainly be instructed to develop a more flowing form of locomotion, but the whole point of the endeavor is to optimize for speed alone.

Margolis and Yang say a big advantage of developing controller software using AI is that it’s less time-consuming than messing about with all the physics. “Programming how a robot should act in every possible situation is simply very hard. The process is tedious because if a robot were to fail on a particular terrain, a human engineer would need to identify the cause of failure and manually adapt the robot controller,” they say.

Mini Cheetah gets the once-over from a non-robot dog.
Image: MIT

By using a simulator, engineers can place the robot in any number of virtual environments — from solid pavement to slippery rubble — and let it work things out for itself. Indeed, the MIT group says its simulator was able to speed through 100 days’ worth of staggering, walking, and running in just three hours of real time.

Some companies that develop legged robots are already using these sorts of methods to design new controllers. Others, though, like Boston Dynamics, apparently rely on more traditional approaches. (This makes sense given the company’s interest in developing very specific movements — like the jumps, vaults, and flips seen in its choreographed videos.)

There are also faster-legged robots out there. Boston Dynamics’ Cheetah bot currently holds the record for a quadruped, reaching speeds of 28.3 mph — faster than Usain Bolt. However, not only is Cheetah a much bigger and more powerful machine than MIT’s Mini Cheetah, but it achieved its record running on a treadmill and mounted to a lever for stability. Without these advantages, maybe AI would give the machine a run for its money.

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The Xbox Windows app will make it easier to know if a game will run well on your PC

With so many different ways to build a computer, one of the trickiest aspects of PC gaming is knowing whether a game you’re about to install will play well on your machine. Microsoft is testing a feature that could make that easier. As spotted by , the Xbox app on Windows now includes a label that will tell you if a game will “play great” on your PC.

Xbox Windows PC app

Tom Warren/The Verge

Since there are some titles where the app says “performance check not available yet,” it doesn’t appear Microsoft is comparing the listed system requirements against the components in your PC. Instead, it would seem the company is building a database where it tests a game against various different machines. If you want to test the labels, you can do so by downloading the and opting into the Windows gaming preview. 

If the system works well, it could solve a common pain point for PC gamers. In recent years, we’ve seen some developers share more granular system requirements for their games, telling you not just the minimum and recommended spec but also hardware that will deliver the best experience at Full HD, QHD and 4K respectively. Unfortunately, there hasn’t been anything close to standardization across the industry, and that’s even before you consider the fact some developers list overly optimistic requirements for their games.

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‘Pokémon Go’ can now run on iOS at higher frame rates

Niantic has just rolled out updates for the Pokémon Go app, and one of the new features for iOS will let you run the game at higher frame rates. The Verge has discovered a new option that allows you “unlock your device’s native refresh rate for higher FPS.” It’s not explicitly stated in the app’s change log and probably falls under the “various quality-of-life improvements” the developer has listed. Also, the option is off by default, so you’ll have to find it in settings if you want to take advantage of your phone’s capabilities.

As the publication notes, Pokémon Go was previously capped at 30fps on iOS — players have had to employ workarounds to get their games to run at 60fps — but newer phones are capable of more frames per second than that. The iPhone 13 Pro, for instance, has a 120fps screen. The Verge says switching the feature on made a huge difference and made the game a lot more responsive. While Niantic may have been trying to cater to owners of the new iPhone, the option can also be accessed on its predecessors. It just may not work as well on hardware powered by older chips.

You can find the the feature in the game’s advanced settings, which will show “native refresh rate unlocked” when it’s switched on.

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Game Boy Advance ‘hacked’ to run PlayStation games using a Raspberry Pi

The Game Boy Advance is useful in the modern era for more than watching Christopher Nolan blockbusters. Gizmodo notes that tinkerer Rodrigo Alfonso has Nintendo’s 20-year-old handheld running PlayStation (and Genesis, and SNES) games without special modifications. The trick, as you might imagine revolves around a custom cartridge — you’re technically running the game on a separate system.

The cartridge houses a Raspberry Pi 3 mini-computer running the RetroPie emulator and streaming both video and input through the GBA’s multiplayer-oriented Link Port. Yes, that’s constraining as you think it is — you can’t transfer more than 1.6Mbps bi-directionally, and the Pi has to routinely give the “poor” GBA’s processor a break for a few microseconds. Alfonso suggests lowering the stream resolution from the console’s native 240 x 160 if a high frame rate is important.

Still, the results are mostly impressive. The special cart can handle classics like the Crash Bandicoot series and Spyro the Dragon at smooth frame rates, albeit with some video artifacts that reflect the limited bandwidth. You can overclock the GBA’s processor to improve the frame rate and quality.

You’ll have to build the cartridge and load code yourself, although Alfonso has helpfully provided both on GitHub. This probably won’t replace a PSP if you want the most authentic PlayStation handheld experience you can get. It might, however, give you a reason to dig your GBA out of the closet.

All products recommended by Engadget are selected by our editorial team, independent of our parent company. Some of our stories include affiliate links. If you buy something through one of these links, we may earn an affiliate commission.

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How to Run Windows 11 on a Mac

Windows 11 is the latest version of Windows, and whether it’s for PC gaming or running a Windows-exclusive app, you may want to run it on your Mac.

Due to TPM and Secure Boot limits, however, you can’t run Windows 11 natively on modern Macs. Even Macs with Apple’s new M1 chips can’t run Windows natively, since Microsoft doesn’t license the ARM version of Windows for Apple to use.

Instead, you’ll have to virtualize the new Microsoft operating system and run it on top of MacOS, which is exactly what we’ll explain below.

How to run Windows 11 on a Mac using Parallels

The best way to get Windows 11 on a Mac is with a program known as Parallels. This lets you run Windows applications on your MacBook, without performance issues. You also can share content between Mac and Windows. There’s even a unique coherence mode that lets you hide the Windows desktop and use Windows apps just like Mac apps. Here’s how to use it.

Step 1: Download Parallels

To get started, visit the Parallels website and download the app, or the free trial. You’ll need to save the DMG file somewhere on your Mac. Parallels is a paid software priced at $80. If you’re not sure about it, a free 14-day trial is available. Here’s how to get started with it.

Step 2: Install Parallels

Installing Parallels on MacOS.

Once finished, launch the DMG file, and double-click the Install Parallels Desktop icon. Click OK at any other prompts, and let Parallels download. When the download is finished, accept the license agreements, enter your Mac password, and initialize the app to grant it permissions. When complete, click Finish.

Step 3: Download Windows 11

Downloading Windows 11 ISO on MacOS.

Once the app is installed, you’ll need to install Windows 11 inside of it. Click the Skip button and minimize Parallels. Open a web browser, and download a Windows 11 ISO file from Microsoft. Choose the Download Windows 11 Disk Image ISO option, and choose Windows 11 followed by English.

Step 4: Install Windows 11 in Parallels

Choosing the Windows 11 ISO in Parallels on MacOS.

Now that Windows 11 is downloaded, open up Parallels again. Then, choose the Install Windows or another OS from a DVD or Image file option. Pick Windows 11 from the list. The app should automatically find the ISO, and let you choose Continue. If you have a license key enter it, otherwise, uncheck the box and click Continue. Select the edition, and choose Done. 

Note: You’ll still need a license key to activate Windows.

Step 5: Choose how you want to use Windows 11

Installing Windows 11 in Parallels on MacOS.

As part of the setup, Parallels will ask how you will use Windows. Choose Productivity or Games only. Name your install, choose where to save it, and click Create. Then, click Continue. Click OK at any other prompts and wait for Windows 11 to install!

The install process for Windows 11 in Parallels might take a while. Give it some time. When finished, the Installation Assistant will put you into your Windows 11 desktop and ask you to sign in and create a Parallels account for activation.

Tips and tricks

You’re now free to enjoy using Windows 11 on your MacBook! Resize the window as you see fit, or take Windows 11 full screen with the green icon at the top left of the window.

You also can enter Coherence Mode by clicking Control, Command, and C on your Keyboard. This will let you use the Windows 11 Start Menu from your MacOS dock, and run Windows apps full screen on Mac for a seamless experience.

When you’re ready to shut down Windows 11 on your Mac, just click the Action Menu at the top and choose Shutdown. You can then quit the app as usual from the Apple Menu. At any time you can get back to Windows by launching the Parallels app on your Mac and pressing the Play icon.

Overall, the performance of Windows 11 in Parallels on MacOS should feel quite snappy. It’s not nearly as good as Boot Camp would be, since Windows is being virtualized, but for using routine Windows apps, things will be just fine.

There are some limits, though. Microsoft doesn’t officially support running Windows 11 in virtual machines, so there’s no guarantee that this might keep working in the future. Parallels also aren’t designed for gaming, even if you have a high-end system. Parallels Desktop 17 doesn’t support DirectX12. Older DirectX11 games that don’t require a specific GPU should work just fine. We’ve played titles like Overwatch just fine on our Mac machine through Parallels.

Editors’ Choice

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AI develops on-demand service to run AI workloads across infrastructures, the Intel-owned company that offers a platform to help data scientists build and deploy machine learning applications, has opened early access to a new managed service called Metacloud.

The offering, as the company explains, gives AI developers the flexibility to run, test, and deploy AI and machine learning (ML) workloads on a mix of mainstream infrastructure and hardware choices, even within the same AI/ML workflow or pipeline. Metacloud: Flexibility for AI developers

AI experts can often find themselves struggling to scale their projects due to the limitations of the cloud or on-premise infrastructure in use. They do get the option to switch to a new environment, but that means re-instrumenting a completely new stack as well as spending a lot of cash and time. This eventually keeps most of the users locked on a single vendor, making it a major obstacle to scaling and operationalizing AI. Metacloud tackles this challenge with a flexible software-as-a-service (SaaS) interface, where developers can pick cloud or on-premise compute resources and storage services of their choice to match the demand of their AI/ML workloads.

The solution has been designed using cloud-native technologies such as containers and Kubernetes, which enables developers to pick any infrastructure provider from a partner menu to run their project. All users need to do is create an account, select the AI/ML infrastructure (any public cloud, on-premise, co-located, dev cloud, pre-release hardware, and more), and run the workload, the company said.

Plus, since there is no commercial commitment, developers can always change to a different infrastructure to meet growing project demands or budget constraints. The current list of supported providers includes Intel, AWS, Azure, GCP, Dell, Redhat, VMWare, and Seagate.

“AI has yet to meet its ultimate potential by overcoming all the operational complexities. The future of machine learning is dependent on the ability to deliver models seamlessly using the best infrastructure available,” Yochay Ettun, CEO and cofounder of, said in a statement.

“ Metacloud is built to give flexibility and choice to AI developers to enable successful development of AI instead of limiting them, so enterprises can realize the full benefits of machine learning sooner,” he added. Metacloud will be provided as part of the full-stack machine learning operating system, designed to help developers build and deploy machine learning models. The early access version of the solution can be accessed upon request via the company website.

Notably, this is the first major announcement from since its acquisition by Intel in 2020. Prior to the deal, the company had raised about $8 million from multiple investors, including Hanaco Venture Capital and Jerusalem Venture Partners.


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The Steam Deck May Not Run Your Whole Steam Library

When Valve announced the Steam Deck on Thursday, the company said that the platform “can pretty much run anything you could run on a PC.” Although that’s true in most cases, the “pretty much” qualifier could make a big difference. As it stands, popular games like Apex Legends, Destiny 2, and Rainbow Six Siege won’t work on the platform.

This comes down to an issue with SteamOS, a compatibility layer known as Proton, and anti-cheat software. SteamOS is based on Linux, which is a problem because the vast majority of games available on Steam are built to run on Windows. That’s where Proton comes in. Proton is a compatibility layer from Valve that allows Windows games to run on Linux.


Proton is available now as part of Steam Play, and many popular games work just fine with it. However, some big titles don’t. According to ProtonDB, only three of the top 10 most popular Steam games have native Linux support — Counter-Strike: Global Offensive, Dota 2, and Team Fortress 2 (all Valve-developed games).

Rust, meanwhile, has a “silver” rating, meaning it runs with minor issues, and Grand Theft Auto 5 has a “gold” rating, meaning it runs after tweaks. PlayerUnknown’s Battlegrounds, Apex Legends, Destiny 2, and Rainbow Six Siege are all in the “borked” category, meaning they’re critically unplayable.

The reason is anti-cheat software. This software has some issues with Linux and the Proton compatibility layer, which has been a problem for years for Linux gamers. The Steam Deck is bringing the issue center stage.

Most games that use some sort of anti-cheat software will either not work or will carry some significant problems. Easy Anti-Cheat, one of the most popular anti-cheat programs, maintains a list of games it is featured in. Comparing it with ProtonDB, the issue becomes clear. In addition to the games mentioned above, titles like Fall Guys: Ultimate Knockout, Outriders, and Dead by Daylight won’t work.

There is some good news, though. Valve says it is working with Easy Anti-Cheat and BattlEye — another popular anti-cheat program — to bring support to the platform ahead of launch. The issue isn’t with the anti-cheat software itself, but rather that’s built to run on Windows, not Linux. If developers can include Linux support, we could see support improve.

The other option is to install Windows on the Steam Deck, which is possible. However, Valve’s own SteamOS build will likely provide the best experience, as Windows is built to run on full PCs that include more powerful hardware.

If you primarily play single-player games, you’re in luck. Games like Monster Hunter: World, Cities: Skylines, and Red Dead Redemption 2 run just fine with Proton. Many multiplayer games do, as well, including The Elder Scrolls Online, Among Us, and Rocket League.  

Editors’ Choice

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Tech News

Windows 11 on OnePlus 6T run Crysis, Hitman 4

If the original Doom is often used as a litmus test for computing devices, both seriously and jokingly, Crysis is sometimes used to test the graphics capabilities of a computer or GPU. There might be other more graphics-intensive games these days, but the tradition has remained the same throughout the years. Now Crysis is again being used for something almost so unlikely you wouldn’t expect it to work, yet some did just make Crysis run on a OnePlus 6T running Windows 11.

On the one hand, it is cheating a bit since Windows 11 on ARM is a full desktop experience, not like Windows 10 Mobile. On the other hand, the hardware and software requirements of running a Windows game on a phone still make this quite an achievement, regardless of the actual platform. Consider that the game runs on a phone not designed to run such games is even more impressive.

When we say “run,” though, we really mean it in its most literal and minimal sense for a video game. As you can probably expect, the graphics quality can’t be pushed to its best settings, and there might be a few frames dropped here and there. It’s still playable, though, which is no small feat for a 2018 Android phone.

Crysis isn’t the only game that has been tested to run on Windows 11 on phones. Hitman 4: Blood Money is shown below, running with better frame rates and visual quality. Skyrim reportedly failed to make the cut. Ironically, no one has tried running Doom yet.

Unfortunately, these are tests with few practical long-term benefits. Microsoft is unlikely to allow Windows 11 to run on phones from other manufacturers and won’t be distributing official images to make that happen. It would be a complete shame, though, since these experiments show the possibilities if Microsoft pushed through with its own Windows 11 phone.

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