The ongoing component crisis has forced manufacturers to raise product prices around the world, which includes the best GPUs. MSI and PNY have just announced new prices for all RTX 50-series graphics cards for China, representing up to a 59% hike compared to MSRP. While Nvidia GPUs have been the most affected, offerings from Intel and AMD are also experiencing a similar predicament, according to Wccftech.

MSI's price list actually shows the old prices (from a week ago) as well as the new ones, giving us an official percentage difference of 10-20%. Keep in mind the old prices still weren't at MSRP. The RTX 5080 Shadow 3X OC/Ventus sees the biggest change, going from 10,000 Yuan ($1,477) to 12,000 Yuan ($1,773), constituting a 20% hike. Other RTX 5080 variants received a pretty similar 18% hike, while the RTX 5070 Ti Shadow 3X OC now costs 19.5% more.

The highest-end Blackwell GPU available in China, the RTX 5090 D V2 is a cut-down version of the RTX 5090, featuring 24GB of VRAM instead of 32GB. MSI raised its price by 13%, which is less than every RTX 5060 and RTX 5050 variant — those were around 14-15%. The RTX 5060 Ti (8GB and 16GB) and RTX 5070 both received price hikes under 10% compared to their old prices.

If you put these numbers up against these GPUs' MSRPs then the picture becomes a lot grimmer. The RTX 5070 Ti Gaming Trio OC is now priced 76% above MSRP, while its cheaper Shadow 3X OC variant is still 67% more expensive than the suggested retail price. The prices for the RTX 5090 D V2 are 57.5% higher, the RTX 5080 and 5060 Ti up to 56% higher, the RTX 5060 up to 45% higher, and the RTX 5070 up to 42% higher.

Moving onto Colorful's list, the company doesn't provide old prices to compare against, so we can only reference them against the MSRPs. The top-end offering, the RTX 5080 Vulcan W OC is now priced 59% above MSRP, while the 5070 Ti Vulcan W OC is 58.5% higher. The RTX 5070 and 5060 Ti 16GB models were largely under the 50% threshold, while price hikes for the RTX 5060 Ti 8GB and RTX 5060 variants were under 30% compared to MSRP.

Both of these lists came directly from the distributors but marketplaces online show that AMD and Intel GPUs are also more expensive now, as well. AMD's cards are selling for 14% to 29% higher prices, and Blue Team's cards are seeing $200 to almost $300 bumps, according to Wccftech. The RX 9060 XT 16GB is the worst offender among the two lineups, being hiked up by 28.8% compared to its MSRP.

All of this is happening because VRAM prices have skyrocketed over the past year, with reports highlighting a $20 price increase per module. That means an 8GB card, with taxes included, should cost almost $100 more now, a 12GB card about $130 more, and a 16GB card roughly $180 more, and that's just accounting for VRAM price changes. With prices going up, there's an additional element of supply and demand that is pushing prices higher.

There are no signs of the component crisis slowing down; in fact, we only see more fearmongering from major companies, persuading them to buy whatever they want now instead of waiting for some miracle market crash. With the way things are looking right now, prices don't seem to be coming down anytime soon, that much is clear.

A coalition of over 30 tech industry leaders, including Nvidia, Microsoft, SpaceX, The Linux Foundation, Adobe, and Siemens has formed the “Open Secure AI Alliance” with the aim of building and distributing open source tools for AI safety and security, according to an official Nvidia blog post on Monday. The Nvidia-led coalition — comprising a mix of infrastructure, cloud computing, cybersecurity, and enterprise software leaders — will serve as a collaborative effort to develop open tools for identifying and patching AI vulnerabilities, sharing security frameworks, and establishing identity verification and audit standards across the AI software stack. Curiously, some of the biggest names in AI, including OpenAI, Anthropic, and Google, are absent from the list of members.

“The world needs both closed and open models. For cybersecurity, open models and open harnesses are essential because they democratize defensive capabilities, increase transparency for defenders, enable cyber defense while protecting data, and complement frontier closed models with customizable, localized controls. Open source enables massively distributed community-driven and self-controlled defense – with no single point of failure,” the announcement reads. Contributors across the alliance are currently building or offering various tools to create an open defense stack.

The initiative was directly galvanized by the OpenAI HuggingFace security incident earlier this month in which an autonomous OpenAI test agent slipped out of its sandbox and breached the AI startup Hugging Face. During the incident, safety guardrails on several frontier closed models prevented developers from performing critical forensic analysis. Hugging Face eventually had to use GLM-5.2 — an open-weight model from Beijing-based Z.ai — running the model on its own infrastructure to analyze more than 17,000 actions and contain the intrusion.

Based on the incident, the alliance contends that being unable to inspect, modify, or run a model locally — impossible in closed systems but doable with open systems — presents a fundamental weakness in relying exclusively on closed AI systems for cyber defense. The Open Secure AI Alliance therefore aims to give entities access to advanced open models, agent harnesses, and security tools that they can independently deploy and adapt, reducing dependence on any single provider while strengthening defenses across a multi-vendor AI ecosystem. “That is the mission of the Open Secure AI Alliance: to ensure defenders everywhere have open, frontier tools they can trust and control,” the post says.

Chinese models such as DeepSeek and the newly released Kimi K3 are open-weight, and are seeing growing adoption, including by U.S. companies, due to their open features. Meanwhile the Trump administration is reportedly gearing up to ban Chinese AI models over security concerns. The alliance acknowledges the potential risks of open source tools but argues that closed systems are not an outright solution. “Those risks are real, but they do not disappear in closed systems, and simply keeping weights closed does not prevent determined attackers from seeking or exploiting powerful AI,” the announcement reads. Unlike regulators who have voiced concerns over open-source technology, the alliance urges policymakers to treat open-weight models as defensive assets rather than liabilities.

It also argues that placing AI development solely in the hands of a few closed providers creates dangerous single points of failure. “The right response is not to deny defenders access to capable open systems. It is to pair openness with strong safeguards, clear rules against malicious misuse, rigorous evaluation and rapid remediation. Defenders need both frontier closed models and frontier open models, working together, so they can choose the right system for the job and ensure that transparency, adaptation and sovereign control are available wherever security demands them,” the alliance contends.

According to the announcement, “The Open Secure AI Alliance — building on the leadership of the Linux Foundation’s Akrites initiative and OpenSSF community work — will work to remediate and disclose vulnerabilities using open technologies”. Founding members include NVIDIA, Dell Technologies, Synopsys, Microsoft, IBM, Red Hat, CrowdStrike, Palo Alto Networks, Cloudflare, Hugging Face, Databricks, SpaceXAI, and The Linux Foundation. Conspicuously absent from the alliance are OpenAI, Google, and Anthropic, companies behind proprietary, "closed" AI models.

Well, the artificially intelligent cat is out of the bag. After publishing a blog post and API documentation for the minty-fresh Kimi K3, Chinese outfit Moonshot AI delivered on its promise to release the model's weights for free, meaning that most anyone with a contemporary rack of AI GPUs can run it and charge for it, with few restrictions.

This is quite the shot across the bow of the big AI players, namely but not only Anthropic and OpenAI. Those companies' latest models are Claude Fable and GPT-5.6 Sol, respectively, and it happens that Kimi K3's capabilities outright beat previous generations of Claude and GPT in Moonshot's benchmarks, and closely trail Fable and Sol— all while seemingly being around 2-3x cheaper to run, up to 10x if a particular query lands in the cache. Moonshot's technical write-up seemingly backs up the benchmarks published last week, as the company reveals which exact software was used for testing.

For its inference cost comparisons, Moonshot says that its costs "are measured internally" versus the publicly available token pricing for other companies, but the figures are quite impressive. For input, Moonshot charges $3 per million tokens for Kimi K3. Meanwhile, Fable costs $10/1M, while Sol goes for $5/1M. That figure is standard non-cached input and is already pretty good-looking, but Kimi K3's caching structure seemingly has a 90% hit ratio for coding tasks, turning those $3 into $0.30/1M if your use case hits the cache a lot. The story is pretty similar for output tokens.

One of the likely reasons why Kimi K3 is so efficient is that it uses a mix of MXFP4 for weights and MXFP8 for input activation, both data types with relatively low precision and thus amenable to running on far less VRAM. Out of Kimi's 2.8 trillion parameters, only 104.2 billion are activated at a time, too.

Interestingly, Moonshot's write-up only mentions Nvidia's H20 being used for running Kimi for some coding tests, a fairly low-end chip by today's standards. That GPU doesn't have native support for MX floating-point types, unlike the export-controlled Blackwell B-series chips.

In turn, this can mean that Kimi K3's optimizations make it particularly amenable to run on lower-end hardware, but it's an equally reasonable guess that running it on something like Nvidia Blackwell or other MXFP-native silicon could make it even more cost-effective than in the presented benchmarks. We'll have to wait for more official figures to confirm this speculation.

Additionally, Kimi K3 doesn't use a conventional ever-expanding key-value (KV) store, instead relying on a fixed-size state handler called Kimi Delta Attention, again theoretically saving both on VRAM and execution time. Its mixture-of-experts (MoE) is particularly sparse with only 16 activated at each time out of 896, further contributing to inference cost reductions. Broadly speaking, Moonshot went for optimization at every layer of inference to avoid unnecessary overhead and bring inference cost down.

This is could be bad news for OpenAI and Anthropic, given that most anyone with decent AI GPUs can now become their direct competitor, and the fact that Kimi K3 is open-weight also gives off the impression that "free" software is nearly as good, and far cheaper to run, than its proprietary competitors. It's worth noting that open-weight does not mean open-source; the training process and dataset are still Moonshot's special secret sauce.

A state-backed company in Shanghai has begun mass-producing immersion deep ultraviolet lithography machines and is due to deliver the first units this year to SMIC, Hua Hong Semiconductor, and memory maker ChangXin Memory Technologies, according to The Information, citing two people familiar with the program. Output targets around five machines in 2026 and roughly 20 in 2027, and all three named recipients sit on the list of Chinese firms that a bill now moving through Congress would cut off from ASML sales and servicing by statute.

The Information didn't name the manufacturer, but its sources described the operation as having pulled DUV development teams from several Chinese companies, one of them the state-backed startup Shanghai Yuliangsheng Technology. SMIC has been testing a Yuliangsheng immersion tool since September 2025. Most components in the new systems are domestic, though some critical parts still come from Japan, and delays at local suppliers have held back output this year.

U.S. House Resolution 8170 designates SMIC, Hua Hong, CXMT, Huawei, and YMTC as restricted entities in law, and three of those five are the named first customers for the domestic scanner. The MATCH Act, introduced in April, was reported out of the House Foreign Affairs Committee on April 22 and has a Senate companion filed as S. 4281. Its immersion DUV provisions cover servicing and technical assistance, not just new exports, which extends its scope to installed tools already operating in Chinese fabs, fabs which have spent the past two years stretching that installed fleet through secondary-channel upgrades.

ASML expects to ship about 130 immersion systems in 2026, matching 2025, CFO Roger Dassen told analysts during the company's July earnings call. Dassen added that ASML intends "to increase capacity by 30% in 2027" for immersion, and is investigating another 30% for 2028. China accounts for around 20% of ASML's net sales this year, down from 33% in 2025, driven mainly by mainstream logic demand.

Immersion DUV prints 28nm-class features in a single exposure and reaches 7nm through multipatterning, at a cost in overlay errors and yield. ASML CEO Christophe Fouquet told the same call that rising DRAM litho intensity partly reflects customers replacing multipatterning with cheaper single-exposure EUV.

Independent analysis from the AI Futures Project in June put commercial-scale Chinese immersion DUV in the mid-2030s, with ASML holding 98.7% of the immersion market. Qualifying the new machines for production lines could take many months, and they trail ASML's tools on performance and build quality. China's domestic EUV effort, which Reuters first reported as a working prototype in December, remains years away.

What kind of mouse do you employ for your daily PC or laptop usage?

Is it a simple two-button mouse, or does it feature more buttons than you can shake a stick at?

Personally, I’m lucky enough to have a mouse for all occasions, as sometimes it’s great to have a peripheral that’s targeted to a specific task that it can excel at. An example of my personal usage is my Logitech G Superlight 2 - this is my current go-to mouse for when I get a little serious about playing my favourite FPS games. But this is the exception, as there are usually not that many buttons needed to be pressed and certainly no need for added functionality such as macros, although the software still supports it.

The main mouse I employ, though, has for years been an MMO/MOBA mouse of some description. For both gaming and a plethora of productivity applications, I’ve found that the intuitive functionality of a multi-button mouse offers me the best experience and streamlines my workflows, as well as saving my hand from cramping due to complex keyboard press combinations.

One of the first mice I started using this way was the Razer Hex when it first came out. This MOBA mouse had the usual left/right mouse buttons and a scroll wheel, but had an extra six buttons on the side panel nearest my thumb. There was a little changeable rubber nodule in the middle of the ring of six buttons to rest your thumb on and grip the mouse, so as not to accidentally press any of the side buttons. It took a while to get used to training the muscle memory of my thumb to use these extra buttons, but it was well worth the effort, as it freed up even more assignable buttons on my keyboard and also sped up my reaction times considerably in high-octane game sessions.

For games such as World of Warcraft and Final Fantasy XVI, the default spells and abilities start populating the number row on your keyboard from the 1 key to the =+ key (1-12), with further spells, abilities, and movement keybinds being assigned to other keys that are comfortable to reach with your non-mouse hand. There are also modifier key press combinations to double up on the number of things you can assign your buttons to on both the keyboard and the mouse, and that’s not even counting the fact that your free hand is also navigating WASD and the Space bar for movement.

Although I persisted with the Razer Hex for a while, I soon desired more buttons and switched up to a Razer Naga, which sported the now-infamous 12-button side panel that's been imitated by almost every other MMO mouse since. Operating 12 buttons with just your thumb does take a lot of practice to commit the button positions to memory, with different manufacturers using slightly differing button design shapes to facilitate easier recognition.

Some of the most popular MMO mouse models are Razer’s Naga, SteelSeries’ Aerox 9, Corsair’s Scimitar, and Redragon’s M908 and M913. An honorable mention goes to Logitech’s G600, which is no longer in production but was a great entry in the MMO mouse lineup and can still be found online, albeit at inflated prices due to its rarity.

Using accompanying software such as Synapse, iCUE, or SteelSeries GG, you’re able to reassign almost every button on a mouse, including DPI buttons. Apart from using an MMO mouse as an 18+button spell-slinger in gaming, these mice are super handy in other areas.

I use my SteelSeries Aerox 9 for video editing and some serious spreadsheet action. Yes, you can do the same with a number pad on a keyboard, but having all your favorite commands at your fingertips just speeds everything up.

How many times do you copy and paste? How often do you use a certain formula? Repetitive tasks can be simplified by making a macro or even recording a process and assigning it to one of the 12 side buttons. To make the most out of the mouse, you can set up application-specific profiles. The mouse software will then automatically detect when you're using a program such as Premiere Pro or DaVinci Resolve and instantly swap your thumb grid to the specific shortcuts you’ve set up for each application.

Remember, you can also set a modifier key so that you can increase the number of assignable buttons. The more buttons, the merrier, and once you’re completely familiar with your setup and workflow, you can edit an entire rough cut without your left hand ever touching the keyboard. Take a moment to think of your most-used tasks, what buttons you press to achieve them, and then program them onto the mouse, turning multiple buttons into just one. Ta-da!

If you’ve never dabbled in an MMO game, you may not be aware of how much of a game-changer a multi-button mouse can be. If you’re using multiple keyboards or macro pads like the Stream Deck for your workflow, you should definitely consider trying out an MMO mouse to improve your efficiency. It does take a little time to fully acclimatize yourself to using a mouse with a 12-button side panel, but it’s worth it, and I certainly recommend giving it a try. I can’t imagine going back, so I hope they keep making MMO mice for many years to come.

All of the mice I have mentioned in this article are available for purchase, with the Razer Naga V2 Hypserspeed Wireless available from Best Buy for $64.99, the SteelSeries Aerox 9 Wireless available from Amazon for $134.99, Redragon’s amazing value M908 Impact available for just $23.07, and finally Corsair’s Scimitar Wireless Elite SE at Amazon for $113.99.

I recently tested the MSI Claw 8 EX AI+ handheld gaming PC, and came away incredibly impressed with its performance. Put simply, it has no rival in gaming performance, easily eclipsing all other handhelds on the market. Even better, the battery life is also stellar.

However, if there’s one thing that it could use, it’s a storage boost. Despite the $1,800 price tag as tested for our review unit, it still only includes a 1TB SSD. After installing a handful of games from Steam and from the Xbox Store, I was left with 128GB free of the 953GB available on the SSD.

I happen to have a 2TB PNY CS2150 PCIe 5.0 SSD that I used for testing a Thunderbolt 5 SSD enclosure with Thunderbolt 5 docks. Since it was currently sitting unused, I decided to take a stab at replacing the 1TB SSD in the Claw 8 EX AI+ with the CS2150 for some extra storage headroom.

Getting the hardware setup for the transition

Fortunately for me, much of the legwork had already been finished. The CS2150 was already installed inside an Orico SSD Thunderbolt 5 enclosure, which would serve as my destination drive.

Although I could complete the entire transfer process using only the input methods on the Claw 8 EX AI+, it’s rather tedious to navigate the Windows 11 user interface and enter text on the 8-inch screen. So, I decided to use a Thunderbolt 5 dock to streamline the process. I used:

• OWC Thunderbolt 5 Dock
• Logitech wireless keyboard and mouse
• Orico SSD Thunderbolt 5 enclosure with 2TB PNY CS2150 SSD installed

I connected the Thunderbolt 5 dock to one of the two Thunderbolt 4 ports at the top edge of the Claw 8 EX AI+. I then plugged the Logitech USB-A receiver into the dock. Finally, I connected the Orico enclosure to the second Thunderbolt 4 port on the Claw 8 EX AI+.

Finding the proper software for cloning the drive

It’s been a while since I last cloned a drive, so I did a quick Google search for free drive-copy software for Windows 11. My search led me to a Reddit thread where the majority of commenters recommended the freeware version of Macrium Reflect 8.

I downloaded the software and installed it without issue. The interface in Reflect 8 was about as straightforward as you can expect, with my source C: drive showing up at the top. The destination field was empty, so I had to select the drive I wanted to use. I pulled up the CS2150, which I have named as Orico in Windows 11 as the D: drive.

After clicking Next, I was prompted with a warning about BitLocker encryption, so I clicked OK to acknowledge it (you’ll definitely want to re-enable BitLocker once you reinstall the SSD). The next screen showed a summary of the operations to be performed: copying the EFI and Windows partitions to the 2TB SSD. I clicked Finish, which brought up another prompt asking if I wanted to run the backup. I clicked OK, which displayed a warning that the D: drive would be overwritten. After clicking Continue, the process started.

After about an hour and a half, the cloning process was completed. At this point, I shut down the Claw 8 EX AI+ and disconnected all of the accessories.

Disassembling the MSI Claw 8 EX AI+ and installing the 2TB SSD

Next, I removed the back panel of the Claw 8 EX AI+, which required removing six screws and using a plastic prying tool. I then removed the single screw holding the already-installed 1TB SSD in the M.2 slot and extracted it. I next removed the 2TB SSD from the Orico enclosure and installed it in the M.2 slot, securing it with the screw I had just removed.

I then reinstalled the back panel for the Claw 8 EX AI+ and secured it with the previously removed screws.

Booting up and checking my work

I powered the Claw 8 EX AI+ back on, and it booted without issue. I was also surprised to see that Windows 11 didn’t throw up any activation errors. I’ll chalk that up as a win. I went to Windows Explorer to see if I was indeed utilizing the full 2TB of storage, but I instead saw that 128GB was free of 953GB – the same predicament as before.

I might have missed a setting during the cloning process that would have expanded the partition to use all free space on the larger SSD. But the fix was simple enough. I opened up the Disk Management utility, selected Windows (C:), and chose “Extend Volume.” After that process was complete, I saw the full 2TB (actually, 1.81TB), with 1.02TB now free for additional game installs.

Today marks 20 years since the first raft of Intel Core 2 Duo processors, codename Conroe, was launched. Ten Intel Core 2 Duo and Intel Core 2 Extreme processors were unveiled for consumer and business desktop and laptop PCs and workstations on July 27, 2006. We were lucky enough to test Intel’s now legendary new desktop processors earlier in the month, and our reviewer anointed the Core 2 Duo “the new king.” Our early hands-on review underlined that “as soon as Core 2 Duo hits the market, it will outperform the complete Athlon 64 family (X2 and FX) in all areas, including gaming, where AMD has traditionally been very strong.”

The GHz race ends with an architectural revolution

One of the defining characteristics of the first Intel Core 2 Duo chips was that they kicked the GHz race to the periphery of the battlefield. After years of chips being sold with this performance statistic placed most prominently, Intel and the tech media had to educate the wider public that higher GHz numbers didn’t define performance.

Intel Conroe desktop chips came with a generational performance uplift we don’t see often. These third-generation dual-core processors from Team Blue would “provide up to a 40 percent increase in performance and are more than 40 percent more energy efficient versus Intel's previous best processor,” according to launch-day PR. Testers also observed that even the entry-level new Conroe chips could outpace the mighty flagship desktop Pentium Extreme Editions, despite running at nearly half the clock speed.

Behind the real-world performance successes Intel eagerly highlighted, and reviewers seemed genuinely excited by, there were a number of architectural innovations and refinements. Intel boasted that the Core 2 Duo contained “a whopping 291 million transistors” and had achieved many benchmark firsts in internal tests. Conroe arrived with higher efficiency, shorter pipelines, improved branch predictions, new shared Smart Cache, and substantially higher IPC, all built upon Intel’s newest 65nm process technology. With this attractive new price, it wasn’t difficult for Intel to retire its former king, with its hot, power-hungry, and big GHz Netburst architecture.

Defining the next decade+

With the Core 2 Duo, Intel regained its performance leadership from AMD and its Athlon parts. Conroe, the desktop Core 2 architecture, didn’t get singled out in our five best Intel CPUs of all time article (2024), but it was the direct ancestor of the legendary Core 2 Quad CPUs. These quad-core CPUs would take the Gillette-like next logical step with four cores on a chip, combining two Conroe dies in a single package, and launching in January 2007.

Moreover, from the mid 2000s onwards, multi-core became mainstream and developers seriously began to optimize applications and games for processors boasting more than just Core 0. Admittedly, single-threaded performance can still be important in Windows/apps/games in 2026.

To conclude, Intel’s Conroe would set the foundations for the firm’s CPU market dominance for more than a decade. Most would argue this successful run lasted all the way until the AMD Ryzen family matured and hit full stride with the Ryzen 3000 series.

If you want a Windows or Linux laptop that you can upgrade and repair, you should be looking at Framework. But to maintain that repairability, the company's build quality has always left something to be desired.

The Framework Laptop 13 Pro ($1,199 to start without SSD, RAM, or OS, $1,599 to start pre-built, approximately $3,272 as tested) is the company's attempt to shake that image, with a CNC aluminum chassis, a haptic touchpad, a high-resolution touchscreen, and other premium features that hadn't previously made it into Framework's repair-friendly laptops.

This design is far and away better than its existing chassis, but launching a laptop based on repairability when parts for it are extremely expensive puts the Laptop 13 Pro out of reach for many.

Design of the Framework Laptop 13 Pro

The Framework Laptop 13 Pro is a big step up in design from its predecessor, the original Framework Laptop 13. It has a new, blacked-out color scheme as opposed to Framework's go-to silver. But more importantly, it feels like a much more premium product.

The CNC-machined aluminum chassis has significantly less flex than the regular Framework laptop, which would sometimes bend with just one hand. That's not to say there's no bending, specifically around the display, but this is an improvement that made me feel far more confident having the laptop in my backpack.

The chassis has an almost velvety feel. I can't explain it, because this is solid aluminum, but it has some sort of coating that feels unique in my hands. The black color, however, is a fingerprint magnet.

With the laptop open, you'll find one area that doesn't feel as premium — the bezel, which is still plasticky and attached with magnets. It doesn't look or feel as premium as the rest of the system, and many other laptops have just a small portion below the screen, allowing for thinner borders, especially at the top.

This keyboard deck, too, feels more solid. There's a bit of give, sure, but the magnetic attachment and five Torx screws (more on that in upgradeability, below) feel a lot more like one consistent piece when assembled.

Our review unit came with the "graphite gray/black" keyboard, with distinct orange accents on the Framework keys (an extra $10). It looks really cool and should be the default, but there are also all-black options and one with lavender keys in place of the gray.

At 11.68 X 9.02 x 0.62 inches and 3.17 pounds, this system easily fits into my backpack. It's similar in size and thickness to a MacBook Pro (12.31 x 8.71 x 0.61 inches, 3.4 pounds with M5).

Upgradeability of the Framework Laptop 13 Pro

Framework's modularity continues on in the new design. In fact, if you wanted, you could build a Laptop 13 Pro around your old Framework mainboard, Ship of Theseus style (though some parts would need to be swapped simultaneously to accommodate the new battery's shape and size).

We reviewed a DIY Edition of the Laptop 13 Pro, which meant installing a Framework-supplied SSD and LPCAMM2 memory before getting started. The only tool you need is the Torx T5 screwdriver that comes in the box.

The SSD is a pretty standard install, but the memory, which is LPCAMM2, was a first for me. You have to be extremely careful with it, aligning the memory with a sensitive interposer (which also happens to be slightly under a ribbon cable connected to the screen. This got in the way for me a few times, but it's easy enough to get around. There's a memory cover that goes on top of the LPCAMM2, with three screws labeled in the order you should tighten them to firmly seat the module.

On AMD Ryzen AI 300 configurations, LPCAMM2 isn't available: you use standard SO-DIMMs.

From there, you need to attach the input cover using a ribbon cable (do take a minute to admire the underside of the haptic touchpad and its four piezo sensors), which then snaps into place on the chassis. The bezel snaps onto the bottom of the screen and also attaches with magnets.

From there, you flip the laptop over and tighten five captive Torx T5 screws. Pop in your expansion cards, lock the slots, and voila, you're ready to install an operating system.

On the Intel version we tested, there's nothing limiting where you place expansion cards. On models using AMD's Ryzen AI 300 chips, however, you'll want to be sure not to put USB Type-A ports in the rear slots, as they'll use more power than other cards.

If you buy a prebuilt version, the OS will come pre-loaded. Otherwise, you can largely reverse any of these steps at any time to make upgrades, including likely eventual mainboard improvements.

My main criticism is that LPCAMM2 is hard to get right now (harder, in general, than regular SO-DIMMs, which at least some enthusiasts might have around). You can get them from Framework, but this isn't a standard that you have a lot of options to buy from. In that way, the DIY version doesn't make a ton of sense, unless you really, really want to bring your own SSD.

Framework Laptop 13 Pro Specifications

Productivity Performance on the Framework Laptop 13 Pro

We tested the Framework Laptop 13 Pro in its most powerful configuration: with an Intel Core Ultra X9 388H and 64GB of RAM. Our system also had a roomy 2TB SSD. To best compare the system to other laptops in our benchmark database, we opted to run Windows 11.

On Geekbench 6, the Framework Laptop 13 Pro notched a single-core score of 2,952 and a multi-core score of 14,965. The single-core score was similar to the same X9 chip found in the Asus Zenbook Duo. But in multi-core, the Zenbook had a score that was 15.49% higher, likely due to cooling; Framework is cooling Intel's top mobile chip with a single fan. Apple's M5 MacBook Pro and Qualcomm's Snapdragon X2 Elite in the HP OmniBook Ultra 14 both scored higher on both tests.

The Framework Laptop and its WD Black SN7100 SSD were the fastest of the group in copying 25GB of files, with a speed of 2,800.33 MBps, followed most closely by the OmniBook at 2,620.91 MBps.

On Handbrake, the Framework transcoded a 4K video to 1080p in 4 minutes and 9 seconds, ahead of the Duo but behind both ARM-based laptops.

To stress test the system, we ran Cinebench 2026 ten times and monitored scores and CPU speeds. The scores were largely in the 3,800 and 3,900's, with a peak score of 3979.85 on run 6, before scores started dropping down slightly. In HWInfo, the CPU’s P cores averaged 3.16 GHz, while the E cores reached 2.75 GHz, and the low-power E cores measured at 2.61 GHz.

Graphics on the Framework Laptop 13 Pro

As we've seen in other Panther Lake laptops, the Intel Arc B390's Xe cores are no joke.

The 12 cores on the X9 388H enabled the Laptop 13 Pro to achieve a score of 1,362 on 3DMark’s Steel Nomad cross-platform benchmark. It lost only to the presumably better-cooled Zenbook Duo using the same chip.

Display on the Framework Laptop Pro 13

The Framework Laptop 13 Pro marks the company's first custom display, along with the first time its 13-inch laptop has had a touchscreen (touch was previously available on the Framework Laptop 12).

The 13.5-inch, 2880 x 1920 display (one of the few 3:2 screens on the market these days) offers a variable refresh rate between 30 and 120 Hz.

Most importantly, Framework's display is exceedingly bright. In the trailer for Avengers: Doomsday, an explosion in the X-Mansion and lightning coming from Thor's Stormbreaker axe were eye-popping.

The panel measured an astounding 709 nits on our light meter, handily surpassing the MacBook Pro's mini LED screen (558 nits) and the OLED panels on the Asus Zenbook Duo (456 nits) and HP OmniBook Ultra 14 (414 nits).

The Framework's display was largely on par with the MacBook's in terms of color volume, covering 114.3% of sRGB and 81% of the more challenging DCI-P3 color space. The MacBook was a fraction of a percent better. The bigger differences are in the benefits mini LED may bring against LCD, but the point remains that for Framework, this is an impressive display. If older Framework Laptop 13 owners take one upgrade from this system, it may well be the panel for the sake of the quality, even if they don't care about touch.

Keyboard and Touchpad on the Framework Laptop 13 Pro

The keyboard cover on the Framework Laptop 13 Pro is one large piece sitting on top of the components, just like previous designs. Here, the keyboard has white backlighting and 1.5 mm of key travel.

And unlike some laptops, you can feel all of that travel. As I breezed through the monkeytype.com typing test at 123 words per minute, I did note that the keys feel a bit stiffer than I'm used to on other laptops. A colleague I showed the laptop to liked the effect, though I think some people might hope the keys wear in a bit.

This laptop also marks the first time Framework has offered a haptic touchpad. This one uses four Piezo sensors to convert pressure into clicks.

On the one hand, it's a huge step up from the company's mechanical touchpads, which often felt cheap. I never had any issues with gestures or navigation. But I could sometimes see and feel the haptic touchpad flex in the keyboard deck, which could probably be fixed with a bit of extra reinforcement.

Audio on the Framework Laptop 13 Pro

The Framework Laptop 13 Pro uses a pair of 2W side-firing speakers, rather than the bottom-firing speakers on the original Framework Laptop 13 design. If you're using Windows 11, you get Dolby Atmos support (tough luck for now if you're a Linux user), including the Dolby Settings app.

Despite Framework's claims that they're louder, they're really best for personal listening. Alice Merton's "Ignorance is Bliss" only barely filled my living room. The vocals, guitars, and drums were distinguishable and sounded pretty good otherwise.

It's not uncommon for wristrests to vibrate while playing music, but on the Framework Laptop, that extended to the haptic touchpad, which would noticeably move under my finger while listening to music.

Battery Life on the Framework Laptop 13 Pro

Framework is debuting a larger, 74 WHr battery on the Laptop 13 Pro. Even when powering the Core Ultra X9 388H, the system was long-lasting. On our battery test, which browses websites, streams video, and runs light OpenGL tests with the screen at 150 nits, the Laptop 13 Pro ran for 15 hours and 55 minutes, beating other Windows-based competitors. Only the MacBook Pro surpassed that, at 18 hours and 14 minutes.

Heat on the Framework Laptop 13 Pro

As configured, the Framework Laptop 13 Pro can get a bit toasty. Again, the Core X9 388H is running under a single fan.

During our Cinebench 2026 stress test, the keyboard hit 110 degrees Fahrenheit, which did feel warm to the touch. The haptic trackpad measured 90 F, while the hottest spot on the bottom was 117 F.

Internally, the CPU measured an average of 90.1 degrees Celsius and reported throttling for much of the back of the test.

Webcam on the Framework Laptop 13 Pro

The 1080p webcam on the Framework Laptop 13 Pro is serviceable, but not the best.

While colors were accurate enough in video and photo testing, there was graininess in both well-lit and dark images.

Additionally, this laptop doesn't sport an infrared camera for Windows Hello. Your only option is the fingerprint reader.

Software and Warranty on the Framework Laptop 13 Pro

We tested the Framework Laptop 13 Pro with Windows 11, and it was blissfully free of bloatware. Framework's dedicated shortcut on the F12 key opens up a page on its website for guides for the system.

I would like to see an easy way to customize the keyboard. VIA's web app doesn't recognize the keyboard in my testing (previously, it has only recognized the 16-incher and its various input modules). I hope to see support for that soon. (At least the key tester function worked).

Framework sells the Laptop 13 Pro with a one-year warranty. An extended three-year warranty is available that varies based on configuration. Framework told Tom's Hardware that this is because the "cost of servicing the warranty scales with the cost of the system," so more expensive systems with pricier hardware have more costly extended service plans.

Framework Laptop 13 Pro Configurations

We tested the Framework Laptop 13 Pro in a DIY configuration that's $1,799 with an Intel Core Ultra X9 388H before adding in memory, storage, an operating system, or any of the extras like the expansion cards.

If you purchased all of our components from Framework, the system was approximately $3,272 when we started testing it. In the middle of testing, the price of the memory on the DIY systems nearly doubled from $849 to $1,600, making our configuration roughly $4,023. That's without an OS, as we installed our own copy of Windows.

That being said, this configuration is backordered, and Framework isn't taking further orders: "We are working with Intel and expect to get additional allocation of X9, but we don't have timing we can communicate on when it will be available for order," Framework said through a PR representative. It's also likely to see a price increase, as the company has already announced that its X7 and X9 chip prices are going up. It's very possible that by time you read this review, any number of components may have changed price.

The 2TB SSD was $505, while the keyboard was $10 (for the colors), as was the bezel ($10), though both of those aesthetic options can be had for free if you opt for black or a few other color options.

The cheapest DIY Edition, with an Intel Core Ultra Series 3, starts at $1,499. If you prefer AMD Ryzen AI 300 over Intel Core Ultra Series 3, that starts at $1,399 with a Ryzen AI 7 350.

On the prebuilt side, the cheapest Intel system (with the same Core Ultra 5 325, plus 16GB of RAM and 512GB of storage) is $1,599 (up from $1,499 during pre-orders), while AMD versions begin at $2,299 (up from $2,099) with 1TB of storage and 32GB of memory.

Bottom Line

When Framework debuted the Laptop 13 Pro, it promised a "MacBook Pro for Linux users." I'm not sure I agree. The MacBook Pro has an air of utility, while the Framework Laptop Pro 13 is more playful. That alone makes it feel different.

This Framework is by far its best-built laptop, feeling more premium and rigid than ever before, along with a bright screen and a haptic touchpad. But there are some areas, like the bezels and expansion ports, that still show the gaps.

The new silicon is also faster and more efficient than we've seen. We've complained about battery life on some older Framework systems, but Panther Lake is efficient enough (along with a 74 WHr battery) to bring Framework in line — and ahead — of at least some premium Windows-based competitors.

Price and availability may scare off some customers. Erratic, expensive chip pricing is the norm these days, but most people don't see that firsthand. They only see the bottom line. But also, at this price, you could get an M5 Max MacBook Pro with 32GB of RAM, which would way outperform this system on multi-core and graphics performance.

There is room for improvement, especially in cooling. But the biggest issue Framework has here for its niche audience is the price of components, especially for the DIY edition. But if you happen to have parts lying around, there's no question that this design should usurp its old one as quickly as possible.

OpenAI is in advanced talks to lease SB Energy's 10 GW data center campus in Piketon, Ohio, with Nvidia in discussions to guarantee roughly $250 billion of the financing behind it, the Wall Street Journal reported on Sunday, citing unnamed people familiar with the matter. The site would be OpenAI's first as a tenant rather than a customer of Microsoft, Amazon, or Oracle, and Nvidia is separately discussing financing the accelerators going inside, which could run to another $350 billion. Terms haven't been settled, and the arrangement could still collapse.

OpenAI has no investment-grade credit rating, and Nvidia's involvement would let SB Energy raise debt against Nvidia's balance sheet instead of its tenant's. Nvidia has already put $30 billion into OpenAI, which has raised its projected compute spending to around $750 billion through 2030, up from roughly $600 billion earlier this year, according to the Journal. Commerce Secretary Howard Lutnick controls allocation of the site's power, and Anthropic, Microsoft, and Google have all spoken to him about it in recent weeks.

Nvidia's Q1 FY2027 10-Q caps maximum gross exposure across all of its partner facility lease guarantees at $3.5 billion, shrinking as partners pay their lessors, with $712 million sitting in escrow against it. Nvidia took the guarantees in exchange for warrants and carries them as credit derivatives, describing their fair value as immaterial.

The first one, disclosed in the third quarter of fiscal 2026, was capped at $860 million with $470 million of escrow behind it. The partner separately contracted to sell the data center cloud capacity, and Nvidia retained the option to assume the lease for internal use or sublease it if the escrow and that contract came up short. Neither remedy has an obvious equivalent at a 10 GW campus on federal land.

Nvidia held $62.6 billion in cash, cash equivalents, and marketable securities when fiscal 2026 closed on January 25, against full-year revenue of $215.9 billion and net income of $117 billion. A $250 billion guarantee works out at roughly 71 times the guarantee book Nvidia has disclosed, more than a year of revenue, and about four times its cash.

SB Energy broke ground at the former Portsmouth Gaseous Diffusion Plant on March 20 alongside Energy Secretary Chris Wright, Lutnick, and SoftBank chairman Masayoshi Son. The site enriched uranium for the U.S. weapons program from 1954 until 2001 and is still being decontaminated. The Department of Energy had listed it among 16 federal sites opened to data center construction, and SB Energy leases the land rather than owning it. Powering the campus takes 9.2 GW of new natural gas generation plus $4.2 billion of transmission work with AEP Ohio, funded by $33.3 billion Japan committed under its trade agreement with the U.S. The first phase, roughly 800 MW, is expected in 2028.

OpenAI gave up on building its own data centers last year in favor of leasing capacity, and SoftBank carries more than $130 billion of debt while funding buildouts in Ohio, France, and elsewhere.

When we talk about running local AI these days, the conversation usually either revolves around mini-PCs like the RTX Spark or drifts into wistful thinking about home servers and ludicrously expensive professional GPUs. Well, I reckon the most impressive AI hardware trick in a good while just happened on a piece of silicon that costs less than a decent burger. Last week, a Ukrainian developer named Slava S, who simply goes by 'slvDev' on GitHub, dropped a project called ESP32-AI. It's exactly what you think: he got a 28.9-million-parameter language model running locally, entirely on-device, on an ESP32-S3 microcontroller.

If you haven't read any of our previous coverage of this tiny chip, ESP32-S3 boards offer about the best bang for buck in the whole computing world. You can snag one online with a protective case for under $20 here in the States, and bare boards are readily available for under $10 around most of the world. As you'd expect from a chip so cheap, it's not powerful. On this variant, the S3, you get exactly 512KB of SRAM, 8MB of PSRAM, and 16MB of flash memory, which is not very much memory at all. So how exactly do you cram a nearly 30-million parameter model onto a chip with less primary storage than a single raw photo from your smartphone?

Usually, to run an LLM, the entire model has to sit in your system's fast memory because the processor needs to constantly do math against every parameter to generate the next word. If you try to run a 29M parameter model normally on an ESP32, you run out of fast RAM instantly. The previous record for a chip like this was around 260,000 parameters by one Mr. Dave Bennett, as pointed out by Slava himself on X.

Our clever hacker got around this bottleneck by borrowing a brilliant architectural trick from Google's Gemma called Per-Layer Embeddings. He quantized the model down to 4-bit (making the total file size just 14.9 MB) and changed where the data lives; instead of trying to stuff the whole thing into the tiny 512KB SRAM or the only slightly-less-tiny 8MB PSRAM, he dumped the 25-million-parameter embedding table into the relatively-slow 16MB Flash memory. Because this specific model architecture only needs to pull a few rows from this table per token, the inherent slowness of the Flash memory doesn't choke the processor, and so the 512KB of fast SRAM is kept clear for just the "thinking core", the actual reasoning weights.

Now, let's pump the brakes for a second, because I know someone out there is already wondering if they can replace their server with an $8 chip. The model he used was trained on the TinyStories dataset, and it's really more of a Small Language Model (SLM), or honestly, a "micro LM." Due to the way it was created, it's only capable of writing short, simple, fictional stories. It will not answer questions, it will not follow instructions, it won't write your Python code, and it possesses exactly zero factual knowledge about the real world.

Focusing on that limitation completely misses the magic of what's happening here in this proof-of-concept, though. The achievement is fitting a structurally quite large model onto a computer with practically no resources. It proves that with clever architecture, you can run genuine neural networks on dirt-cheap embedded hardware, and there are useful applications for a model this size. Slava imagines the idea of a coffee machine that actually knows about coffee: every bean, grind, ratio, water temperature, all offline, no app required.

Truthfully, when we're talking about "AI", it all comes down to what you are trying to accomplish. To put it plainly, asking how much hardware you need for local AI without specifying the workload is like asking what vehicle you need without saying what the goal is. A bicycle, a sedan, a pickup truck, a semi-trailer, and a train all "get you from A to B," but they're built for radically different jobs. AI is the exact same way; it's what you're doing with it that determines how much hardware you need.

To illustrate the point, last year another developer published a project cramming an AI language model (a bigram name generator) into the original Dragon Warrior and Final Fantasy games on the NES. Yes, the Nintendo Entertainment System. Developer Emanuele Rodolà managed to fit the entire model weight table (729 bytes) and the inference code (~140 bytes of hand-written assembly) into the original game ROM to generate new character names on the fly. That's real AI, running on a MOS 6502 processor, a piece of silicon that dates back to 1975.

The ultimate takeaway from slvDev's project is that Per-Layer Embeddings scale far further down than most people would have imagined, and it lends credence to the recent enthusiasm surrounding High-Bandwidth Flash as a tiered storage medium for AI servers. That's exciting not because it means an ESP32 will replace your desktop GPU, but because it suggests the same architectural ideas could make AI dramatically more practical across the entire spectrum of hardware, from tiny embedded devices all the way up to datacenter accelerators.

A Steam Deck devotee was so fed up with their family borrowing their handheld and leaving it with a flat battery that they have resorted to quite extreme measures. On the Steam Deck subreddit, ConroyyJenkinss says that they have modded the console with a range of anti-sharing measures, including a 3D-modeled spiky case and a taser, among other things.

I Built a Security System for My Steam Deck from r/SteamDeck

In the video embedded above, you can see ConroyyJenkinss augmented Steam Deck, which has basically sacrificed the Steam Deck’s comfy ergonomics on the altar of defensive design. Its hand-filling gentle curves have been nullified with a 3D-printed case that was modeled by the anti-sharing Steam Deck owner in Blender. We hope this Steam Deck chastity belt is easily detachable by the owner for when ConroyyJenkinss wants to have some fun.

The hostile architecture-inspired case has another scary trick or two up its sleeve, though. “To make it worse, I rigged up a Raspberry Pi with a small speaker, an object detection camera, and a servo motor,” the Redditor explains. “[I] wrote a py script so if the camera sees someone in frame, it plays a goofy audio clip, waits a few seconds and then the servo triggers a taser to scare them away.” So at least those tempted to touch this spiky handheld get warned before potentially being tased.

Most importantly, ConroyyJenkinss asserts that their Steam Deck anti-sharing measures have done the job. “Nobody touches my Steam Deck anymore,” concludes the dangerous DIYer. Answering some queries in the Reddit thread, the embattled Steam Deck owner admits having to remove the case and disarm the taser to use their own handheld is “a hassle but overall worth it.” Now other family members ask permission to have a gaming session on the portable console.

In recent weeks, Steam Decks have joined the ranks of the desirable devices that have been heavily impacted by the RAMpocalypse. It’s nice to share things, but we fully understand ConroyyJenkinss protecting his precious unit, especially against the devil-may-care battery drainers in the family.

AMD used its recent Advancing AI 2026 event in San Francisco to launch sixth-gen EPYC "Venice" processors, Instinct MI400 Series GPUs, and Helios rack-scale systems, and to confirm that the Zen 7 generation arriving in 2028 will launch as three separate EPYC families rather than one.

The company named Florence, Ferrara, and Fidenza in its launch release, extended its annual CPU, GPU, networking, and rack cadence out to 2030, and put its total addressable market at roughly $2 trillion in 2030. It also introduced a competitive yardstick it hasn't used before, claiming the most AI agents per watt, per dollar, and per rack, though its own endnotes state those agent counts are estimated from CPU thread resources used as a proxy.

Three Zen 7 CPUs

Florence carries fresh Zen 7 cores, a new set of AI compute extensions, and support for newer memory technologies, AMD chair and CEO Lisa Su said during the keynote. Ferrara is the AI host node portion, and it appears a second time further along the roadmap as the CPU inside the Helios 600 rack alongside MI600 Series GPUs and Pensando "Palma" and "Levanzo" networking. Fidenza, meanwhile, is the agentic sandbox product. AMD disclosed no core counts, no process node, and no socket for any of the three, and said only that Zen 7 uses leading-edge process technology.

The fourth-gen EPYC generation, built on Zen 4, spanned Genoa, Bergamo, Genoa-X, and Siena across two sockets. The fifth-gen "Turin" generation then went the other way, folding Zen 5 and Zen 5c parts into a single 27-SKU stack on one socket with no separate cache-stacked or edge line at launch. Venice restarts the fan-out, with the 9006 series on the new SP7 socket now, and Venice-X arriving in 2027 with 1,152MB of 3D V-Cache, 96 cores, and a 5.15 GHz boost clock. Three named Zen 7 families at announcement, two years out, is a wider spread than AMD has ever opened a generation with.

AMD's own portfolio endnote describes the EPYC range as covering general-purpose enterprise, cloud, telecom, SMB, and HPC systems, plus, as a distinct category, sandboxed agentic AI deployments and GPU head node servers. Su told analysts in May that AMD was already working with customers on architectures beyond Venice, without naming categories at the time. The Zen 7 lineup puts a name to those two AI-specific segments for the first time.

Agents per rack

AMD's main server CPU claim at the event is that sixth-gen EPYC enables the most agents per watt, per dollar, and per rack. Endnote 9xx6-012 in the launch release states that agent counts are estimates derived from available CPU thread resources used as a proxy under a consistent theoretical workload, and that real capacity varies with workload, model, memory, software, orchestration, and system configuration. The per-rack comparison behind it is core count at a 100 kW rack power envelope, pitting the 256-core EPYC 9996 against an 88-core Nvidia Vera, AMD's own 192-core EPYC 9965, and Intel's 128-core Xeon 6980P. The per-dollar metric is based on top-of-stack thread count divided by the 1,000-unit list pricing.

The per-watt comparison in that endnote lists Nvidia Vera at 450W and Arm's AGI CPU at 300W with one thread per core, alongside Intel's Xeon 6980P at 500W and AMD's EPYC 9965 at 500W. AMD had already claimed a 3.3 times rack-level advantage over Vera in June. Mercury Research put AMD at a record 46.2% of x86 server CPU revenue in Q1 2026, against 33.2% of units, and Arm-based designs took roughly 17.7% of server shipments in the same quarter, so the widening comparison shows where these units are going.

Starting with sixth-gen EPYC, AMD has replaced TDP with a figure it calls Default CPU Power, defined as total power consumed across the processor's compute and I/O dies at a stated performance target. AMD says both references can serve for product comparison and performance-per-watt analysis, and the endnote itself mixes the two conventions, quoting the EPYC 9956 at 400W Default CPU Power against TDP figures for the Nvidia, Intel, and Arm parts.

2030 cadence

Helios racks pair 72 Instinct MI455X GPUs with 18 Venice CPUs, 31TB of HBM4, and 1.4 PB/s of aggregate memory bandwidth, and are in production now. AMD claims up to 30% more inference tokens per dollar than Nvidia's Vera Rubin NVL72, based on AMD Performance Labs estimates from July 2026 using a Kimi K2 Thinking workload at 32K input and 8K output, with hourly GPU pricing projections. The 34-times token throughput gain AMD quotes for MI455X over MI355X comes from AMD's own measurements on DeepSeek V4 Flash at FP4. Both, however, are vendor-provided benchmarks with no independent verification yet.

The forward roadmap runs MI500 Series GPUs in 2027 inside a Helios 500 rack built on EPYC "Verano" and Pensando "Como" and "Monza" networking, MI600 Series in 2028 inside Helios 600 on Ferrara, and Ravenna on Zen 8 in 2030.

OpenAI expects to bring Helios online from the fourth quarter of 2026, with deployments accelerating through 2027, while Meta is validating sixth-gen EPYC platforms in its labs and has begun testing Helios racks. Anthropic committed the day before the keynote to up to 2GW of MI455X GPUs in Helios systems, with the first gigawatt due in the first half of 2027. SemiAnalysis reported in February that manufacturing delays would push mass production and first production tokens on an MI455X UALoE72 system to Q2 2027; AMD software chief Anush Elangovan publicly rejected that assessment and said Helios remained on target for 2H 2026.

AMD's cautionary statement in the launch release lists the availability of essential components, naming memory supply specifically, among the risk factors that could cause results to differ from its projections. A Helios rack carries 31 TB of HBM4, and DRAM contract prices roughly doubled quarter-on-quarter in Q1 2026 before rising again in Q2.

Residents of Dowagiac, Michigan, just filed a lawsuit against a data center that allegedly generates a high-pitched whining sound — and has done so 24/7 for the past two years.

The data center, which is owned by Alliance Cloud Services LLC, a subsidiary of Hyperscale Data, used to be an industrial building that sat behind a row of pine trees across the street from the most affected residents, according to ABC-affiliateWXYZ. The site started development in 2018 and was eventually turned into a cryptocurrency mining center in 2021. But in 2024, something changed drastically when the building started emitting noise pollution around the clock. Residents say it sounds like a vacuum cleaner in their living room, and the company has offered to buy up homes from unhappy residents.

“It sounds like someone set up a vacuum, like in your living room. And the vacuum is just... that thing needs to be cleaned ... the filter is clogged up, so it’s a high-pitch whining. And they just left it on and walked out," said Lindy Valenzuela, one of the residents living across from the data center. Billy Finn, who also lived nearby, added, “You’ve seen movies and stuff where they have somebody in a cell torturing them with sound. And that’s basically what it is."

Dowagiac has recently instituted an industrial noise ordinance, with a daytime limit of 65dB during the day and 55dB at night, and it has fined the data center for violations. However, Hyperscale Data is challenging the city’s readings and methodology. The company said that it’s planning to expand its operations in the footprint in the area, but the city said that it hasn’t received any permit applications.

Hyperscale Data CEO William Horne told the residents directly in a special council meeting that the site is pivoting away from cryptocurrency operations towards AI computing and advanced robotics, and that it’s spending $100 million to achieve this. It has also bought acres of adjacent properties for use as a natural buffer to reduce the noise that affects residents in the future, alongside other efforts that will reduce sound levels. "If they still aren't happy, and feel that their home isn't enjoyable, then we'll buy their property from them,” Horne said.

Still, this statement did not sit well with the affected residents. One of them said that their family has been living in the area for close to a hundred years, with their friends and support system, while another has a 17-month-old baby and another on the way, meaning moving for them is going to be difficult, if not impossible. Because of this, the residents asked the CEO why they didn’t act on the complaints as soon as they started and accused the company of not being a good neighbor.

This isn’t the first noise pollution lawsuit that a data center is facing in the country. A Microsoft data center is facing a similar class-action lawsuit in Wisconsin from residents who live within 1.5 miles of the facility. One non-profit organization also said that inaudible vibrations, called infrasound, that these industrial sites emit can be heard and felt for hundreds of feet in surrounding areas and could potentially have negative health effects on anyone who can feel it.

ChangXin Memory Technologies closed its first day on Shanghai's STAR Market at 49 yuan on Monday, up roughly 466% from an 8.66 yuan offer price, giving China's only volume DRAM maker a market capitalization of about 3.3 trillion yuan ($487 billion) and the top spot on the mainland market ahead of Industrial and Commercial Bank of China. The company raised 57.92 billion yuan ($8.6 billion) in Asia's largest IPO of 2026, and its prospectus assigns the bulk of the named project spending to wafer lines and process upgrades for DRAM it already produces, with nothing earmarked for high-bandwidth memory.

The prospectus splits 29.5 billion yuan across three projects: 13 billion yuan for DRAM technology upgrades, 9 billion yuan for next-generation DRAM research, and 7.5 billion yuan for memory wafer manufacturing line upgrades. The filing contains no dedicated HBM project and no disclosed funding commitment to a near-term HBM expansion, and CXMT hasn't broken down where the remaining roughly 28 billion yuan goes beyond describing it as working capital.

Conventional DRAM yields more than three times the bits per wafer that HBM does, and SemiAnalysis models CXMT's 8-high HBM3 yield at around 25%. Its cost per bit on DDR5 runs more than 30% above Samsung, SK hynix, and Micron. CXMT will add around 85,000 wafer starts per month of DRAM capacity this year, against 60,000 at SK hynix, 30,000 at Micron, and 15,000 at Samsung, per SemiAnalysis estimates.

That puts the company on course for roughly 350,000 wafer starts per month by the end of 2026, within 25,000 of Micron's total on Citrini Research's model, before a Shanghai fab two to three times the size of its Hefei headquarters reaches volume production in 2027.

It has been reported that output is already booked through the end of 2027, with DigiTimes having cited supply chain sources and Dell, HP, Lenovo, and Apple ahead of smaller buyers in the queue. CXMT signed a five-year server DRAM agreement worth more than $7 billion with ByteDance this month, and a $3 billion deal with Tencent in June, and server products grew from 8.4% of its revenue in 2024 to 26.5% last year.

Nomura opened coverage with a buy rating and a 116 yuan target, 1,239% above the IPO price, on an assumption that CXMT's share of global DRAM output climbs from about 10% now to 18% by the end of 2028. Morningstar puts fair value at 14.90 yuan, under a third of Monday's close, citing the company's lack of access to EUV lithography as the constraint on further conventional DRAM scaling. Nomura's downside case, built around potential equipment and materials embargoes, cuts 2027 to 2028 net profit by 30% to 33%.

Only 6.73% of CXMT's enlarged share capital was tradable at listing, and the lock-up expires on January 27, 2027. Buyers of the resulting modules aren't getting a discount, either. Retail DDR5 kits using CXMT dies track big three pricing, and early testing has shown the dies resist voltage scaling and overclock poorly next to SK hynix parts.

The AMD Ryzen 7 7700X3D is AMD's brand-new Ryzen 7 CPU, designed to slot neatly between the 7800X3D and the 7600X3D. While it's not a stellar offering as a standalone CPU upgrade, in this bundle featuring 16GB of DDR5 RAM for $16, it's a no-brainer as the perfect start to an AM5 gaming build.

Right now, you can get the 7700X3D at Newegg with an MSI Pro X870-P motherboard, 16GB of DDR5 G.Skill Ripjaws M5 (1x16GB), and a free Cooler Master Elite Liquid 240 CPU AIO for just $588, a $233 saving that gets you the RAM for basically nothing.

• Check out this deal on Newegg

While this bundle does limit you to single-channel RAM for the time being — it's just a single stick of 16GB DDR5 — it's an ample start for a mid-range gaming build in the current economy. If you really wanted, you could buy a second stick for the list price of $249, and get yourself 32GB of DDR5 for $265, which is much cheaper than the current going rate for RAM. If you factor in the free cooler, you're also saving another $80.

The 7700X3D can't match the more expensive 7800X3D or the hallowed 9800X3D, but as you can see from our testing, it can sustain average frames of 164fps in our 1080p gaming test suite, making it a great option for 1080p and 1440p gaming. This Zen 4 chip has 8 cores and 16 threads, and features 104MB of Cache.

That this bundle comes with a single stick of 16GB DDR5 6000 RAM is a small downside. It won't be as performant as two 8GB sticks, but it does leave you the option to upgrade to 32GB in the future, with timings of 36-36-36-76 and a black aluminum heat spreader. The M5 comes with addressable RGB lighting and support for both AMD EXPO and Intel XMP 3.0, the former being the pertinent one in this AM5 build. It's a small compromise that allows you to save a ton of money and get your hands on one of AMD's latest X3D processors for less.

The motherboard is MSI's Pro X870-P Wi-Fi board, which has a sleek aluminum look. The board supports up to 256GB of DDR5 RAM (four DIMM slots) for that aforementioned future upgrade, as well as Wi-Fi 7. It also has support for both PCIe 5.0 and 4.0 for M.2 SSD storage, and 5G LAN for super-fast networking.

The final cherry on top is Cooler Master's Elite Liquid 240 CPU AIO Cooler, a 240mm cooler that will keep your CPU temperatures down, giving you plenty of headroom for gaming. If you're looking to start a mid-range AM5 gaming build in 2026, you could do a lot worse than this bundle, which is one of the best we've seen in terms of RAM pricing in recent weeks.

If you're looking for more savings, check out our Best PC Hardware deals for a range of products, or dive deeper into our specialized SSD and Storage Deals, Hard Drive Deals, Gaming Monitor Deals, Graphics Card Deals, gaming chair, or CPU Deals pages.

You can save $1,000 on a truly high-end gaming laptop that, for many, is way over the top for the average user in both hardware and price. But if you want the most powerful laptop gaming setup, or a portable desktop replacement, then this Lenovo Legion 9i gaming laptop from B&H Photo, priced at $5,799, is something you should check out. It's certainly not your average gaming laptop, as it comes with the top-tier halo components from Intel and Nvidia, and is absolutely massive at 18 inches.

● Check out this deal at B&H Photo

Gaming laptops have always been more expensive than desktop gaming PCs with comparable components, even with the comparable components not being as powerful as their desktop counterparts due to the size, power, and heat limitations of stuffing hardware components into a smaller laptop chassis.

This model of the Lenovo Legion 9i contains the most powerful laptop graphics card available, Nvidia's RTX 5090 with 24GB of GDDR7 VRAM, and Intel's Core Ultra 9 290HX Plus 24-core processor, plus a massive 64GB of DDR5 RAM (2 x 32) with four RAM slots available, able to support up to 192GB of total RAM. For storage, there is a 2TB SSD, with Windows 11 Home edition pre-installed.

The luxurious 18-inch display features a detailed 3840 x 2400 resolution with a 3ms gray-to-gray response time. The display can reach up to 240Hz at native resolution and up to 440Hz when set to an FHD resolution. The display uses an IPS panel with 520 nits of brightness and a color gamut of 100% DCI-P3

The Lenovo Legion 9i also uses the latest Thunderbolt 5, WiFi 7, Bluetooth 5.4, and 2.5GbE Ethernet. The keyboard is fully RGB backlit with chiclet-style keys. This is a very powerful gaming laptop that you can also use for some serious creative work and go hard on some smaller local LLM models if you want to dabble with some AI workloads.

It is a very expensive laptop, made more costly by the price of RAM and NAND in the current economy. Still, it's also $1,000 cheaper than the usual price, so if this kind of laptop is something you're interested in, then head to B&H Photo and check out the RTX 5090-powered Legion 9i available for $5,799.

If you're looking for more savings, check out our Best PC Hardware deals for a range of products, or dive deeper into our specialized SSD and Storage Deals, Hard Drive Deals, Gaming Monitor Deals, Graphics Card Deals, Gaming Chair, Best Wi-Fi Routers, Best Motherboard, or CPU Deals pages.

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Imperial Valley Computer Manufacturing has filed a lawsuit in a bid to gain access to Colorado River water, 287 million gallons of which it says it needs to cool a 330-megawatt data center, which would be the largest in the state. Despite only representing a fraction of the region's water supply, the buildout of the data center may affect the local farming and adjacent industries and terminate hundreds, if not thousands, of positions, reports Business Insider.

After two cities in the region denied the California-based AI data center recycled wastewater for cooling, it filed a lawsuit demanding to get water from the Colorado River for cooling. The 330-megawatt facility was not only designed to be the biggest AI data center in California, but it specifically committed not to use water from the Colorado River because it was promised wastewater. But now the owner of the data center is essentially asking to redirect water supply from agriculture to the facility.

Imperial Valley Computer Manufacturing — the owner of the 330 MW AI data center — is requesting access to approximately 287 million gallons of water per year after two cities — El Centro and Imperial — declined to supply reclaimed wastewater for cooling. The Imperial Irrigation District (IID), which distributes Colorado River water throughout Imperial Valley, also denied the company's request. The Colorado River supplies water to roughly 40 million people across seven western states and serves as the valley's sole freshwater source for roughly 180,000 people. Agriculture consumes about 80% of California's allocation from the river, while roughly 95–97% of the water IID delivers goes to agriculture.

The data center is seeking roughly 287 million gallons per year (about 750,000 gallons per day, or ~880 acre-feet per year), whereas the Imperial Irrigation District (IID) holds rights to approximately 3.1 million acre-feet of Colorado River water annually, which means that the data center demands only a small fraction — 0.028% — of IID's total water supply.

Sebastian Rucci, a Huntington Beach attorney who leads the project, claims that the facility's water consumption would be comparable to that of a 160-acre farm and will require no additional Colorado River allocation. In fact, he states that the facility would not increase pressure on the river because the company intends to purchase nearby farmland together with its associated water allocations.

Under the proposal, irrigation on those properties would cease, thus transferring the existing water quotas to be redirected to the data center cooling, at the expense of local farming output and associated jobs. "There's a lot of resistance in any agricultural community to 'buy and dry' because that's jobs," a senior fellow at the Pacific Institute focused on Colorado River Basin water use told the outlet. According to them, local resistance to the plan is less about the amount of water, and more about buying up farmland and reallocating it for industrial use.

The approach, of course, differs from the earlier plan that intended to avoid using Colorado River water altogether. However, after the data center was denied wastewater from two cities, it does not have a choice if it wants to go ahead with the buildout.

Rucci reportedly indicated that the project would provide substantial economic benefits for the local community, including 1,688 construction jobs, more than 100 permanent positions, and an estimated $2.95 billion in economic impact over 30 years. For a region where unemployment stood at approximately 17% in May, the economic diversification is essential. However, the big question is whether 100 permanent roles could offset the lost positions in the farming industry and industries tied to agriculture.

Water policy specialists interviewed by Business Insider said that the debate extends beyond the project's annual consumption. Instead, they questioned whether converting irrigated farmland into industrial use is an appropriate long-term direction for the region, which has historically depended on farming. The experts also warned that although landowners could benefit from selling land or water rights, surrounding rural communities may lose employment and business activity adjacent to agriculture, which includes equipment suppliers, repair shops, and sellers of fertilizers. Another factor mentioned by the experts was the U.S. reliance on farms around Imperial, California, and Yuma, Arizona, as they were the main suppliers of certain agricultural products in winter.

Physicists at the Universities of Konstanz and Stuttgart have run chaotic-signal forecasting and anomaly detection on 400 microscopic particles orbiting in a drop of liquid, in work published in Communications AI & Computing. The array predicted a chaotic Mackey-Glass series and picked out anomalies that leave a signal's mean, variance, and short-time autocorrelation untouched, scoring an F1 of 0.90 on that harder task. It also came in roughly 10 times less accurate than memristor-based reservoirs, a gap the paper admits candidly.

Each oscillator is a silica sphere of 3μm radius, capped on one side with 80nm of carbon and suspended in a water-lutidine mixture held at 28°C. A 532nm laser heats the cap and drives the particle toward an assigned target point, but the delay between imaging a particle and repositioning the beam means it overshoots and settles into a small orbit instead. Flow fields in the liquid couple neighboring orbits, and data enters the system as displacements of the target points.

Lattice spacing sets coupling strength, since hydrodynamic forces fall off with distance, and a damping threshold sets how far each particle swings. Both are adjustable while the experiment runs, with a forecasting error that varies by more than a factor of three across that parameter space. Accuracy also held up when the input reached only 20% of the oscillators, and when individual particles stopped responding to the laser or clumped together.

The colloidal array reached a normalized root-mean-squared error of about 0.1 on the one-step Mackey-Glass prediction. Memristor devices now reach 0.01 or better on the same benchmark, the paper notes, adding that those results depend on time-multiplexing and follow nearly a decade of concentrated work.

The authors write that their reservoir doesn't outperform established physical implementations. An arXiv preprint from January, however, framed it differently, arguing that avoiding time-multiplexing set the platform apart from nearly all existing physical reservoirs, photonic, memristive, and spintronic ones included.

Running the reservoir takes a 532nm laser, a two-axis acousto-optical deflector scanning at 100 kHz, real-time microscopy with particle tracking, a temperature-controlled quartz cell, and a conventional computer for the 1,000 Gaussian kernels and ridge regression that produce the output. No energy stats appear anywhere in the paper, despite energy efficiency being the stated motivation, and the authors concede that the laser-driven setup might not be practically applicable and instead point toward simpler actuation schemes, such as electrode-driven colloids.

Clemens Bechinger, professor of soft condensed matter at the University of Konstanz, said in the university's announcement that the dynamics don't need to be fully understood, only to respond reliably, at which point "its physics can be directly harnessed for computation."

A separate team synchronized 105,000 nano-oscillators in 45 nanoseconds this month on a platform projected to run at tens of gigahertz.

MRI machines are life-saving medical devices that can let doctors and radiologists diagnose various critical conditions, but they’re also insanely expensive. Brand-new models start at $1.1 million and could go as high as $3 million per unit or more. The Open Source Imaging Initiative recognized this limitation and has been working on the open-source OSI2 ONE MRI scanner, which had already been replicated multiple times globally. However, this portable device, which has a 3D-printed core, has a limited field strength of just 50mT (compared to the 1.5T to 3T used by full-sized units). This gave them lower spatial resolution and lower signal-to-noise ratio, but tech analyst Brian Roemmele said on X that AI can overcome this and make it usable for medical diagnoses.

“Low-field MRI has historically been limited by lower signal-to-noise and greater field inhomogeneity. That is exactly the regime where modern AI thrives,” Roemmele wrote on the social media platform. “Image reconstruction becomes dramatically better when deep networks trained on high-field data or physics-informed models denoise, correct for inhomogeneity, and push resolution beyond the raw acquisition limits. Real-time sequence adaptation can adjust gradients and RF pulses on the fly as the AI monitors signal quality.”

Note that this isn’t just a general run-of-the-mill AI that everyone uses but a specially trained model on high-field MRI (1.5T to 8T) data or using the actual physics of the MRI machine so that it can create a more accurate picture. Scientists have already been using this technique for years, with some researchers training an AI model on 1.6 million brain scans to make it more accurate in detecting dementia. If an institution does not have access to anonymized patient data used to train the specialized AI, it can rely on synthetic data generation because of the open-source nature of the OSI2 ONE MRI scanner. Since all the information about the machine is publicly available, researchers could use this instead to build a physics model that the AI model can use.

Some people commented, saying that this won’t work in the highly regulated medical environments usually found in first-world countries. Nevertheless, Roemmele said, “No one can stop us from building in garages.” It also seems to be targeted for regions that have low access to technologies like these or do not have the financial capacity to purchase and maintain a full-sized device (even refurbished MRI machine units start at $100,000, and you also have to spend more to set up the specialized room that will house it).

While a portable MRI scanner like the OSI2 ONE will never have the resolution of the expensive, full-sized machines, it’s arguably better to have something that doctors can use for diagnosis without costing millions of dollars if the specialized AI model turns out to be effective and accurate. With that, even less wealthy hospitals and clinics could have access to this imaging device and save more lives. It also shows how the medical industry and even patients use AI to save on costs.

One of the common misconceptions in today's memory market is that once memory modules based on chips from CXMT enter the consumer market, there will be cheaper alternatives to memory sticks running DRAM from the Big Three. While availability of CXMT-powered modules certainly impacts average selling prices, these products are not cheaper than those based on ICs from Micron, Samsung, and SK hynix even in China, as noticed by @harukaze5719.

A 64GB DDR5-5600 RDIMM based on memory from Samsung or SK hynix costs 18,595 CNY ($2,745) at JD.com, whereas a module featuring the same capacity and specification, but using DRAMs from CXMT is priced at 18,999 CNY ($2,805), according to observations by @harukaze5719. It is noteworthy that a Samsung 64GB DDR5-5600 RDIMM made by Samsung and sold by Samsung costs $2,425 at Amazon.com in the U.S.

While the $60 difference seems significant, it is really just 2.2%, which can be considered negligible at these sky-high prices. Nonetheless, many expect CXMT-based memory modules to be cheaper than those carrying chips from Micron, Samsung, or SK hynix, so observing that they are even a bit more expensive than offerings with ICs from renowned manufacturers may be a bit surprising.

Indeed, because CXMT produces memory chips using an outdated fabrication technology, its DRAM ICs consume more power than those made using the latest manufacturing processes, have lower performance potential, and mediocre overclockability. Furthermore, the Chinese government heavily subsidizes both ChangXin Memory Technologies and Yangtze Memory Technologies (YMTC), which enables both to sell their memory at lower prices although their actual costs may be higher compared to those of the Big Three.

As a result, it is reasonable to expect CXMT to charge less for its chips compared to chips from renowned producers. Truth to be told, we do not know CXMT's quotes, they may be as high as those from other manufacturers and the only reason why module producers buy them is that they are the only chips available. Furthermore, companies tend to price products based on what the market will bear, not strictly on production cost or their characteristics. In fact, as everyone is capacity constrained these days, there is little incentive for CXMT to price its DRAMs significantly below those from renowned makers. However, while CXMT may indeed charge less for chips (e.g., because the Chinese government issues an appropriate directive), this lowers costs for module makers, but has a limited effect on retail prices of actual DIMMs or RDIMMs.

Whether potential savings reach end customers depends on market competition, supply-demand conditions, and the pricing strategies of module makers, distributors, and retailers. Furthermore, when prominent companies like Apple, Dell, or Corsair use a memory chip SKU, this one must pass rigorous validation processes and is usually tested in-house, or by contract manufacturers, which adds to costs and somewhat erases the price difference between suppliers.

As a result, CXMT memory chips may make the lives of hardware makers easier, but are not expected to impact the retail prices you pay.