Yangtze Memory Technologies Co. (YMTC) told investors and stakeholders in recent IPO preparation meetings that it aims to become the world's largest NAND flash producer by the end of 2027, overtaking Samsung and SK hynix, according to a Financial Times report. The Wuhan-based company filed last week to raise 33 billion yuan ($4.9 billion) on the Shanghai Stock Exchange's STAR Market, with most of the proceeds earmarked for production line upgrades and R&D.

The target would require YMTC to nearly double its market share in 16 months. Counterpoint Research put the company at around 14% of global NAND shipments in the second quarter, level with Kioxia, against roughly 25% for market leader Samsung and 22% for SK hynix and its Solidigm subsidiary combined. Analysts currently rank YMTC third globally, and first in China, by both NAND revenue and shipment volume in the first quarter.

YMTC, which has been profitable only since 2024, reported first-quarter revenue of 47.04 billion yuan ($7 billion) and net profit of 33.38 billion yuan, more than double its net profit for all of 2025. Gross margin, which sat at 5.45% in 2023, reached 35.3% in 2025 and 76.77% in the first quarter of 2026, with fabs running at 98.02% capacity utilization. The cost to get there has been significant for YMTC, which has spent 96.39 billion yuan in capital outlays on long-term assets and 15.95 billion yuan in cumulative R&D spending over the reporting period, generating depreciation and amortization charges of 50.95 billion yuan that leave margins exposed if memory prices turn.

YMTC plans to issue between 1.98 billion and 2.43 billion A-shares, representing 10% to 12% of its post-offering capital, in a deal sponsored by CITIC Securities and CSC Financial. Of the proceeds, 20.8 billion yuan is allocated to mass production line upgrades and 12.2 billion yuan to advanced R&D. This implies a post-listing valuation of 275 to 330 billion yuan, though the Financial Times reported that the company is expected to trade well above that once its shares debut, and that pricing will likely be set conservatively under regulatory guidance.

The listing follows the template set by ChangXin Memory Technologies (CXMT), China's leading DRAM maker, which raised $8.6 billion in July in Asia's largest IPO this year and surged 466% on its first day of trading. By mid-August, CXMT had overtaken Tencent to become the most valuable company listed in China, and both firms are expanding output with new fabs in Shanghai and Wuhan.

That expansion is what worries investors elsewhere in the sector. Joanna Yang, a portfolio manager at Ninety One, told the FT: "For global investors, one question is how these Chinese companies' capacity expansion is going to impact the supply-demand dynamics for memory. This is a global product — it has global pricing."

YMTC has been on the U.S. Commerce Department's Entity List since December 2022, cut off from advanced American fab tools. Its filing to list as the world's third-largest NAND supplier, with 76% gross margins, shows where U.S. export controls work and where they don’t.

The sanctions regime was built to choke access to leading-edge lithography, and it has done so effectively for logic. NAND competitiveness, however, runs through a different channel, with layer counts, stacking architecture, and hybrid bonding determining bit density, and none of them depend on EUV. YMTC's fifth-generation NAND bonds two decks of 150 and 144 layers into a 294-layer device using its Xtacking architecture, and the company has been building production lines around homegrown Chinese tools to close off the remaining dependencies.

YMTC’s listing turns that position into a funding mechanism. Margins earned at the top of the memory cycle are already paying for domestic tool development, and the IPO adds a capital source that sits outside the reach of U.S. policy entirely: Shanghai's equity market, where CXMT's debut has already shown how much money is waiting for exactly this kind of company.

The Trump administration is weighing a second round of semiconductor tariffs that would extend duties beyond chips to products built with them, including laptops, gaming consoles, and data center servers, eight unnamed sources familiar with the talks told POLITICO in a report published today. Commerce Secretary Howard Lutnick favors a structure that would cap duty-free chip imports at a volume pegged to each company's committed U.S. production, four of the sources said, and Commerce officials have indicated in private talks that the exemptions attached to January's 25% tariff, which cover data centers, R&D, startups, and consumer devices, may not carry over. A phase-in period is under discussion, and the framework could still change substantially in the coming weeks.

Proclamation 11002, signed on January 14, imposed a 25% duty on a narrow set of advanced accelerators, with Nvidia's H200 and AMD's MI325X named in the accompanying White House fact sheet, and explicitly labeled the action Phase 1.

The same document directed Commerce to report to the president by July 1 on the market for semiconductors used in U.S. data centers, and a separate April 14 report from the USTR and Commerce covered tariff negotiations with Taiwan, South Korea, and Japan. The exemption categories now in question match those written into the January action and include U.S. data centers, research and development, startups, repairs, non-data-center consumer and industrial applications, and public sector uses.

Taiwan's January trade agreement applies zero tariffs on Taiwanese chips within 2.5 times a company's current U.S. manufacturing capacity while new plants are under construction, tightening to 1.5 times once they're built. TSMC has committed $265 billion to its Arizona site, the largest foreign direct investment in U.S. history, yet projects only around 30% of its most advanced capacity there at full build-out, according to POLITICO. Taiwan produces more than 90% of the world's leading-edge chips, and industry representatives argued in the talks that a quota keyed to current domestic capacity can't cover the volumes hyperscalers are buying during a record AI spending run.

Jonathan McHale, digital policy chief at the Computer and Communications Industry Association, whose members include Amazon, Google, and Meta, compared the data center buildout to "building the transcontinental railroad" and said added cost and unpredictability put that investment at risk.

Tech lobbyists have met with Lutnick and Bureau of Industry and Security undersecretary Jeffrey Kessler with growing frequency since the start of summer, per the report, but three of the sources said recent talks moved against the industry. One person involved put the domestic manufacturing build-out at more than five years, longer than any phase-in the administration has allowed on previous tariff rounds.

White House spokesperson Kush Desai defended the approach, saying reshoring chip manufacturing is a top priority for the president; the Commerce Department didn't respond to POLITICO's requests for comment.

Cerebras' SRAM-packed wafer-scale engines (WSEs) have carved out a niche in the AI model serving space for extremely low-latency, high-throughput inference, enabling services like OpenAI's ChatGPT-5.6 Sol Ultrafast tier. At Hot Chips 2026, the company revealed the next two generations of its wafer-scale accelerator roadmap. It also discussed the benefits of its new Nexus rack design for the CS-4 rack-scale accelerator and the performance of the three WS-3T wafer-scale engines contained within.

The integration of a huge coherent processor on a single massive slice of silicon is a unique feat in the industry. But that approach also comes with limitations. AI demands for memory are only increasing due to growing model sizes (the memory occupancy of which can be amortized across multiple inference sessions) and ever-lengthening contexts stored in large KV caches (which are also unique to each inference session).

Traditional GPU makers have addressed those pressures, in part by working with memory makers to stack HBM higher and by using more of it per accelerator to expand that precious resource in proximity to the processor. But on a wafer-scale design whose area is already 100% utilized by logic and memory, adding more of a particular resource requires giving up area that might have been used for some other purpose. Since silicon production will continue to take place on 300mm wafers for the foreseeable future, Cerebras must look in other directions to scale up the on-chip resources available to its processors.

Cerebras revealed that it will start expanding its wafer-scale engines into stacked designs with its CS-6 system’s WSE, currently two generations out on its roadmap. For the first time, Cerebras will attempt 3D stacking of DRAM on top of its logic and SRAM wafer, a move it claims will maintain the company's performance lead for inference while reducing the area required for the overall chip.

The goal of stacking wafer-scale logic and memory chips on top of one another is certainly ambitious, but it’s only one potentially important change in the CS-6 system. The concurrent reduction in area Cerebras foresees suggests the company might be able to increase the overall number of WSEs it produces, which could relax a crucial constraint as the company seeks to scale its business amid a world of ever-increasing wafer demand.

In the present, Cerebras is boosting the performance of its existing wafer-scale platform with its new CS-4 rack-scale system and its Nexus rack design. CS-4 incorporates three of the company's refreshed WS-3T wafers into self-contained "backpacks" that incorporate power delivery, scale-up networking, and liquid cooling infrastructure into a single pluggable module.

Cerebras notes that because these modules are self-contained, future wafer-scale engines built with this architecture can be swapped in without exchanging the entire rack in the process.

Cerebras chief system architect JP Fricker had choice words when describing the 5,000 cables that are used to connect the Rubin NVL72 NVLink scale-up domain within each of those racks, calling it "a mess" and contrasting it with the cleaner and less failure-prone design provided by the on-die interconnects and self-contained compute module design of the Nexus system.

The Nexus backpack design also disaggregates the I/O interfaces of the WSE from the rest of the backpack's components. Two I/O modules now connect to the edges of the wafer, providing RoCE v2 RDMA connections for interoperability with other systems, alongside a direct connection to other wafers in the rack. Because these modules are also interchangeable, they provide another potential route for future upgrades, independent of the core compute wafer.

The Nexus design situates up to 10 rack power delivery units for each backpack at the front side of the rack, each group of which can be configured for varying levels of redundancy in accordance with an operator’s needs. The rack also provides air cooling for the backpack components that need it.

Mounting the wafer-scale engines vertically in the backpack modules lets Cerebras do away with a PCB or substrate for the wafer to handle all its supporting infrastructure. Instead, the backpack connects the large copper busbar that delivers juice to the chip directly to its back side. This close contact is important, as it minimizes power losses that occur on the way to the chip, as happens with a BGA GPU chip mounted on a PCB module with all of its power delivery circuitry located around the die.

Cerebras translates the power saved this way directly into performance in the WS-3T. The company says the losses avoided by the Nexus backpack design allow it to deliver twice as much power to the wafer-scale engine as in past designs, which leads directly to increased clock speeds and up to twice the performance of the WS-3.

Using the same base silicon as the WS-3, each WS-3T delivers twice as many sparse FP16 petaFLOPS and twice as much memory bandwidth from its SRAM. But the WS3-T is still limited to 44GB of memory across the entire wafer, and three such wafers in a CS-4 rack only scale up to 132 GB, far less than the 20.7 TB of HBM in the Vera Rubin NVL72 system and the 31 TB of AMD’s Helios.

The company doesn’t publish dense PFLOPS figures for these engines, possibly because the dataflow architectural design of the chips is specifically built to derive advantage from sparsity in a way a traditional GPU usually isn’t.

In any event, to accommodate the larger models of today and tomorrow, Cerebras will need to scale up and out. But unlike other rack-scale systems that rely on Ethernet for scale-out, Cerebras can simply connect CS-4 systems together using the same wafer-to-wafer interconnect that connects wafer-scale engines together in the Nexus rack. The company claims 2.4 Tb/s of direct scale-up bandwidth per wafer within the rack for a total of 7.2 Tb/s of inter-chip bandwidth at 2 μs latencies.

Cerebras notes that with its architecture, only the model activations need to pass between wafer-scale engines, so the relatively low bandwidth of the direct wafer connection isn't the obstacle to scaling out the system that it might seem when evaluated against the hundreds of terabytes per second of scale-up bandwidth of a system like Vera Rubin NVL72 or AMD's Helios. (The on-die fabric of the WSE-3T boasts 53.4 PB/s of bandwidth, regardless.)

The CS-4 system architecture lays the groundwork for the next-generation CS-5 accelerator, which will use new WSE silicon in 2027. For smaller models, the company says the next-generation WSE will deliver up to 10,000 tokens per second per user, while larger frontier models from labs like DeepSeek or OpenAI could run at 5,000 tokens per second per user.

As Nvidia CEO Jensen Huang has said, AI agents are impatient, and the ability to provide such vast numbers of tokens per second using specialized accelerators like the CS-4 will likely continue to be an important niche for Cerebras to exploit alongside its partners at OpenAI and AMD going forward.

Glass-core substrates, the replacement for organic chip packaging that Intel promised in September 2023 with more than $1 billion behind it, are now in final qualification. However, the product is still not in a single commercial product. SKC, a material manufacturer and chemical affiliate of the SK Group, said on its July 27 earnings call that embedded glass substrate samples from its Absolics plant in Covington, Georgia, are undergoing package-level reliability evaluation in Taiwan, with results possible before year-end.

Samsung Electro-Mechanics formalized a 482.1 billion won ($310 million) glass-core joint venture with Sumitomo Chemical's Dongwoo Fine-Chem on July 2, targeting first production in the second half of 2027. Intel, which started the race, has shifted to licensing its patents and showing demo vehicles, with its own deployment now pointed at around 2030. However, and rather predictably, every timeline in the segment has slipped. Absolics originally planned mass production for the first half of 2024, and reported claims that AMD would adopt glass substrates for CPUs between 2025 and 2026 have come and gone unfulfilled.

Why glass?

The technical case for glass core substrates leans heavily on numbers Intel published a while back, such as 10 times the interconnect density of organic substrates and a 50% reduction in pattern distortion. Glass cores can be tuned to a thermal expansion coefficient of roughly 3 to 10 ppm per degree Celsius against silicon's 2.6, which cuts warpage by about half compared with organic cores, and rectangular panels in the emerging 510mm x 515mm format use more than 75% of their area for large die against roughly 50% for round 300mm wafers. Through-glass vias have been demonstrated at six microns in diameter with aspect ratios beyond 15:1 at ECTC 2025, and Georgia Tech has shown stacked glass running at 220 GHz with 0.3 dB of loss.

Glass also chips and cracks at the edges during drilling and dicing, and MIT Technology Review reported in March that early Absolics production runs broke hundreds of panels every couple of days during early testing days. Edge-coating work has cut measured edge stress from 95 MPa to 49 MPa, and low-temperature dielectrics that cure below 180°C have been developed to reduce thermal stress during build-up, but metallizing vias below 10 microns and holding nanometer-scale flatness across half-meter panels remain open manufacturing problems.

Intel's program

Intel demonstrated a working system booting Windows on a glass-core substrate in early 2025, and Rahul Manepalli, Intel's VP of module engineering, told MIT Technology Review that the benefits of glass cores are "undeniable" and that Intel wants "to be one of the first ones who do it." The commercial plan around that engineering has changed shape, however. DigiTimes reported in late July that Intel is in early-stage talks with Chinese cover-glass maker Lens Technology about a packaging partnership, but there has been no solid agreement made to date.

At NEPCON Japan in January, Intel Foundry showed its first thick-core glass substrate with two EMIB bridge dies embedded directly in the glass: a 78mm x 77mm package with two 800-micron-class glass layers, 10 redistribution layers on each side, and around 1,716 mm2 of silicon on top, roughly two full reticles, with no micro-cracking reported in testing.

There’s currently no production time to this, however, with TrendForce placing Intel’s commercialization somewhere around 2030, alongside co-packaged optics prototypes built on glass at its Rio Rancho, New Mexico site. Meanwhile, Amkor, Intel's packaging partner on the optics work, put commercialization within three years at an industry event in Seoul in April.

Korea aiming for 2027

Samsung Electro-Mechanics moved its glass program from advanced R&D into a business-execution unit in February and has been sampling from a pilot line at its Sejong plant since late 2024. The GLASEM joint venture announced on July 2 splits ownership: 66% to Samsung Electro-Mechanics and 34% to Dongwoo Fine-Chem, with site production in Pyeongtaek, and targets an operating plant in the second half of 2027, with the joint venture making the drilled and metallized glass core that feeds Samsung's substrate line. Korean industry reporting says samples have gone to AMD and Broadcom, and also puts Samsung's overall glass maturity at 40 out of 100, a gap between marketing dates and process readiness worth keeping in mind.

Absolics' $600 million plant in Covington, Georgia, backed by $75 million in CHIPS Act funding plus a further $100 million through the government's advanced packaging R&D program with Georgia Tech, has a Phase 1 capacity of 12,000 m² of substrate per year, enough for roughly two to three million H100-sized packages. The company produced mass-production samples in the first quarter, began customer qualification that reportedly includes AMD and AWS, and is targeting mass production by the end of 2026. LG Innotek runs a third Korean program from its Gumi plant, with prototypes delivered in 2024 and production targeted for 2027 to 2028.

TSMC and Japan

TSMC's CoPoS line in Chiayi, built around 310mm x 310mm rectangular panels, received tools in February, completed its pilot line around June, and is aiming for pilot production in 2027, with mass production in the second half of 2028. Equipment supplier SCHMID has described glass integration in that platform as under review, not committed, and TrendForce puts TSMC's commercial-scale glass-core production after 2030, meaning that the industry's biggest packaging operation is going panel-level first and glass later, if at all. TSMC revived glass substrate research a couple of years ago after earlier deprioritizing it, reportedly under pressure from Nvidia, whose accelerator packages are the main thing outgrowing current packaging.

Japan’s Dai Nippon Printing began phased operation of a TGV glass-core pilot line at its Kuki plant in Saitama in December 2025 on 510mm x 515mm panels, with sample shipments from early 2026 and full mass production targeted for fiscal 2028. Toppan's pilot line for glass cores and interposers at its Ishikawa plant was scheduled for commissioning in July, and Nippon Electric Glass has scaled its ceramic-reinforced GC Core panel to 515mm x 510mm at 1mm thickness. Rapidus is studying panel-level packaging on 600mm x 600mm glass as part of its 2nm program, with viability put at the late 2020s. China has its own entrants, led by display maker BOE, whose pilot line is sampling.

TrendForce estimates Nvidia's Rubin Ultra package at roughly 7,470 mm², about nine reticles' worth of silicon and memory, against around 2,739 mm² for Blackwell, and CoWoS interposer wafers cost on the order of $10,000 each, comparable to a processed 7nm wafer.

Organic substrates warp and lose dimensional stability at those sizes, and round interposer wafers waste a growing share of their area, which is what glass panels are meant to fix. SEMI's first dedicated market report on glass cores, published in May with Global Net, projects initial production around 2028 in select high-performance applications and a 67.2% compound annual growth rate from 2028 to 2040, while Yole puts the advanced IC substrate market at $31 billion by 2030 with glass cores among the drivers.

At the moment, no production design exists, and all customers that have been named or otherwise attached to the tech, including AMD, Broadcom, AWS, and Nvidia, come from wider industry reporting. Nothing has been confirmed officially. As for whether we might see more substantial progress next year, Absolics will need to publish some solid package-level reliability results by the end of this year, or a first officially named customer at any of the Korean manufacturers. We also need to hear from TSMC on whether glass cores will go into CoPoS or remain under review into the 2030s.

Nvidia expects sales of the Vera Rubin platform to account for around 20% of its data center revenue in the third quarter of its fiscal year 2027, which will make it the company's fastest-ramping data center product in history. As Nvidia projects its revenue to be around $108 billion in Q3 FY2027, sales of Vera Rubin hardware alone will total around $20 billion in just one quarter.

"We see Vera Rubin accounting for about 20% of data center revenue in Q3," said Colette Kress, chief financial officer at Nvidia, during the company's earnings conference call with financial analysts and investors. "Having already received purchase orders from every major hyperscaler, AI cloud, and system OEM, we expect Vera Rubin to mark the fastest product ramp in Nvidia's history."

Data center revenue has accounted for around 92% of Nvidia's quarterly sales in recent quarters, so as long as Nvidia meets its $108 billion earnings projection in Q3, its data center revenue will reach around $99.36 billion, meaning that sales of its Vera Rubin platform hardware will be around $19.872 billion.

Nvidia began to ramp up production of Vera Rubin components — that include Vera CPUs, Rubin GPUs, BlueField 4 DPUs, and other units — this spring and commenced first revenue shipments of actual VR200 NVL72 racks in August, with Microsoft being the first to deploy commercial systems (at least according to Satya Nadella).

Normally, companies ramp up production and sales of their data center platforms for several quarters. Ramping up a data center platform from effectively no production revenue in Q2 FY2027 to 20% of revenue ($20 billion in this case) in just one quarter is a record not only for Nvidia, but perhaps for the whole industry, given Nvidia's scale. However, there is a catch: actual unit shipments do not look truly breakthrough.

Exact volumes of Vera Rubin units that Nvidia plans to ship in the third quarter of its fiscal 2027 are unknown. However, if Nvidia ships only VR200 NVL72 rack-scale systems, depending on their actual price and configuration (estimated from $5 million to $7.8 million per unit), Nvidia will supply from roughly 2,550 to approximately 3,975 machines containing 91,700 – 143,100 Vera CPUs as well as 183,500 – 286,100 Rubin GPUs.

These numbers are, of course, very rough since the company will sell a boatload of MGX servers and even Vera CPUs and Rubin GPUs separately, meaning that actual unit shipment volumes of these key components will be higher. Nonetheless, we are still talking about hundreds of thousands, rather than millions, of CPUs and GPUs, which is not particularly many. Still, given the stellar prices of leading-edge AI hardware, even relatively limited volumes of these components can generate tens of billions of dollars in revenue.

Remember back at CES when Samsung showed off a huge suite of Odyssey gaming monitors? The vast majority of those screens are now shipping, but arguably the most interesting monitor among them, the Odyssey G6 (G60H), still hasn't launched. Back at CES, Samsung was touting a 1,040-Hz refresh rate for that screen; now at Gamescom 2026, Samsung has shared the finalized specification, which is a nice and round 1,100 Hz. That's right; this monitor refreshes vertically at 1.1 KHz.

It's a fascinating display because aside from that specification, it's actually a bit of a pedestrian gaming monitor. You've got a 27" Fast IPS LCD in 2560×1440 (QHD) resolution, and at its native sharpness, it tops out at "only" 600 Hz. Surely that should satisfy anyone, but if you really feel the need for speed, you can drop the resolution to one-quarter of native, bringing you to 1280×720 (HD), and then refresh the panel at 1.1 KHz. That means each frame is displayed for less than one millisecond.

With its Fast IPS LCD panel and one millisecond response time, this new monitor will have its work cut out keeping up with those claimed performance figures. It's surely still going to be incredibly sharp and clear in motion (assuming your system can put out the requisite 1,100 frames per second), but it does seem like an OLED would have been the more appropriate technology for this feature.

Indeed, the rest of the displays that Samsung is showing off at Gamescom 2026, comprising its next-generation Odyssey family, are all OLEDs. Starting from the OLED G7 (G75SJ), we have what Samsung says is "the world's first 42-inch 16:9 curved OLED gaming monitor," which might be true. A 42-inch screen area is absolutely massive, and as you'd expect, to keep DPI up to reasonable levels, it's 3840×2160 (4K UHD) resolution.

The Odyssey OLED G7 G75SJ has a 1000R curvature, which is quite tight for a display of this size. It also has a 165-Hz refresh rate and a rated 0.03-ms response time, although it's an OLED, so you already knew that. Samsung doesn't really share any other specifications for this screen, but the firm does say that this monitor includes "Glare Free technology" to help reduce reflections for "clearer daytime play." That's very helpful on an SDR OLED because the peak brightness of this display is probably low enough that it becomes hard to see in daylight.

The next step up from the G7 is the Odyssey OLED G8 (G80SJ). This beautiful beast sports a 32-inch 3840×2160 (4K UHD) QD-OLED panel that tops out at 360 Hz and features "triple mode" support, meaning that you can drop the resolution to QHD for a 520-Hz refresh rate, or all the way down to 1920×1080 (FHD) for a blistering 680 Hz refresh rate. That's not as fast as the 720-Hz OLED that ASUS announced, but it's also a higher resolution, as that screen has to drop to 720p HD to hit 720 Hz.

The Odyssey OLED G8 (G80SJ) has a few other tricks up its sleeve, too. It supports FreeSync Premium Pro (and is G-SYNC Compatible as a result), and it is spec'd for VESA DisplayHDR TrueBlack 600, which should result in a pretty decent HDR experience, especially in darkened rooms. Notably, its USB Type-C port supports up to 98W of power delivery, meaning it can very rapidly charge whatever device you connect to it.

Finally, the star of the show, the Odyssey OLED G9 (G95SJ), is a 49-inch "DQHD" gaming monitor. DQHD is "Double Quad High Definition", and if you can do arithmetic, you already know that the resolution of this 32:9 monitor is 5120×1440. The super-ultrawide uses a fifth-gen (latest) OLED with "Penta Tandem" technology, meaning that it has a five-layer panel structure that Samsung says lasts twice as long as older OLEDs. It also uses an RGB stripe subpixel layout that should mostly eliminate the text fringing which plagues older-gen OLED monitors.

You've already heard me whinging about the brightness of OLEDs; that won't be a problem on the Odyssey OLED G9 because this thing apparently supports a peak brightness of 1,300 nits. That's admittedly only on a 3% window at 4K resolution, but it's a high enough value that it should mean HDR highlights are nice and vibrant on this screen, although Samsung curiously doesn't list a DisplayHDR cert for this one. Other critical specs include the 360-Hz refresh rate, dual mode support (meaning 720Hz at 2560×720), and the usual 0.03ms response time rating.

Unfortunately, there's no pricing or specific availability for any of these displays. Samsung says that the 1,100-Hz Odyssey G6 will be available globally in "the second half of 2026", which basically means "any day now." Meanwhile, the other displays are explicitly noted as 2027 monitors, where "availability will vary by market," and pricing is anybody's guess in today's tech market.

Researchers from the University of Mary Washington in Virginia found that of the 31 localities with an existing, approved, or proposed data center, 25 of them have non-disclosure agreements (NDAs) that make it difficult, if not impossible, for their citizens to find information about these projects. According to NPR, these NDAs have left the questions of many residents unanswered, making them feel left out of the decision process and increasing their distrust of their leaders.

Many officials defended signing these secrecy agreements, saying that this is standard practice among tech companies, and that they could lose the potential business and revenue that data center projects could bring to their jurisdiction if they fail to sign the NDA. Louisiana Economic Development Secretary Susan Bourgeois even told the publications that NDAs are “fundamental for economic development” as they allow the state to compete with other states and territories for the data centers’ business. “The last thing that they want to have happened in the beginning of the discussion, or in any part of the discussion, is for their information that they’re sharing to be disclosed publicly. So, without NDAs, our work, frankly, just doesn’t happen,” the agency secretary said to NPR. She also added, “The irony is that we, we use NDAs not to be more cloaked, we use NDAs to be more forthcoming.”

NDAs aren’t only limited to Louisiana, as other towns across the U.S. have signed these secrecy agreements even before they started talks with potential data center projects in their regions. Some Wisconsin citizens raised a similar issue in early 2026, when at least four municipalities reportedly signed NDAs with Microsoft and Meta as they negotiated with the two companies. These confidentiality agreements are some of the things that drive the heated debate between elected officials and ordinary citizens, with the latter often feeling betrayed and sold out by the former when they belatedly find out that a data center will be put up in their community.

However, West Feliciana Parish proved that it could get a data center deal without keeping its details top secret. Parish President Kenny Havard said that economic development does not require NDAs. “I don’t think NDAs are necessary unless they’re going to bring you in a room and share some proprietary information. When you’re talking about land sales, tax incentives, those sorts of things that affect the citizens, it should be out in the open. It should be transparent,” Havard told the outlet. He also said, “We’re proud that we did it without having to sign NDAs. And I think it just shows that you can do these projects without keeping the people in the dark.”

Ahead of the Labor Day weekend, HP is hosting its Labor Day sales event with discounts of up to 65% on HP gaming PCs, desktop computers, gaming and productivity laptops, and HyperX peripherals. If you're on the hunt for some great bargains, then take a look at our list of some of our favorite available deals.

With Labor Day looming, it also heralds the end of the summer break and the return to school, so taking advantage of a sales event like this is a great opportunity to pick up some essential pieces of tech at a discount ahead of the new semester, or just as a new gaming system; there doesn't need to be a reason to treat yourself.

An enterprising founder and indie hacker has successfully pre-sold enough advertisement sticker space on his dream MacBook’s lid to raise 111% of the laptop’s retail price. Vincent, AKA @VynseDev on Twitter/X, has so far raised $3,274 across a total of 10 sticker positions. With over 12 days of the auction remaining, it isn’t beyond the realm of possibility that the cheeky Bordeaux-based chancer could raise enough for several laptops.

Vincent admits that his Brand My Mac project isn’t an original idea. In a follow-up Tweet, the founder tips his hat to prior projects like Outbid by Jonathan Wilke and BrandMyTesla from Im Bulent. We also recall money-spinning digital online experiments in a similar vein, like the Million Dollar Homepage, and perhaps even the whole NFT industry.

However, the fact that these projects get column inches in media coverage shows that the auctioned advertising space isn’t just seen on Vincent’s new MacBook “both online and IRL.” Advertisers also might get their logos seen in articles like this, as well as in subsequent coverage that comes at the end of the auction.

While logos in the video are obviously placeholders, as Vincent also mentions on social media, the Brand My Mac website shows the current 10 leading bidders’ logos and positions in a rendered Apple laptop. Also on the dedicated auction website, you can see the current winning bid for each spot. Note that some spots command far higher premiums than others, and on either side of the immutable Apple logo there are a pair of half-sized slots.

The prime advertising real estate area of a laptop lid seems to be at the top-middle position, above the Apple logo. Currently, the leader here is RISTI with its bid of $761. For the fee, and by our understanding of the FAQ, Vincent will print and apply a “die-cut vinyl sticker of your logo” in the position you secure. A link on the Brand My Mac website is also thrown in. If you are interested in bidding, you must at least beat the current bid by €10 ($11.65).

With this latest ‘Brand My’ project going viral and already a surefire success, there are some copycat endeavors popping up. Actually, Vincent sees copycats as an opportunity so has decided to create a waitlist for followers to highlight any machine, at any price. Then Vincent’s backend build will have you covered: “the sticker spots, the auction, and everything else is handled for you,” says the enterprising founder and indie hacker.

The Environmental Protection Agency (EPA) has recently given two new chemicals approval for use within the U.S., but the non-profit organization Earthjustice is suing the agency for not doing its job. According to a lawsuit reported by The Guardian, these chemicals are photoacid generators used for semiconductor manufacturing, but they’re also quite toxic for humans. Exposure to them could allegedly lead to “sudden death,” as well as various health risks like cancer, eye corrosion, neurological damage, and reproductive harm. It is alleged that the approvals are "part of a broader pattern around data centers."

The current administration wants to bring semiconductor manufacturing back into the United States, with President Donald Trump issuing an executive order last year to fast-track the approval of chemicals needed for data centers. It’s unclear if these two chemicals are related to the order, but Atty. Jonathan Kalmuss-Katz, who works with Earthjustice, said that the EPA “does not know the level at which the chemicals are ‘acutely lethal’ or cause other serious health damage.” It also said that “the chemicals may present an ‘unreasonable risk’ to workers and the public.” Nevertheless, the agency allegedly approved them for import and use in the U.S. with minimal and non-protective restrictions.

“If a chemical may present an ‘unreasonable risk,’ then the Toxic Substances Control Act requires the EPA to ‘prohibit or limit the manufacture, processing, distribution in commerce, use, or disposal of such substance or to prohibit or limit any combination of such activities to the extent necessary to protect against an unreasonable risk,” Kalmuss-Katz told The Guardian. He also added, “This is turning the new chemical review process on its head. You have a situation where the EPA has failed at its most fundamental obligation when it comes to new chemicals, and that is to protect the public from unreasonable risk.” Aside from the health and environmental risks posed by the photoacid generators, these chemicals also appear to be PFAS or per- and polyfluoroalkyl substances. These are synthetic chemicals with unusually strong bonds that could last a long time, even if they’re exposed to the environment, which is why they’re often referred to as “forever chemicals.”

The semiconductor industry has long been known for its use of PFAS, and researchers are working hard to find ways to help clean up these chemicals used to make chips. So, the approval of new chemicals without rules and regulations that require them to be removed from discharges is potentially disastrous for the environment. This is the same concern that a lawsuit against Micron’s planned New York fab raises, which says that the approved wastewater and air permits that the company received could still allow these “forever chemicals” to leak into the Oneida River. Environmental groups acknowledge that domestic chip production requires the use of these potentially harmful chemicals, but the EPA must ensure that these do not find their way into the water system and that the people working with them are protected from exposure.

Nvidia has agreed to acquire Hugging Face for $12.9 billion, according to The Information, citing a person familiar with the deal. If the report is accurate and Nvidia indeed buys Hugging Face, the purchase could strengthen Nvidia's open-model strategy, provide another route to sell AI hardware, and help defend its hardware business as Anthropic, Google, OpenAI, and other major hyperscalers develop their own accelerators.

Nvidia sells hundreds of billions worth of AI hardware every year. Although the ubiquity of its CUDA software stack and leading performance of its hardware are the primary reasons why Nvidia's AI platforms are sold like hot cakes, another important factor is that many AI models were trained on Nvidia hardware and are optimized to run on it. Therefore, the more models trained on Nvidia hardware, the more products the company is going to sell eventually.

Hugging Face is an AI development platform best known for the Hugging Face Hub, a GitHub-like repository where researchers and developers publish, discover, download, and collaborate on AI models, datasets, and applications. Hugging Face also develops widely used software such as the Transformers library and provides tools and cloud services for training, optimizing, and deploying models on different types of AI hardware.

In addition to hosting models, datasets, and applications, Hugging Face provides software that helps developers optimize and deploy AI models on different CPUs, GPUs, and AI accelerators, while its Inference Endpoints service lets customers run models on managed infrastructure hosted by AWS, Google Cloud, and Microsoft Azure.

To make things simple, Inference Endpoints allows customers to select the provider, region, hardware type, and instance. What is important here is that the hardware used by Amazon, Google, and Microsoft is not all Nvidia. Hugging Face currently offers, depending on the provider, AWS Inferentia, AMD Instinct, Google TPU, Intel CPUs, and Nvidia accelerators, among other configurations.

OpenAI models, datasets, popular applications, and abilities to optimize and deploy AI models on different hardware make Hugging Face strategically important to Nvidia. On the one hand, the company can make the platform exclusively rely on its hardware, though this may face a backlash from the community, so this is something unlikely to happen in the short term (even assuming Nvidia is indeed set to buy Hugging Face). On the other hand, Nvidia wants open models to remain competitive with proprietary offerings from companies like Anthropic and OpenAI that may eventually get optimized for proprietary non-Nvidia hardware. Nvidia has been building its own Nemotron open models and has committed tens of billions of dollars to the effort. Furthermore, as Hugging Face grows, so is adoption of AI hardware in general and Nvidia hardware in particular.

Hugging Face is growing rapidly, but its revenue remains modest compared with the purchase price, according to The Information. The 10-year-old company recently reached approximately $150 million in annualized revenue, compared with about $100 million several months earlier, which puts Nvidia's price at roughly 80 times forward revenue, something that clearly highlights the strategic nature of the acquisition. Negotiations reportedly began after Hugging Face received acquisition interest elsewhere.

CEO and co-founder Clem Delangue said in June that paying subscribers doubled during the first half of 2026 and recently said the company was close to profitability. Demand has benefited from improving Chinese open models from Z.ai, Moonshot, and DeepSeek.

If Nvidia proceeds with the takeover, the transaction will be a part of Nvidia's increasingly aggressive investments across the AI ecosystem that spans from hardware to models to software. Last week, Nvidia agreed to pay $6 billion to license development technology from open-model developer Poolside and offered jobs to more than 100 employees. Nvidia also acquired Groq, Enfabrica, Essential AI, Illumex, and Kumo AI, just to name some.

OpenAI made quite a splash back in June, when it unveiled its 'Jalapeño' AI accelerator and revealed that the chip reached tape-out in just nine months. At the Hot Chips conference, OpenAI disclosed more details about the architecture of its Jalapeño inference processor as well as shared its target and real-world performance numbers. The company claims its NUMA-style spatial architecture enables Jalapeño to outperform Nvidia's GB200 and GB300 in low-latency inference and in terms of performance-per-watt, while a 2,048-processor system scales to 27 exaFLOPS and 32 PB/s of aggregate memory bandwidth.

A massive chip with massive scaling

OpenAI's Jalapeño is a massive AI inference accelerator co-developed with Broadcom, with 216 GB of HBM4 memory and up to 15.4 TB/s of bandwidth. The processor delivers up to 3.4 MXFP8 PFLOPS as well as up to 13.4 MXFP4 PFLOPS at 700W, which makes it suitable for inference, though the MXFP4 format may not be enough for training. The ASIC has a 700W power rating and operates at 1.70 GHz on silicon already running in OpenAI's labs, though OpenAI's engineers said at Hot Chips that the plan is to increase clocks up to 1.80 GHz, perhaps to get higher peak performance.

On the scalability side of matters, Jalapeño can scale to 128 accelerators in a local rack interconnected using Ethernet at 600 GB/s and to 2,048 ASICs in a 16-rack pod configuration at 200 GB/s per processor. The complete system offers 27 EFLOPS of MXFP4 performance, 432 TB of HBM4 memory, and 32 PB/s of aggregate memory bandwidth. For connectivity, OpenAI uses Broadcom Tomahawk 6 Ethernet switches and what it calls a 'half-flattened' two-level Clos topology that provides higher bandwidth for tensor-parallel traffic, lower bandwidth for expert-parallel communication, and prioritizes low latency for both. During the Q&A session, OpenAI confirmed that the scale-up network uses Ethernet with 200-Gb/s links. Jalapeño's physical hardware around the Broadcom-made chip is set to be made by Celestica.

On paper, Jalapeño's specifications look good, but they barely look impressive compared to Nvidia's Blackwell Ultra accelerators (10 FP8 PFLOPS, 20/15 sparse/dense NVFP4 PFLOPS). However, OpenAI argues that raw compute and memory bandwidth are not what makes its Jalapeño platform different.

The company says a 128-ASIC Jalapeño domain has more than 1 PB/s of aggregate HBM4 bandwidth, which is significantly higher compared to GB300 NVL72 (576 TB/s). A one-trillion-parameter model using FP4 weights requires about 0.5 TB. So, purely from a bandwidth perspective, the system could read the entire model more than 2,000 times per second. Meanwhile, actual inference performance comes nowhere near that theoretical ceiling, which is why hardware developers do not tend to add HBM bandwidth infinitely.

Smart data movement

Instead, Jalapeño uses what OpenAI describes as a memory-sliced, or NUMA-style, architecture. The chip has 64 core slices, and each of them is paired with its own HBM slice to guarantee predictable latency and bandwidth. OpenAI says this arrangement avoids conflicts associated with a unified memory subsystem and lets frequently used operands remain close to the compute resources that need them. OpenAI does not explain why it chose exactly 64 slices, which likely means it was a sweet spot for the current architecture.

To connect the 64 core slices, OpenAI uses a specialized high-bandwidth, low-latency collective network that moves data coupled to compute operations 'register-to-register, with zero conflicts' in a bid to eliminate several performance bottlenecks. In addition, Jalapeño has a separate general-purpose network-on-chip (NoC) that handles less common communication (e.g., remote/global memory accesses) and provides access to the external scale-up network.

The distinction between these fabrics is substantial. OpenAI describes the general NoC as deliberately more 'anemic than you would expect from a normal chip architecture' as it does not want performance-critical traffic to use it typically. By contrast, the ultra-fast collective network organizes data movement, so operands arrive in registers when needed rather than leaving compute engines and then waiting for memory, network traffic, or global synchronization, which creates internal performance bottlenecks.

In general, it looks like one network is optimized for speed and predictability for inter-core communications, whereas the other is optimized for general use cases. Trying to make one NoC do both would require a much more capable general network, consume more silicon/power, and reintroduce contention.

A different approach

OpenAI's Jalapeño is also designed for the very different types of work that happen during a single agentic inference request.

To explain how it works, let us compare OpenAI's and Nvidia's approaches. Nvidia's standard system-level decomposition is primarily two phases: prefill and decode. Prefill is generally compute-bound (which is why Nvidia tried to assign Rubin CPX with GDDR7 for this one), while decode is generally memory-bound (which is why Nvidia wants to keep GPUs with HBM for this). By contrast, OpenAI breaks the process into three phases: prefill (compute-bound), draft (latency-bound), and verification (bandwidth-bound).

In theory, OpenAI could have assigned each phase to a different type of specialized accelerator. However, the amount of prefill, drafting, and verification changes depending on the model, context length, token efficiency, and software algorithms. As a result, it is hard to predict the number of processors that must be deployed, so it's inevitable that some phases would sit idle. To that end, it makes more sense to develop one balanced ASIC that can do everything.

As a bonus, a universal inference accelerator does not need to move increasingly large KV cache to its peers, unlike highly specialized ASICs, which ultimately means less power consumed.

Chips develop chips

Despite Jalapeño's exceptionally short development cycle, OpenAI says that most of the processor was designed from scratch rather than assembled from existing Broadcom accelerator IP. OpenAI's Richard Ho said the compute die reuses some interface IP, but most of its Register Transfer Level (RTL) was newly written using XLS and Verilog. Meanwhile, development moved remarkably quickly: initial RTL work began in February 2025, the design taped out in November, first silicon arrived in May 2026, and OpenAI had Codex running on Jalapeño that same month.

One reason OpenAI was able to move so fast with its development was its extensive use of AI to assist and optimize the design. More than half of the core was written using the XLS hardware language and compiler infrastructure, while OpenAI's own AI models searched for ways to improve power, performance, and area (PPA). Compared with human 'baseline' designs, OpenAI reports improvements of 56% for a BF16 multiplier, 21% for an FP4 dot-product block, and 10% for an FP32 accumulator, along with 10% and 8% area reductions for the matrix and SIMD units. The company says that AI-assisted optimization even helped squeeze circuitry into a floorplan block that otherwise would not have fit, though OpenAI remains tight-lipped about what circuitry it was.

Jalapeño is OpenAI's first, but not the last, attempt to develop custom inference hardware. The company says its 2^nd Generation already well into development and heading toward tape out, while Richard Ho said during the presentation that Gen 3 is already 'operational,' even though his slide said 'planned.'

Programmability

Because Jalapeño features its unique spatial and sliced architecture, it is programmed differently from Nvidia's CUDA GPUs. OpenAI says Jalapeño can be programmed using a low-level programming environment in the open-source Triton ecosystem. Unlike the latest versions of CUDA, which ensure that its code runs on all Nvidia GPUs and aligns with the tensor-heavy execution model of Blackwell processors and their successors, Jalapeño gives software more explicit control over where data and computation are placed.

Each core has fast access to its local portion of HBM; these cores are interconnected using an ultra-fast network, so the software must be able to determine where tensors are physically located and how they are distributed across the chip. This makes programming the spatial architecture more complicated, so OpenAI also uses AI to find efficient data placement, scheduling, and communication patterns and to optimize kernels for the hardware.

Meanwhile, optimal mapping is architecture-dependent, so once OpenAI changes the number of cores, local-memory organization, collective-network topology/bandwidth, or compute resources in next generations of its accelerators, the old placement and scheduling may no longer be optimal and will require AI tools to perform hardware-specific optimizations again. By contrast, software written for Blackwell will work on Rubin and then Feynman without modifications.

But how good is OpenAI's software stack compared to CUDA? Apparently, good enough, based on performance results published by the company.

Performance

Instead of comparing peak performance numbers, OpenAI used SemiAnalysis' InferenceX benchmark to compare Jalapeño and Nvidia's GB200/GB300 across their complete latency-versus-throughput curves. The company measured how many tokens each system could deliver at comparable user-perceived latency and normalized the results by package power — 700W for Jalapeño, 1,200W for GB200, and 1,400W for GB300 — meaning that while Nvidia's hardware can lead in terms of absolute performance, OpenAI's accelerator leads in efficiency.

Compared to Nvidia GB200/GB300, Jalapeño delivers:

• Roughly 1.5X – 1.9X higher peak throughput per watt
• 1.7X – 3.6X lower end-to-end latency
• 2.1X – 4.1X lower minimum time between tokens (TBT)
• At Nvidia's minimum-TBT operating points, Jalapeño can deliver up to 104.3X higher throughput.
  
  This methodology particularly favors Jalapeño's strength at low latency, so the result is significantly inflated. The 104.3X figure means that at GB300's lowest-latency operating point, Jalapeño can maintain 104.3X higher throughput, not that Jalapeño is 104.3X faster overall.
• OpenAI also noted that its internal models show an even larger advantage for the Jalapeño platform and says applying multi-token prediction to Jalapeño can improve latency by another 3X to 5X at equivalent efficiency.

AI makes chips now

OpenAI used Hot Chips 2026 to fully detail Jalapeño, its first custom inference AI accelerator co-developed with Broadcom. The ASIC relies on a NUMA-style architecture built around 64 memory/core slices, carries 216 GB of HBM4 memory, and has compute performance of up 13.4 MXFP4 PFLOPS at 700W. Being aimed at AI data centers, Jalapeño scales from 128 accelerators per rack to 2,048 inference processor per pod and can provided up to 27 EFLOPS of MXFP4 compute, 432 TB of HBM4, and 32 PB/s of aggregate memory bandwidth per cluster.

While absolute performance of Jalapeño may fall short of what Nvidia's Blackwell or Rubin offer, the company argues that the main advantage of its AI inference accelerator platform is its performance-per-watt achievements as well low latency. OpenAI says its Jalapeño delivers roughly 1.5X – 1.9X higher peak peak-performance-per watt and 1.7X – 3.6X lower end-to-end latency than Nvidia GB200/GB300 in its InferenceX comparisons.

Perhaps the most impressive, or maybe even terrifying, though certainly not unexpected thing that OpenAI revealed is that AI played a major role in Jalapeño's unusually fast nine-month RTL-to-tapeout development cycle and also enabled the company to improve performance, power, and area, of the design. Jalapeño's successor is already heading toward tapeout and OpenAI's 3^rd Generation AI accelerator is already in development.

What gives a product flagship status? Some might say price, and that is sometimes true. But in the computer monitor world, a flagship is something that stands apart from the rank and file.

I’ve reviewed many 34-inch ultra-wide panels. These 21:9 aspect ratio curved displays can cover the user’s peripheral vision almost as well as a pair of VR goggles. When using them, I have often wished for a little more vertical space. The easiest way to achieve this is with a 39-inch size.

Alienware’s latest flagship delivers that very thing to the best gaming monitors category. The AW3926QW is a 39-inch, 21:9 1500R curved OLED panel with Tandem RGB Stripe technology, 5120x2160 (WUHD) resolution at 165 Hz, eSports modes that run at 330 Hz, Adaptive-Sync, HDR10, Dolby Vision, VESA DisplayHDR 500 and wide gamut color. Let’s take a look.

Alienware AW3926QW Specs

This isn’t my first foray into Tandem RGB Stripe tech. I saw it in Asus’ recent PG32UCWM as well. It brings two important advantages: higher brightness with lower power consumption and wide gamut color without the addition of a Quantum Dot layer. The AW3926QW easily tops 500 nits in HDR mode for a 25% window pattern and can fill the screen at over 300 nits. Alienware claims 1,300 nits for a 3% window, and I have no reason to doubt that. It doesn’t quite have the color volume of a QD display, but my sample covered more than 100% of DCI-P3. It also came out of the box with reference-level color accuracy and a pro-worthy sRGB mode.

The AW3926QW also has higher pixel density than typical 34-inch ultra-wides. Most of them are 3440x1440, which means 109ppi, equivalent to a 27-inch 16:9 QHD panel. A 39-inch panel at 5120x2160 pixels delivers a 4K level density of 144ppi. That’s a clearly visible difference. The AW3926QW is super sharp with no fringing or other clarity-reducing artifacts.

Gaming performance is high thanks to Adaptive-Sync, FreeSync, and G-Sync support, and a 165 Hz refresh rate. For eSports applications, there are multiple resolution and refresh modes that go up to 330 Hz in full screen, 25, 27, and 32-inch formats. These can be switched with a single click thanks to a programmable joystick that controls a comprehensive OSD. Alienware also provides its AlienVision feature with its designable aiming points and sniper aids. And you get a frame counter, timer, and screen alignment marks.

On the outside, the AW3926QW sports AlienFX lighting with colored LEDs for the power button and the Alien Head on the back. These can be controlled in the OSD or with the Alienware Command Center app. Also included are the latest video inputs, DisplayPort 2.1 and HDMI 2.1, along with USB-C for a total of four ports. And there are USB upstream and downstream connections with KVM control.

With everything included except for internal speakers, you’d expect the AW3926QW to cost a lot, but it’s opening at $1,100, which makes it competitive with many other premium OLED ultra-wide displays. On paper, Alienware is delivering everything a flagship should have without an extreme price. Let’s see how it performs.

Assembly and Accessories

Alienware’s packaging is completely recyclable, even down to the paper tape that seals the clamshell carton. The panel, upright, and base assemble without tools. Included cables are IEC power, two USBs, DisplayPort, and HDMI. The power supply is internal.

Product 360

The AW3926QW maintains Alienware’s latest styling aesthetic with a thin bezel and the cool floating upright that appears to be detached from the small-footprint base. Everything is solid, with metal cores and heavy plastic coverings finished in a matte texture. On the back is a lozenge-shaped vent ring that provides effective fanless cooling. Inside it is the Alien Head and a polished logo. The stand unsnaps to reveal a 100mm VESA mount with fasteners included if you want to use an arm.

The stand provides a 4.3-inch height range plus 5/21 degrees tilt and 20 degrees swivel. The base remains stationary while the stand pivots upon it. There is no portrait mode, but you can tweak the panel with five degrees of yaw if your desk isn’t level. Movements are firm and sure and befit a flagship product.

The screen curve is 1500R, which in the scheme of things is fairly subtle. There’s just enough wraparound effect to bring the sides of the screen into your vision cone when sitting around three feet away. You can sit closer if you like since pixel density is tight, but then you’d have to turn your head to see the whole image. The screen coating is shiny, but a polarizing layer keeps reflections from affecting the picture.

There are inputs aplenty for video, four in all. You get two HDMI 2.1, a DisplayPort 2.1, and USB-C, which functions as DP Alt and delivers 90 watts of power. There’s a 3.5mm headphone jack plus more USBs: two on the bottom of the panel edge (C and A) and an A and B on the I/O panel. They can be bound to video inputs using the KVM wizard in the OSD.

OSD Features

The AW3926QW’s OSD appears when you click the joystick. First up is a quick menu with signal info at the top, then an up-click opens the full menu, which is quite extensive, which is why there are 12 photos below.

The little quick menu at the bottom of the first screen is programmable by the user. You can put almost any monitor function into the five slots provided. You can also program the four joystick directionals for a total of nine options. This makes it easy to change picture modes, refresh modes, call up aiming points, or whatever. That’s all controlled in the Personalize menu, photo 11 above.

The first menu is Game, and it has everything a gamer could want. There are 12 picture modes in total. Three of them have color temp and dark stabilizer settings, called Game 1-3. Custom Color also has color temp gain and offset sliders along with color management. Creator is where you’ll find gamma presets and colorspace options like sRGB. Standard is the default mode, and it’s visually perfect out of the box.

Game Enhance Mode has a timer, frame counter, and alignment marks. For the aiming points, go to AlienVision where you’ll see a cool reticle editor that includes settings for sniper aids. You can create up to three configurations for later recall.

The eSports modes are where you can change the refresh rate and image size. I’m not sure why they aren’t all in one menu, but between the two sections, you’ll find 25, 27, 32, and 39-inch settings. All but the 32 run at 330 Hz. The 32-inch gives you 4K at 165 Hz and puts black bars on the sides of the image. That one is handy because it doesn’t change the pixel density like the others. The 25- and 27-inch options can be moved up or down from the center if you prefer.

AlienFX Lighting refers to the power LED and Alien Head’s color and effect options. You can customize them to a variety of colors and movements.

For HDR signals, there’s a Dolby Vision option along with eight modes for HDR10 content. Custom Color HDR includes grayscale calibration. Of the listed modes, the brightest one is True Black, and the one with the best luminance tracking is Peak 1300 Bright.

The USB menu is where you’ll find the KVM options. There, you can bind the HDMI and DisplayPort inputs to specific USB ports which means you can use one keyboard, monitor and mouse for multiple systems like a PC and a console.

Personalize is where you can program the quick menu and joystick directionals, as well as toggle the power delivery of the USB ports.

For panel maintenance, the AW3926QW has pixel refresh, which runs automatically when the panel health indicator goes red. That’s determined automatically by internal software. Alienware backs the AW3926QW with a three-year warranty that includes burn-in.

Alienware AW3926QW Calibration Settings

The AW3926QW doesn’t need calibration in its Standard picture mode. I noted slightly light gamma, but the color gamut and temperature are visually perfect out of the box. To adjust the grayscale, I used Custom Color mode, which has both gain and offset sliders. If you want to change the color space, Creator mode has that option along with gamma presets. The three Game modes also have grayscale adjustments. My recommended SDR settings are below. For HDR, there are eight modes available plus Dolby Vision. I suggest trying Peak 1300 Bright and True Black to see which you prefer. They look similar, but the latter is a tad brighter while the former has more accurate luminance tracking.

Gaming and Hands-on

A few days with the AW3926QW proved its flagship status beyond a doubt. It excelled as a gaming monitor and workday display in equal measure.

For gameplay, my test PC with its GeForce RTX 4090 was more than enough to drive it at 165 Hz at full 5120x2160 resolution. That’s 11 megapixels in case you’re counting, about 23% more than Ultra HD. The video processing here is good enough that I didn’t wish for 240 Hz. And I had little need for the 330 Hz modes. I can see their use in competition or with lesser video cards to keep the action smooth. But I saw nothing but perfect motion resolution and hyper precision. Aiming and moving became an extension of my brain as control inputs were instantly translated.

Image quality was also superb, with lush bright color and deep contrast. Playing in SDR mode with the full color gamut was excellent, but HDR games were even better. The AW3926QW is a superb HDR display. And don’t forget to try Dolby Vision with its dynamic tone mapping. I didn’t have any DV games, but I watched content from a Blu-ray player that looked stunning. Going back and forth between HDR True Black and Peak Bright 1300, I found I preferred True Black because it had a tad more impact. On page five, I’ll explain the technical differences.

I’ve reviewed many 34-inch ultra-wide displays, and while I find them excellent for gaming, they lack the height I prefer for writing content. The 39-inch AW3926QW is perfect for getting work done. It is only half-an-inch shorter than the 32-inch 16:9 screen I use every day. So, it’s ideal for lining up two or three windows side by side when comparing copy or graphics. I loved it for Photoshop, where I could open and see multiple photos and still have room for toolbars. If you’re already a user of dual screens, the AW3926QW is the perfect replacement if you’re sick of that pesky dividing line.

The monitor proved to be flagship-worthy in every respect. The stand is solid; styling is premium and everything about the AW3926QW exudes quality. The inclusion of USB ports and KVM further extends its usability. And the selection of esports modes makes it flexible for a wide variety of gaming systems.

Takeaway: The AW3926QW is super useful in every way and excels as a gaming display. It delivers speedy and responsive performance at 165 Hz and impressive frame rates up to 330 Hz in the esports modes. The picture is gorgeous with vivid and accurate color and plenty of brightness. HDR has high impact and multiple picture modes to choose from, plus Dolby Vision support. The AW3926QW is a great all-around monitor that delivers a lot of bang for the buck.

MORE: Best Gaming Monitors

MORE: How We Test PC Monitors

MORE: How to Buy a PC Monitor

The AW3926QW is somewhat unique in my database and is the only 39-inch WUHD panel I’ve reviewed. The next closest comparison is LG’s 45GX950A, which is a 45-inch panel with the same 5120x2160 resolution at 165 Hz. The other screens are Alienware’s AW3426DW, Deco Gear’s DG49OLED240, Acer’s X34 OLED, and Asus’ PG34WCDN.

Pixel Response and Input Lag

Click here to read up on our pixel response and input lag testing procedures.

Faster panel response at higher refresh rates is not a given but in the AW3926QW’s case, 330 Hz halves the time it takes to draw a one-inch white square from black. It’s impossible to see this difference with the naked eye but the numbers are there for your interpretation. I noted that the Asus at 360 Hz did the draw in 0.24ms, the same as the AW3926QW at 165 Hz.

In the lag test, the AW3926QW is mid-pack at 165 Hz with a 2.3ms speed increase at 330 Hz. Notice that the LG drops 7ms off its lag score but is still slower than the other screens. The Asus PG34WCDN is the king of speed here but the AW3926QW at 165 Hz is on par with other 240 Hz displays.

Test Takeaway: The AW3926QW isn’t the absolute fastest ultra-wide OLED out there but it is just a couple of milliseconds off the pace, which in practice is nothing. Only Obi-Wan Kenobi might notice a difference between 16.6ms and 14.1ms of input lag. It’s plenty quick and with its extra esports modes, it’s versatile too. For most gamers, driving it with a speedy video card at full resolution and 165 Hz will bring the most fun.

Viewing Angles

I’ve noted a slight color shift with the Tandem RGB Stripe OLED panels I’ve photographed like the AW3926QW. This comes from the stacked emissive layers and added polarization. It rejects ambient light effectively but introduces some slight artifacts. You can also see a slight drop in brightness, but gamma remains constant in the horizontal plane. The vertical shot shows reduced brightness, gamma and a green color shift.

Screen Uniformity

To learn how we measure screen uniformity, click here.

The AW3926QW has exceptional screen uniformity, even among OLEDs with a 3.71% deviation from the center zone. There is no visible change in brightness in any field pattern when viewed head-on. With all the screens here being under 10%, you won’t see a difference between them with the naked eye.

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To read about our monitor tests in-depth, please check out Display Testing Explained: How We Test PC Monitors. We cover brightness and contrast testing on page two.

Uncalibrated – Maximum Backlight Level

Be not concerned about the AW3926QW’s 303 nit score in the SDR test. It isn’t as bright as the others because it only operates at constant brightness in SDR mode. This is a trait of all the Alienware OLEDs I’ve reviewed. It means there’s no difference in brightness between a full field and a 25% window pattern. The top-finishing Asus manages 501 nits with a window but just 306 nits with a full field, so at constant brightness, the two monitors are equal in luminance. Black levels and contrast cannot be measured.

After Calibration to 200 nits

Calibrating the AW3926QW makes no difference in either black levels or contrast. I set 200 nits based on a full-field pattern since there’s no variable brightness available for SDR. ANSI contrast was also theoretically infinite. This is typical OLED performance.

Test Takeaway: The AW3926QW has similar peak SDR brightness to other ultra-wide OLEDs when constant brightness is engaged. It does not have a variable option for SDR content. Black levels and contrast could not be measured under any test conditions.

MORE: Best Gaming Monitors

MORE: How We Test PC Monitors

MORE: How to Buy a PC Monitor

The AW3926QW comes set to its Standard picture mode, which employs the full native gamut for SDR content. It’s nearly perfect out of the box and does not need to be calibrated, but there are multiple options for adjustment, including a very accurate sRGB mode.

Grayscale and Gamma Tracking

Our grayscale and gamma tests use Calman calibration software from Portrait Displays. We describe our grayscale and gamma tests in detail here.

With all errors below 2dE, and most under 1dE, the AW3926QW does not need to be calibrated. My only complaint is slightly light gamma that tracks a bit below the 2.2 reference. You can adjust gamma, but only in preset increments in the Creator mode. Custom Color has no gamma options.

I managed a slight gain in grayscale accuracy, but it was so small that it could not be seen with the naked eye. Gamma gets a tad better in the 60-100% brightness steps, but that too is a tiny difference. Ultimately, calibration is only able to satisfy my ego, rather than making any visible improvement.

sRGB color, accessed from the Creator mode, has a couple of visible grayscale errors at 90 and 100%. It also has gamma anomalies at 10 and 90% where it is too light. These are minor complaints. You’ll see in the next section that sRGB color is visually perfect. This mode is suitable for color-critical applications.

Comparisons

The AW3926QW has the best out-of-box grayscale score with a tiny error of 0.70dE. That’s well into the professional realm. You could use this screen for video post-production or content creation and save yourself the cost of a professional monitor. After calibration, the top five screens are equal, with the LG just a tad behind the rest. For gaming, this is a non-issue.

In the gamma test, the AW3926QW stays tight from dark to light with a small 0.13 range of values. It’s a little light overall at 2.73% off the 2.2 reference with an actual value of 2.14. This is solid performance, but it would be nice to have gamma options in the Custom Color mode.

Color Gamut Accuracy

Our color gamut and volume testing use Portrait Displays’ Calman software. For details on our color gamut testing and volume calculations, click here.

There is zero reason for complaint about any of the AW3926QW’s color test results. Though it doesn’t have quite as much color volume as a Quantum Dot display, it covers a perfect amount of DCI-P3, hitting every target squarely in the process. You can see slight oversaturation in magenta and blue, but only on the chart. You won’t see anything on the screen but perfect and vivid color. Calibration takes the error from visually perfect to a tad more visually perfect. In other words, there’s no visible difference. This is excellent performance.

The sRGB test is even closer to numerical perfection with a 1.24dE average. The AW3926QW is completely qualified for critical work where the smaller gamut is needed.

Comparisons

You’d think 1.24dE would be a winning color score, but clearly, these OLED panels are seriously accurate. The spread from best to worst is only 0.67dE, and the worst number is 1.73dE. That’s impressive, to say the least. The AW3926QW is on par with the best displays I’ve ever tested and is clearly a flagship.

In the volume test, the AW3926QW is mid-pack, though with 101.20% coverage of DCI-P3, it’s ideal. The Deco Gear 49-inch claims the win at 112.28% thanks to its Quantum Dot layer. No one will think the AW3926QW isn’t colorful. It presents brilliant primary colors in all parts of the image, from dark to light. sRGB coverage is 96.09%, which reflects a tiny shortfall in blue. This is excellent performance.

Test Takeaway: The AW3926QW has color accuracy that rivals any professional monitor, and that’s straight out of the box. You can get a small improvement in numbers with a calibration, but it is completely unnecessary. Gamut coverage is ideal for both DCI-P3 and sRGB. The AW3926QW is fully qualified for critical work and can easily manage video post-production or content creation for photography and graphics.

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Our HDR benchmarking uses Portrait Displays’ Calman software. To learn about our HDR testing, see our breakdown of how we test PC monitors.

The AW3926QW supports both HDR10 and Dolby Vision formats. For HDR10 signals, you get an automatic switch and eight image modes. I tested True Black and Peak 1300 Bright to see which looked best.

HDR Brightness and Contrast

You’d think a mode called “Bright” would be the brightest, but the AW3926QW’s most luminous HDR mode is True Black. I measured 517 nits from a 25% window pattern. Peak 1300 Bright came up to 502 nits. Yes, that’s a tiny difference. And the color tests might sway your decision as to which mode to run. In practice, they both look good with deep blacks, bright highlights, and clear detail rendering. HDR definitely looks better than SDR on this monitor.

Grayscale, EOTF and Color

The first two charts above show the difference between Peak 1300 Bright and True Black modes. Grayscale tracking is excellent and identical for both modes, but the EOTF is different. This is the luminance tracking, like gamma for SDR. Peak 1300 Bright tracks close to the reference (the yellow line) with only slight variations and a 60% tone map transition. True Black runs too light starting at 20% but transitions closer to 70% thanks to its higher peak value. These differences are minor, but they are visible. Looking at the charts, I’d choose Peak 1300 Bright, but in most of the content I watched, I liked True Black better. These are the two best HDR modes in the AW3926QW’s toolkit. I suggest trying them both to see which you prefer.

Either way, HDR color is exemplary. And the differences are minuscule, so I’m just showing the True Black result. There is general oversaturation, which adds impact to the image. DCI-P3 is fully covered, while BT.2020 runs out of color at 90% red, 75% green, and 95% blue. This is close to what you’d see from a Quantum Dot display.

Test Takeaway: The AW3926QW has excellent HDR image quality and is one of the very few displays that supports Dolby Vision. With eight HDR10 picture modes, you can get a variety of looks, though the best accuracy comes from True Black and Peak 1300 Bright. Which is better is a matter of personal preference. Peak 1300 Bright measured a tad better, but True Black had a little more verve. Either one delivers excellent performance.

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Often, flagship means a halo product; one that has a huge feature list, bleeding-edge performance and premium build quality, with a high price to match. But in the case of Alienware’s 39-inch AW3926QW, you get all that for no more than the cost of a high-end 34-inch display.

The Tandem RGB Stripe panel means you get more than 100% coverage of DCI-P3 without the added cost of a Quantum Dot layer. It also means high brightness, more than 500 nits in HDR mode, with no extra power consumption. Resolution is 5120x2160, which means pixel density equivalent to a 32-inch 16:9 aspect ratio 4K panel. The 1500R curve is just enough to bring the sides in without image distortion and provides the immersive quality curved screens are famous for.

The image is colorful and incredibly accurate. Reference-level color comes right out of the box, no calibration needed. DCI-P3 coverage is over 101%, which is a little less than Quantum Dot monitors but more than the category average of 95%. The only flaw I noted was slightly light gamma in the Standard and Custom Color modes. I wished for gamma presets, but they were only available in the Creator modes.

As a gaming monitor, you’ll have a hard time finding a better OLED. The AW3926QW’s unusual size is perfect for first-person titles, and you can employ a slick aiming point editor, AlienVision, to help you stay on target. The extra eSports modes with speeds up to 330 Hz mean better performance with a wide variety of gaming systems. Panel response is smooth, and input lag is low enough that competitive gamers will want to add one of these to their arsenal.

Alienware has positioned the AW3926QW as a flagship display, and it earns that title while offering decent value. If you have $1,100 to spend on a large-format OLED monitor, definitely check it out.

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Game studio MiTale Ltd. is at Gamescom promoting a narrative-driven post-apocalyptic RPG dubbed C.L.A.Y. - The Last Redemption. You may well have heard the same or very similar descriptions of a new game before, but C.L.A.Y. stands out for its use of quantum computing techniques in the creation of game assets as well as for game narrative and procedural systems. MiTale worked with experts from IBM Quantum Computing and claims this is the “first commercial game on the market utilizing quantum computing in an immersive narrative experience.” Moreover, it reckons this is “the first step toward using real quantum hardware in games to accelerate complex tasks in a manner similar to a GPU,” which is rather bold.

The official game page on Steam (linked above) doesn’t mention quantum computing, but perhaps it hasn’t been updated with details from this Gamescom announcement, or kept up-to-date with the game development process. A little bit of investigation reveals that C.L.A.Y.’s development seems to have spanned at least six years so far. We’ve been emailed a fuller statement about MiTale’s exploration of quantum games, and there is some more public information about MiTale’s IBM Quantum collaborations on the game’s Gamescom 2026 press kit.

MiTale has been exploring quantum games since 2019. Its work in the games development field has included connecting quantum systems with familiar development environments, including Unity, Unreal Engine, Godot, and the Ink narrative system. However, it has a longer-term goal of integrating real quantum hardware into game development. It hopes to enhance the player-centered design this way, as well as to provide truly unique real-time adjusting experiences in every gaming session.

Back to C.L.A.Y., this game has leveraged IBM Quantum tech to help MiTale developers generate maps, characters, and graphic simulations. It also hints that using real quantum hardware in PCs could become the new normal, one day. Discrete or integrated quantum accelerators could “accelerate complex tasks in a manner similar to a GPU.” Certainly it wouldn’t be more of a waste of silicon than the NPUs in my current AMD Ryzen AI and Intel Core Ultra PC systems.

Often quantum computers are pitched as a hifalutin solution “to solve problems that affect all of humanity.” But their touted strengths in complex modeling, optimization, simulation, discovery, and ability to handle vast numbers of variables could also be applied to huge next-gen gaming universes.

Gaming on quantum computers? Quandoom has already been there.

When it comes to building a new PC, memory has seen the sharpest rise in pricing in years. Now, you have to seriously consider the cost of the RAM kit in your build, and especially for those stepping up from a DDR4 platform, whether the cost is worth it for the extra performance. Currently, the prices continue to rise with no end in sight. It's hard to suggest holding off on upgrading now, as we have no clue when the prices will start to fall - if at all. However, if you are looking for a brand new pair of DDR5-6000 RAM sticks for a desktop PC, then Corsair's Vengeance 32GB DDR5-6000 kit has just received a $215 discount that brings the price down to $401.99, from its $616.99 list price. This makes it the cheapest 32GB DDR5-6000 kit that you can purchase today.

● Check out this deal at Corsair

The kit includes two 16GB DDR5 modules that run at 6000 MT/s with a CAS latency of CL36 and further timings of 38-38-76. Corsair's Vengeance kits are a popular choice among PC building enthusiasts and feature black aluminum patterned heat spreaders with RGB lighting running across the spine of the sticks. They come with Intel XMP BIOS profiles for preconfigured overclocking.

Prices of memory, especially DDR5 kits, are very off-putting at the moment, and looking at upgrading can feel the same. With current-gen game consoles going up in price, expectations for next-gen consoles are that the prices will stretch over a thousand dollars. So if you're thinking of getting a new gaming PC or upgrading from an older DDR4-based system, it's probably best not to wait around. Just be prepared for some hurt to the bank account. Industry figures say the memory shortage could last for years or even a decade, with no relief at all in sight; prices are much likelier to go up rather than down.

Gaming PC building is getting more expensive by the minute, but price hikes on RAM, SSDs, and GPUs are filtering down more slowly to pre-built gaming desktops, making them better value than DIY in this economy. Best Buy has slashed $300 off this CyberPowerPC, which gets you a very potent 9800X3D CPU, RTX 5070 GPU, 32GB of DDR5, and a 2TB SSD for $1,999.

● Check out this deal at Best Buy

This gets you the best gaming CPU on the market, a mid-range GPU perfect for 1440p gaming, and ample RAM and storage in an economy where all of those commodities are increasingly hard to find. The 32GB of DDR5 RAM will cost you $400 alone, while a Gen 4 2TB SSD is around $250 minimum. The GPU in this build is also now $800, which means that on those three components alone, you'd be spending nearly $1,500. That's before you factor in the $464 CPU, accounting for nearly the entire price of this build before you've considered a motherboard, PSU, case, cooling, and the build cost.

The AMD Ryzen 7 9800X3D is the best gaming CPU on the market thanks to its 8-core, 16-thread power with boost clocks of up to 5.2 GHz. The standout feature is its 3D cache technology, which drastically improves fram rates, as you can see from our gaming benchmark data below.

The RTX 5070 is an excellent pairing; with 12GB of VRAM, it's an ample card for 1440p gaming, or 4K at a push if you use Nvidia DLSS. As you can see, the 5070 averages 74 frames per second in our 19-game testing suite at 1440p.

The rest of this PC's specs are also pretty positive. 32GB of DDR5 RAM and 2TB of SSD Gen 4 storage are considered generous in today's market, so you've got enough RAM for multitasking and gaming as well as plenty of storage. Liquid cooling is more than enough for this processor, and the B850 motherboard will provide plenty of ports for connectivity as well as headroom for expansion.

Ultimately, the selling point of this PC is the price; given the current market cost of its components, you're saving a small fortune versus building a PC like this yourself. As mentioned, the $800 GPU, $464 CPU, $400 RAM, and $250 SSD make it a worthwhile purchase.

Nvidia ostensibly brought back the RTX 3060 12GB in late June to help gamers cope with the ongoing component crisis. Since most of the world's memory is being routed to expensive AI chips, the RTX 3060 12GB offered gamers a 12GB VRAM pool for what was supposed to be a reasonable price... if only the price remained steady. As of today, this five-year-old GPU now costs almost $500 across the world, which is just as much as the RTX 5060.

MSI's Ventus 2X OC variant of the RTX 3060 12GB launched at just $300 on its online store and $340 on Newegg. It's now listed for $480 in both places. That's a 60% increase in less than two months, compared to its lowest price. Similarly, Asus' Dual V2 OC variant was released with a $330 MSRP, which quickly rose to $360 on Newegg, and the same card is now sitting at $500, constituting a 52% price hike from launch.

The cheapest RTX 3060 12GB right now, the Gigabyte Windforce OC Rev 2.0 model, currently costs $460, up 35% from the $340 it arrived at in late June. The same GPU is listed for 417 GBP on Amazon UK, which converts to $566. In Europe, the cheapest variant is once again Asus' Dual V2 OC, costing 386 EUR on Amazon Germany, or roughly $450; price aggregator Geizhals says it was just 330 EUR a month ago.

In comparison, the RTX 5060, which is two generations newer, can be easily had for around $450 from a bunch of different vendors. That GPU is not only significantly faster but also more efficient, while carrying the latest features such as DLSS multi-frame gen support. In our GPU hierarchy, the 5060 is actually just as powerful as the RTX 3070 at 1080p and 1440p, and it carries the same amount of VRAM, 8GB.

That VRAM capacity is the only thing going for the 3060 12GB at this point, and even with extra memory to utilize, the GPU isn't really capable enough to deliver better performance in games. Plus, you can find better deals on the used market, or even renewed offers from big retailers, so the re-launched RTX 3060 12GB just doesn't make sense from any angle right now, despite its still-decent 1080p chops.

The 3060 is cheaper to produce for Nvidia, thanks to its slower GDDR6 memory compared to the GDDR7 you get on the 5060. It's fabricated on the older, less in-demand 8nm node from Samsung, compared to TSMC's N5, which should also help with yield. But none of those manufacturing leniencies seem to be trickling down to the customer. Even the RTX 5050, which can be had for $300, is faster than the revived RTX 3060 12GB.

A Sony PlayStation 3 console modder has shared images and details of their ‘PS3 Pro v3’ design. On the PS3 subreddit, Fun-Equivalent-7785 showcases this project, which includes a heavily customized 3D-printed top case, dual Noctua fans, and liberal use of tin foil. While you may love or hate the new aesthetic, the results seem impressive, with the PS3 overclocked like a champ and running very cool and quiet.

Yes, this is at least the third PS3 Pro mod from Fun-Equivalent-7785, but all projects benefit from refinement. PC builders will be familiar with the dread of unwrapping their motherboards and seeing the revision number is v1.0.0, contrasting with the pleasure of seeing v1.0.3. Moving swiftly back on topic, you can look and read about the first version of this PS3 Pro project here, and there’s also a video about the PS3 Pro v2.

In some comments following the original post, Fun-Equivalent-7785 provides more technical details about the PS Pro v3 mod. They claim that the processors aren’t delidded, nor undervolted. Instead, they have installed “server heatsinks” on Sony’s custom PS3 processors. In front of these, one can clearly see two chunky Noctua fans, presumably pushing much air through the heatsink fins. Tinfoil has been used to shape the ducting. The Noctua fans are somewhat wider than the heatsinks, and overall it is “super silent.”

One screenshot shows an overlay from the ‘PS3 Pro v3’ running. Specifically, we can see the CPU is 40 degrees Celsius, the Reality Synthesizer (AKA the RSX, a GPU) is 42 degrees Celsius, while the fans are only rotating at 24% max RPM. This is while the RSX is overclocked to 750 MHz core and 1,000 MHz memory. A stock PS3 would achieve around 500 MHz core and 650 MHz memory.

Those uprated numbers result in broadly improved performance in PS3 gaming. “Almost every game has a boost in FPS,” crows Fun-Equivalent-7785. The results seem pretty worthwhile, especially if you like the look of this design from Fun-Equivalent-7785.

While it is an interesting project that might inspire you to do something similar with your old PS3, there are some aspects to modding like this that you have to consider. This particular project isn’t a ‘product’ so you can’t just buy a kit to copy it. There aren't yet 3D printer resource files shared on GitHub or similar. We are also not aware of any other shared resources to make it easy for other modders to replicate the PS3 Pro v3 project.

Lastly, please remember, before you risk your precious PS3 under the knife, there can be risks to modding. For example, there are many opportunities to inflict accidental physical damage to electronics when undertaking DIY projects like this. Moreover, overclocking may shorten the lifespan of your console's components, and applying unofficial firmware has the potential to soft- or hard-brick your system.

Data centers are unpopular among U.S. citizens right now, with projects facing resistance and moratoriums across the nation. While we’ve seen over a hundred protests against the developments happen simultaneously, researcher Andy Masley wanted to answer one question: what is driving the pushback against these projects? Is it big tech? AI skepticism? Or maybe even China? But after reviewing data from multiple polls, his answer seems quite simple: the “object-level local environmental and economic harms” that have been widely reported in the media.

Multiple data center sites owned and operated by different hyperscalers have found themselves in hot water with the people surrounding them. The biggest issue that caught the attention of the public is the massive amounts of electricity that these developments use, requiring grid operators like PJM Interconnection to expand their infrastructure to service AI data center needs, which costs billions of dollars. Unfortunately, the capital expenditure for these upgrades has typically been passed on equally to all users, with ratepayers seeing price hikes of up to 76% on their utility bills.

It has gotten to the point that President Donald Trump had to make tech giants, AI hyperscalers, utility operators, and state governors sign a “ratepayer protection pledge” so that AI companies “pay their own way” when it comes to their electricity needs. But this move was too little, too late to halt increased electricity costs to the huge swathe of the American population that PJM Interconnection serves. Oregon is the only state, so far, to have passed a law that requires sites using 20MW or more to pay for their own share, with PGE, the state’s largest power provider, hiking data center bills by 30% while also cutting residential costs by 1.3%.

There have also been reports of two data centers in Georgia and another in Wyoming causing water issues in the surrounding neighborhoods. This includes low water pressure due to excessive consumption, a drop in quality with muddy water pouring out of taps, and water discharges that contaminated a city’s reclamation water supply. Aside from those, there are also problems with noise pollution, like the site that generates noise 24/7, as if “someone set up a vacuum, like in your living room,” and air pollution from mobile turbine generators and dedicated natural gas power plants.

All of these are “object-level” concerns, meaning these are specific issues that exist, have happened on other sites, and are directly observable by other people. However, Masley also points out that some of the coverage of these issues was either overblown or only showed the concerns of the residents, without putting them into the context of the actual project and comparing them with other existing data centers in other areas. Because of this, many people have become fearful that the issues found in other developments will automatically apply to the data center being proposed in their community.

Some data center developers also aren't helping to assuage fears. Some are pushing back against moratoriums and bans, and have begun suing local jurisdictions. One town in Pennsylvania listed 43 specific demands that the developer called “too difficult” and pulled out of negotiations before applying again, with the council calling the move an attempt at “approval by tantrum.”

Atari has officially unveiled the Atari x Activision Collection, a compilation featuring 39 classic Activision games on physical cartridges. An individual cartridge starts at $34.98, while the Ultimate Deluxe Set, which includes all 39 games, costs up to $999.96. Atari and Activision's latest collaboration brings the nostalgia and value of a physical cartridge to long-time retro fans and new gamers who want to relive the classics.

• Check out the Atari x Activision Collection on Atari

At a time when major manufacturers like Sony are giving up on physical media, it is refreshing to see Atari stepping up to keep the tradition of owning your games alive. Many gamers still appreciate physical media, which often goes beyond a simple disc or cartridge. Extras, such as unique artwork or packaging, matter to many collectors.

Although Atari is bringing back an impressive lineup of 39 Activision titles, only a select group, including Pitfall!, River Raid, Megamania, Enduro, Kaboom!, H.E.R.O., Keystone Kapers, Private Eye, Pressure Cooker, River Raid II, The Activision Decathlon, and Pitfall II: Lost Caverns, will arrive as Standard Editions. These Standard Editions are priced at $34.99 each and include a sleek black cartridge and a newly designed, full-color manual.

The Deluxe Edition, on the other hand, retails for $249.99 and comes in four different collections: Treasure, Speed, Secrets, and Victory. Each collection includes 10 games, except one, which has only nine. The Deluxe Edition, as the name implies, features deluxe editions of Activision titles on vibrant, colorful cartridges that will stand out in any collection. The Deluxe Edition also includes 10 woven patches and 10 enamel pins, plus a special collector slipcase to house the games.

Atari x Activision Collection

The Ultimate Deluxe Set, priced at $999.96, represents the pinnacle of the Atari x Activision Collection. The ultra-premium package includes all 39 classic Activision titles, combining the content of all four Deluxe Sets into one big collection. Building on everything offered in the Deluxe Edition, the Ultimate Deluxe Set adds 12 exclusive metal trading cards and a yet-to-be-announced collectible item.

All game cartridges included in the Atari x Activision Collection are fully compatible with the original Atari 2600 and Atari 7800 as well as the more modern Atari 2600+ and Atari 7800+ consoles.

If you want only one or two specific titles from the Atari x Activision Collection, buying individual cartridges is the most affordable option. However, if you are going to buy more than seven, the Deluxe Edition offers significant savings. You end up paying just $24.99 per game instead of the standard $34.99, a discount of up to 29% per title. The Ultimate Deluxe Set will not save you much compared with buying the Deluxe Sets individually. However, you do get the extra goodies and free shipping.

Pre-orders for the Standard Edition cartridges, the Treasure Collection, and the Ultimate Deluxe Set are officially up on Atari's website. These will ship in November 2026. The Speed Collection, Secrets Collection, and Victory Collection, on the other hand, will not be available until Spring 2027, Summer 2027, and Fall 2027, respectively.

It has become a tradition that every single quarter Nvidia reports record results that outpace all of its quarterly results before that. This Wednesday was no exception as the company posted revenue of $96.2 billion, which was up 106% year-over-year, due to rising demand for its AI hardware. But such results come at a cost, as the company has to invest massively in its future. In the second quarter of its fiscal 2027, Nvidia had to commit to procuring memory worth up to $160 billion, which includes its memory supply pact with SK hynix.

Nearly $100 billion revenue per quarter

For the second quarter of Nvidia's FY2027, which ended on July 26, 2026, the company's GAAP revenue hit a record $96.221 billion, up 18% quarter-over-quarter (QoQ) and 106% compared to the same quarter a year ago. Nvidia's net income totaled $59.688 billion, up 126% year-over-year (YoY), as its gross margin reached 75.0%. Sales of Nvidia's Compute & Networking hardware reached $88.299 billion, up 18% sequentially and 114% YoY, whereas sales of its graphics hardware hit $7.922 billion, up 12% sequentially and 46% year-over-year.

"AI has reached its inflection point," said Jensen Huang, founder and CEO of Nvidia. "AI is doing useful work. Its tokens are productive and profitable. Now, compute is revenue. And demand is accelerating. […] We have a golden age of new AI labs and startups, multiple frontier labs scaling in parallel, a thriving open-model ecosystem and physical AI coming online […]. The AI infrastructure buildout is at full steam. Vera Rubin, now in full production, was built to power exactly this moment."

Nvidia's results were driven by sales of its data center-grade AI hardware as various customers bought $89.023 billion worth of equipment, an increase of 18% sequentially and a rise of 117% compared to the same quarter a year ago. Hyperscalers purchased $48.710 billion worth of hardware from Nvidia (up 102% YoY and 13% QoQ), while revenue from AI Clouds, Industrial and Enterprise climbed to $40.313 billion (up 138% YoY and 25% QoQ), an indicator that while hyperscalers still purchase more equipment from Nvidia, the ACIE segment is growing faster. Sales of Nvidia's Edge Computing products were $7.198 billion (up 27% YoY and 13% QoQ), which means that sales of graphics products for PCs were strong despite shortages of GPUs and memory.

Commitments total $279 billion

Nvidia expects demand for its products to remain strong in the coming years. To meet that demand, the company increased its long-term purchase commitments from $119 billion in Q1 FY2027 to $279 billion in the second quarter. Typically, Nvidia's long-term supply commitments included pre-payments and commitments for wafer processing and advanced packaging at TSMC, as well as for HBM memory made by DRAM makers. This time around, Nvidia explicitly says that the bulk of the commitments are 'primarily related to the procurement of memory.'

Such huge commitments indicate that the company projects massive demand for its data center AI products in the coming years. During the conference call with financial analysts and investors, it indicated that its customer forecasts point to doubling demand next year, but Nvidia currently believes its supply chain can support about 70% growth.

"Even though our demand is much greater than 70%, our supply allows us to confidently deliver 70%," Huang said. "The unconstrained would be a lot, a lot higher. […] We have secured a lot of supply, but we just need a lot more."

To that end, the $279 billion supply commitment should be interpreted as not a precautionary inventory-building, but a strategic move to ensure shipment growth. Nvidia is effectively reserving memory and other capacity because it expects demand to exceed what the supply chain can deliver through at least the end of FY2028, as its management explicitly says supply will remain a bottleneck at least through FY2028.

$108 billion per quarter envisioned in Q3

For the third quarter of FY2027, Nvidia expects revenue of approximately $108 billion, ± 2%, with no data center compute revenue from China included in its outlook due to uncertainties with export and import licenses. The company projects a GAAP gross margin of around 74% and expects GAAP operating expenses of approximately $9.2 billion.

Nvidia's NVLink Fusion program gives the company's partners the building blocks necessary to connect custom chips with the NVLink scale-up domain used to join many separate processors into a single coherent system like the Vera Rubin NVL72 rack-scale accelerator. Today, Nvidia is adding a new building block to that toolkit: NVHBM, a custom implementation of the high-bandwidth memory that underpins practically every AI accelerator in use today.

As Nvidia describes it, NVHBM is a custom HBM base die that promises higher bandwidth, lower power usage, and a smaller on-die footprint than traditional HBM4e. Nvidia says it's designed and validated with "leading memory vendors," so it promises custom silicon developers faster time-to-market than implementing commodity HBM from the ground up. But it's worth re-emphasizing that this isn't an HBM replacement. Instead, it's a new building block that Nvidia is only offering to its custom silicon partners.

Memory bandwidth is everything for AI accelerators, and NVHBM promises up to 30% higher bandwidth per stack than standard HBM4e. For memory-bandwidth-bound AI workloads, that higher bandwidth translates into higher throughput, such as a higher tokens-per-second rate for AI inference.

The custom NVHBM base die also reduces the footprint of memory-related circuitry on the main custom accelerator die. Traditionally, the HBM memory controller has been incorporated into the primary silicon die on the package. NVHBM instead moves the memory controller into the base die of the HBM stack and provides a smaller custom PHY that NVLink Fusion customers can then integrate into their designs.

Nvidia says this approach frees up precious package real estate that can then be used for additional compute die area — up to 30% more compute on the primary silicon die. NVHBM further promises to simplify the interposer routing used to join multiple chips together for designs using advanced packaging techniques.

NVHBM also provides power savings versus off-the-shelf HBM4e stacks. As Nvidia has continuously hammered home in the Vera Rubin roll-out, every watt that isn't going into token production is a watt wasted. Nvidia says NVHBM uses 15% less power than commodity HBM4e, and that power savings can be banked for performance-per-watt improvements, reallocated into more functional units for a custom accelerator design, or translated into higher sustained performance within the same power budget.

Higher bandwidth at lower power is a huge win for AI accelerators that are moving massive data structures like model weights and KV caches around, especially when those savings are multiplied across many thousands of chips. The energy saved on data movement can be plowed back into higher performance from the accelerator itself or reallocated to support larger numbers of accelerators within the same fixed power envelope.

But these are, as of now, reasons for Nvidia's prospective partners to consider incorporating NVLink Fusion and NVHBM into their custom designs, not benefits that will materialize in the Rubin rack-scale systems already in production.

Along with NVHBM itself, Nvidia announced that Amazon's Annapurna Labs will be its first partner on NVHBM. , and Annapurna VP Nafea Bshara says: “We look forward to this technology collaboration to benefit future AWS infrastructure designs.” Annapurna's next-generation Trainium 4 AI chips will already support the NVLink Fusion scale-up interface, so it seems likely that follow-on chips will support NVHBM, too.

Groq's former chief architect stood on stage at Hot Chips 2026 and presented his former company's inference chip as Nvidia silicon. Igor Arsovski, now Nvidia's VP of hardware, presented the Groq 3 LPX rack's architecture and published the first third-party benchmark of the hardware: Artificial Analysis measured it at 3,431 output tokens per second on a 100K-context Gemma 4 31B reasoning workload, roughly four times the 870 tokens per second of the next-fastest public endpoint. Arsovski said the rack is already in production, built on the LP30 chip Nvidia obtained through its $20 billion Groq deal in December 2025, the same deal that pushed the Rubin CPX it replaced off Nvidia's roadmap.

SRAM without HBM

Artificial Analysis ran the comparison on a private, pre-release Gemma 4 31B endpoint served through Google Cloud, taking the median of 50 sequential client requests at a concurrency of one, while the public providers it measured against ran shared production serverless endpoints. Serving one request at a time produces the highest per-user token rate the hardware can post, and it's not directly comparable to the multi-tenant conditions the other endpoints run under.

Nvidia's on-stage demo showed a higher figure still, 10,996 tokens per second on the same 31B model, which Igor Arsovski, Nvidia's VP of hardware, flagged on stage as "self-reported" before telling the audience the aim was "third-party verified independent benchmarks that you guys can trust." Gemma 4 31B is also a dense model small enough to sit inside a single LPX rack, and the picture at trillion-parameter mixture-of-experts scale, where memory capacity becomes the main constraint, went unaddressed.

Each LP30 carries roughly 500MB of on-die SRAM and no HBM, so a full LPX rack of 256 chips holds 128GB of memory delivering 40 PB/s of aggregate bandwidth against 315 PFLOPS of FP8 compute, with 350 ns of chip-to-chip latency in a Vera Rubin-compatible, MGX liquid-cooled rack that scales past 1,000 LPUs.

Keeping model weights resident in SRAM rather than streaming them from HBM removes the memory-access latency that dominates single-token decode, and the design drops caches, branch prediction, and out-of-order execution in favor of a fully deterministic pipeline that the compiler schedules at clock-cycle granularity. The architecture descends directly from the Tensor Streaming Processor that Groq, founded by ex-Google TPU engineer Jonathan Ross, described in a 2020 ISCA paper titled Think Fast, the same title Arsovski and Raghavan reused at Hot Chips.

A Rubin GPU carries 288GB of HBM4, roughly 576 times the memory of a single LP30, so a 31-billion-parameter model at FP8 needs on the order of 62 LPUs to hold its weights, and a large mixture-of-experts model runs into four figures of chips across several racks. Capacity is the cost of the SRAM-only design, and it's why Nvidia is describing the LPU as for decode rather than as a general-purpose replacement for its GPUs.

Determinism lets the compiler predict power draw cycle by cycle, which Nvidia uses to pre-order current from the rack's regulators ahead of demand, cutting voltage droop by more than 60% and overshoot by more than 70% against an uncompensated load. The same per-block scheduling lets the hardware equalize heat instead of throttling to the hottest tile, which Arsovski put at roughly 10% to 11% additional performance under a fixed thermal limit. "By doing this, we can actually get more utilization of the chip under the same thermal limit, basically. So we can actually get, again, about 10 to 11% more performance under the same thermal limit. So this is another benefit of deterministic execution."

Across racks, Nvidia synchronizes chips to a single virtual clock in what it calls a plesiosynchronous network, with each chip acting as both processor and router so the fabric needs no adaptive routing or congestion sensing, and clock drift between chips is compensated at the chip-to-chip links. Asked during Q&A about the blast radius of a chip that fails mid-workload, Arsovski said users "would experience the exact same as any other hardware in the industry" and would "just checkpoint it or reconfigure the hardware."

Splitting inference with Rubin

Nvidia is pitching the LPX rack as a decode co-processor bolted onto Vera Rubin NVL72, with Rubin GPUs handling the compute-heavy prefill phase and building the KV cache while the LPUs generate output tokens. Nvidia showed three ways to divide the work: disaggregated prefill and decode; attention-FFN disaggregation, which keeps attention and its cache on GPU HBM while the LPU runs the feed-forward layers; and external-draft speculative decoding, where a small model on the LPU proposes tokens that the GPU verifies in parallel, with only draft tokens crossing the link.

An FPGA bridges the synchronous LPU domain and the asynchronous world of host I/O and GPU hand-offs, and Nvidia's Dynamo runtime, together with an LPU extension to CUDA, orchestrates the split. The company put the gains from these modes at roughly three-to-five-times over Rubin alone on a two-trillion-parameter workload with a 400K-token cached context, all Nvidia-measured.

Cerebras CS4

Cerebras used the same Hot Chips session to present its CS4 wafer-scale system, which chief system architect Jean-Philippe Fricker said runs up to 30 times faster than GPUs and doubles the token rate of the CS3 while carrying 10 times the token capacity. Each CS4 rack packs three wafer-scale engines into a new modular platform Cerebras calls Nexus, built around pluggable compute "backpacks" that separate power, compute, and I/O, and Fricker put its memory bandwidth at 43 PB/s, which he told the audience was "2,000 times higher memory bandwidth than Nvidia's next-generation Rubin chip." Cerebras also has a partner for the prefill side of the same problem: it agreed in July to pair AMD Helios GPUs for prefill with its wafer-scale engines for decode, the same division of labor Nvidia now builds in-house with Groq.

Nvidia pulled the Rubin CPX, its own GDDR7-based long-context accelerator, to focus on shipping the LPU this year, a decision VP Ian Buck laid out at GTC 2026. The $20 billion deal that produced the LP30 was structured as a non-exclusive IP license plus the hiring of Ross, president Sunny Madra, and most of Groq's engineers, a form that avoided a formal merger review. Arsovski opened the Hot Chips talk by calling it "a pinch me moment for the Groq team that's now integrated into the Nvidia group."

Senators Elizabeth Warren and Richard Blumenthal wrote to the FTC and to Nvidia in early 2026, arguing the arrangement acquired Groq "in all but name," and no formal, deal-specific investigation has been confirmed as of late August.

Full Nvidia Groq Hot Chips 2026 presentation

The U.S. Department of Justice and FBI revealed in a statement Wednesday that it had seized domains related to platforms that it claims were operated by China state-sponsored hackers. The press release says the Federal Reserve, Department of Energy, Department of Justice, Department of Health and Human Services, NIH, NASA, and U.S. Senate all experienced "computer intrusion activity."

The Justice Department says a state-sponsored group known as QTFY is responsible for the intrusion, which the U.S. government claims came to be through two pieces of malware: QTRouter and QScan. The release says the People's Republic of China (PRC) Ministry of State Security was among QTFY's paying customers.

According to the U.S. government, QScan "scans and automatically infects thousands of [IoT] devices worldwide." Those devices are then added to the QTRouter network. It's a botnet, but the Justice Department also calls it an "obfuscation layer" to mask the origin of malicious traffic. QTFY's system has been used to compromise U.S. critical infrastructure since 2018, according to the affidavit.

The group is said to be employed by the Nanjing Xinjiuwei Network Technology Company, which we were unable to find any information on.

As part of the action, the Justice Department seized three domains: qtproxy.xyz, qt-proxy.org, and qt-team.com. Those domains now show the seizure notice you can see below.

The investigation into the group began as early as 2019, when the FBI looked into a system intrusion at NASA related to the CVE-2019-11510, which was subsequently patched. The FBI traced the activity back to two Gmail accounts and a phone number with a +86 country code (the code for the PRC).

The group allegedly rented infrastructure from commercial platforms, leading to a series of abuse complaints to the emails by hosting provider Hostwinds. The FBI says the group obtained the three domains it seized between 2022 and 2024, registering them with domain registrar Namecheap and paying through PayPal.

Although the PRC routinely denies hacking activities in the U.S., Chinese officials reportedly acknowledged that the government was behind a series of attacks on U.S. infrastructure late last year. In 2024, 30-year-old wiretap systems deployed by the U.S. government in telecom and internet providers were reportedly compromised by Chinese attackers.

Xbox has unveiled its long-rumored new disc-to-digital service, which will let players who own physical copies of games claim digital entitlements, playing them as if they own digital copies. In an announcement, Microsoft confirmed support will include digital benefits such as Xbox Play Anywhere and Xbox Cloud Gaming.

"Players have spent decades building their XBOX libraries. Some games were purchased on disc. Some were purchased digitally. Many span multiple generations of hardware," the company stated in a blog post on Xbox Wire. Microsoft says the new feature "brings the benefits of digital to physical discs." Xbox Insiders can test the feature from August 31.

The company says that "most" disc-based games for Xbox One and Series X support the new feature. If you insert a game disc into your Xbox One or Series X, you can claim a "digital entitlement" by launching the game.

That means that games you own on disc will be accessible through your digital library, including support for Xbox Play Anywhere and Xbox Cloud Gaming "where supported."

You can also keep using your physical copy as normal. Microsoft says that "thousands" of titles are supported, with more being added over time, but confirms that not every title will be available at launch.

The news will be of some comfort to fans of physical media, especially given recent developments in the gaming sphere, notably Sony's decision to end physical media with its next console. While the move does give physical media a new lease of life in some regards, it still marks a shift away from physical media towards online, digital libraries, encouraging users to transition from physical to digital.

Lux Claridge, the teacher who was arrested in Emporia, Kansas, during an AI data center meeting for clapping, said that the city has dropped the charges against them. According to 404 Media, the Emporia assistant city attorney told Claridge’s lawyer that the case has been dismissed without prejudice in municipal court because of conflicts on the matter. Unfortunately, that doesn’t mean that they’re in the clear yet, as the case has been bumped to the County Attorney’s office for further review.

“Today, I was informed by my attorney that the city has dropped my charges and has passed my information along to the county,” Claridge told the publication. “As far as I know, I’m in the clear unless the county is feeling froggy.” This series of unfortunate events started when the teacher politely clapped five times after a speaker gave their comments against the proposed data center in the city. It seems that the city’s commissioners had warned against clapping and other reactions beforehand, saying that it was disruptive. So, when they clapped, someone from the crowd, presumably someone with authority, asked a police officer to take the next person who claps outside of the venue.

This was when several cops approached Claridge, who asked them to leave, but they stood firm, saying, “I have a right to speak.” When the authorities insisted that they go, they just said, “Drag me out,” after which the four officers cuffed the individual, physically lifted them, and hauled them off to the Lyon County Jail. They were finally let out after eight hours, after which they were charged with disorderly conduct and interfering with a law enforcement officer.

After this incident, the city canceled the public meetings set for August 5 and August 19, 2026, and moved them online. The city said that commissioners were receiving death threats from external sources and that it’s taking the measure to protect the safety of its officials and the public. However, it also didn’t allow public comments, which some are questioning, although it did tell citizens that they can phone in their concerns instead. Nevertheless, the city will return to in-person meetings soon, and it will also open public comments once more, with Claridge quoting Emporia mayor Becky Smith saying that she’s “willing to give us another shot to act like grown-ups.” Emporia is expected to put the data center ban to a vote in November via public ballot. However, it also said that even if the people approve the proposed ordinance, it can only take effect after it has been ruled that the petition is deemed legal in the first place.

For as much as we might discuss the performance of an individual CPU, GPU, or other chip in a rack-scale AI system, the ultimate constraint on the performance of those chips is the amount of power one can get to the building and into each of the racks that contain them. The management and allocation of that power is a major concern for maximum productivity from a data center installation going forward.

During Nvidia's Hot Chips presentation on the Rubin GPU, the company emphasized this hard limit on data center capacity and touted the amount of compute that Vera Rubin NVL72 systems can deliver within an example fixed facility power budget of 100MW.

Nvidia says that the use of all of Vera Rubin’s power management technologies, in tandem with its DSX MaxLPS (Land, Power, Shell) suite of design and site-level dynamic power management resources, will allow operators to provision installations of 40,000 of those next-gen chips GPUs (or about 40 Rubin DGX SuperPODs) within that 100MW budget, and expects that hardware to deliver up to 2 zettaFLOPS (ZFLOPS) for NVFP4 inference and up to 1.4 ZFLOPS for NVFP4 training.

Doing some back-of-the-napkin math for ourselves from publicly available Rubin specs, we feel safe in assuming that those performance figures are estimated, not measured. The maximum number of achievable FLOPS from real-life workloads is likely to be significantly lower for a host of reasons.

But the overall point still stands: getting the most compute out of precious power budgets when planning the AI data centers of the future is going to require more refined planning, monitoring, and facility management than simply applying the coarse measure of estimated peak power draw for every electrical component in the facility. And Nvidia has those building blocks ready for data center constructors in the form of its DSX toolkit.

According to a companion blog post that Nvidia shared, as data center operators provisioned their facilities in the past, many of the assumptions they made around power usage focused on those fixed, worst-case power peaks per rack, potentially leading to inflated power budgets that end up stranding power allocation in racks that will rarely, if ever, use all of it.

In just one example, if there was an application load differential between racks in a cluster such that one system would benefit from having more power sent its way in that moment, it couldn’t be re-routed under a static provisioning scheme. The less-utilized rack would use less of its allocated power budget, and the more heavily loaded one might still run into the limits of an overly conservative guard band.

The DSX MaxLPS approach is meant to overcome the limitations of static power provisioning by instead applying an intelligent, dynamic scheme that is continuously aware of power usage at the chip level, rack level, and groups-of-racks level. Where unused power is available due to workload characteristics or idle capacity, Nvidia's Dynamic Power Software control loop can find and redistribute that energy to systems where it's most needed in the moment, maximizing the number of systems that can be installed and performance per watt from the facility over time.

In Nvidia's measured example of current GB300 racks, within a 540kW power budget and with static provisioning, an operator might be able to install four 135kW systems by statically provisioning for peaks rather than measured values from workloads. But in practice, as much as 170kW of that power budget might sit unused due to differences in rack utilization.

For modern rack-scale systems like Nvidia’s NVL72s, that’s an entire rack and change that could safely be installed within the same power budget, and indeed, that’s just what the company’s example shows. And across those five systems, the amount of reserve power allocated for peaks can be much lower.

At the rack level, DSX MaxLPS offers further flexibility through workload-specific power profiles. Much like the quiet, balanced, and high-performance power modes that client PC users are familiar with, Nvidia has produced rack-level power profiles that can be assigned to systems performing example workloads like inference, training, and more general memory-bound or compute-bound tasks.

As we noted, Nvidia didn’t share measured Rubin power or performance-per-watt results, but it has characterized the benefits of MaxLPS for prior-generation systems running inference workloads to prove the concept.

For a Grace Blackwell GB300 system running DeepSeek-R1, Nvidia says the past fixed-peak regime would have assumed a 1400W GPU TGP and an estimated rack power of 136kW. Applying MaxLPS, however, the typical GPU TGP under this workload falls to 1000W, and the total rack power falls to 101kW, all without affecting delivered performance.

That less conservative envelope translates directly into higher performance per watt, larger numbers of racks that can be installed within the same facility, and ultimately more tokens that can produce revenue for the data center operator or its tenants.

Nvidia further notes that designing a data center with MaxLPS from the start grants an operator greater flexibility over the life of the installation. For example, if a site starts as a training-focused facility outfitted with cutting-edge hardware, each installed system is likely to need a greater share of the available site power for that more intense workload, and so an operator might not want to populate every available floor space for those racks from the get-go.

But later in the life cycle, as training shifts to new generations of hardware and older systems transition into inference roles, the power demands of each GPU and rack will fall, and so a facility with dynamic power provisioning would be able to free up capacity that can then be used to install more hardware within the same facility and to generate more profitable tokens.

Another major component of MaxLPS in data centers deploying Vera Rubin hardware is the use of higher liquid coolant temperatures for the exclusively liquid-cooled Rubin NVL72 racks. Those systems are designed to work with 45 °C inlet coolant temperatures, much higher than for past liquid-cooled systems. We learned more about this “dry cooling” approach during our visit to Nvidia’s Vera Rubin proving grounds earlier this year.

The use of this higher coolant temperature for Rubin installations is important because the mechanical chillers used to shed waste heat in non-evaporative systems also consume a large portion of the site power budget – as much as 40% for past installations, Nvidia says. As with static provisioning for servers, the company notes that those chillers have traditionally been sized for the worst-case scenario that a facility might face, even if they’re operating well below that capacity for much of the year.

Again, this approach strands power that could be dynamically reallocated to compute given the proper operating conditions and site-level monitoring and management. Those chillers might still need to run during the hottest parts of the year, but outside of those conditions, the higher coolant temperature generally enables more power to be put to productive use, improving a site’s power usage effectiveness (PUE) figure, all else equal.

Power for AI data centers, whether generated by public utilities or behind the meter using alternative power sources, is expected to remain one of the most critical constraints for those facilities for the foreseeable future, and we heard that concern from multiple presenters during Hot Chips.

Nvidia’s DSX MaxLPS approach looks ready to provide the building blocks needed for dynamic allocation of that resource to extract the maximum possible performance per watt from Rubin facilities, and it reflects a comprehensive concern for the interplay of power and achievable performance that only seems likely to grow in importance going forward.

Newegg has come out with a new high-end AM5 bundle that pairs the venerable Ryzen 9 9950X3D processor, 32GB (2x16GB) Corsair Vengeance DDR5-6000 RAM, and a premium motherboard in MSI’s MPG X870E Carbon Wifi for only $1,345.99. You save $260 (16%), which, when you apply that beefy combo discount to the 32GB of RAM, makes it only $230, which is a solid deal for these expensive times.

• Check out all the RAM combo deals on Newegg

The RAM portion of the bundle, Corsair’s Vengeance RGB 32GB (2x16GB) DDR5-6000 CL36 RAM kit, is currently available on Newegg for a wallet-emptying $489.99 when bought separately. The good news is that the massive discount brings this RAM down to $230. This black kit matches the MSI X870E Carbon board and features a frosted RGB light bar that runs the length of the sticks. If RGB lighting isn’t your thing and you prefer a stealthy, unassuming look, you can turn it off. The DDR5-6000 speed lands in the sweet spot for the AM5 platform and is a worthwhile, good-looking option. The SK Hynix ICs under the heatsink pair nicely with the MSI board and offer plenty of overclocking headroom.

Next is the Ryzen 9 9950X3D processor. Before the X3D2 came out, this was the flagship X3D SKU and offered not only a lot of cores and threads (16c/32t) but also X3D cache (128MB) on one of the CCDs, which is great for gaming. Clock speeds start at 4.3 GHz, with turbo boosting a couple of cores up to 5.7 GHz. Between the clock speeds, core count, and extra cache, this processor is not only a great gaming CPU, but also awesome for any type of PC work that needs a lot of cores and threads, like video editing, 3D rendering, and even running VMs.

Last but certainly not least is the MSI X870E Carbon Wifi motherboard (one we reviewed in the past. We loved the robust power delivery (perfect for the 9950X3D), the dual Ethernet ports, EZ features, storage options (4x SATA, 4x M.2 sockets - one PCie 5.0) and ample connectivity on the rear I/O, including four USB Type-C ports (2x 40 Gbps). The all-black motherboard sports a cool RGB design element of the MSI dragon on the VRM heatsink to break up the monotony and offers some bling to the inside of your chassis.

In the end, we like this combo as it takes some of the pain out of buying a PC today. Priced at $1,345.99 and at a significant savings of $259.99 (16% off) with a free 240mm Cooler Master Elite AIO ($79.99 value) and AMD Game Bundle ($69.99 value), it will be tough to find a better deal out there for this wicked gaming (and productivity!) bundle.

Fujitsu gave us a detailed look at its 144-core Monaka server CPU at Hot Chips 2026 on August 24, confirming for the first time that the Arm chip runs dual 256-bit SVE2 vector units, down from the 512-bit SVE in its A64FX predecessor, and that its entire last-level cache sits on a separate 5nm die beneath the 2nm compute die.

Ryohei Okazaki, lead architect of Fujitsu's processor development team, presented the design as "a made-in-Japan CPU, specifically engineered for AI performance and power efficiency," built for what the company calls green AI data centers and subsidized by Japan's New Energy and Industrial Technology Development Organization. The chip ships in two SKUs: a 350W air-cooled part at 2.1 GHz base and a 500W liquid-cooled part at 2.9 GHz base, with evaluation samples available now and volume production in 2027.

Three dies, one stack

Monaka splits into three tiers of silicon: a 2nm core die on TSMC N2P, a 5nm SRAM die on TSMC N5 that holds the whole last-level cache, and a 5nm IO die. The core die stacks face-to-face on top of the SRAM die through hybrid bonding, sitting on the cooling side because it runs hottest, while the IO die connects to the SRAM die across a silicon interposer. Fujitsu keeps 2nm silicon under 30% of total die area, a split Okazaki said lets Fujitsu "accelerate the time to market for our 2-nanometer-based chip" by pushing everything that shrinks poorly onto the 5nm SRAM and IO dies.

Putting the full last-level cache on a distinct stacked die separates Monaka from AMD's 3D V-Cache, which bonds extra SRAM on top of a compute die that already carries its own L3, and lines it up closer to Intel's Clearwater Forest, where local cache sits in a base tile with compute stacked above. Fujitsu also moved the low-dropout voltage regulators onto the 5nm SRAM die because analog circuits scale poorly at 2nm, and placed them directly beneath the core's floating-point units to feed per-core dynamic voltage and frequency scaling.

Dr. Ian Cutress of More Than Moore asked whether Fujitsu was "doing anything special to minimize core-to-core latency" given that the core dies sit on opposite sides of the package and traffic routes through the IO die and back. Fujitsu pointed to the face-to-face hybrid bonding between the core and SRAM dies but declined to disclose latency figures.

From 512-bit vectors to 256

Chester Lam of Chips and Cheese asked why Fujitsu narrowed the vector datapath from the 512-bit SVE in A64FX to 256-bit SVE2 in Monaka. Okazaki said the chip is built "for [the] data center" and that Fujitsu wanted to "minimize the core size" for the best cost and performance, with the narrower units also cutting SIMD width for general-purpose code.

A64FX, the 7nm CPU that powered the Fugaku supercomputer and became the first chip to implement Arm SVE, paired its 512-bit vectors with on-package HBM2 for memory-bound HPC. Monaka drops HBM for 12-channel DDR5 at 8000 MT/s and runs two 256-bit SVE2 units per core, each aligned to a 256-bit load/store unit, with FP8 and INT8 matrix support added for inference.

The core carries mainframe-class reliability features Fujitsu inherited from its own processor line: ECC or duplication on the L1 and L2 caches, parity checks on execution units and registers, and a hardware instruction-retry mechanism to recover from transient errors. It also runs a three-level TAGE branch predictor and six ALUs for general-purpose throughput, on a core that Fujitsu measures at roughly 1.47 mm².

Performance estimates and rivals

Fujitsu estimates the 350W SKU at 4,355 GFLOPS in DGEMM and 69.7 TOPS in INT8, and the 500W SKU at 6,013 GFLOPS and 96.2 TOPS, with both parts rated around 500 GB/s in STREAM Triad. The company claims up to two-times AI performance and over 50% TCO reduction against unnamed comparisons, and credits ultra-low-voltage operation, running the core around 30% below nominal voltage for roughly half the power, for holding 144 cores inside the 350W envelope. Okazaki described the voltage technique as delivering "energy saving comparable to moving one generation beyond the 2 nanometers," achieved with custom SRAM and a proprietary CAD flow tuned for non-standard low-voltage operation.

By 2027, Monaka's 144 cores will land in the middle of the Arm server field rather than at the top of it. AWS's Graviton5 reaches 192 Neoverse V3 cores on a single 3nm die, Ampere's roadmap runs to 512 cores in AmpereOne Aurora, and Microsoft's Cobalt 200 packs 132 cores with its own per-core DVFS. Monaka's separation from that group rests on the cache-on-die stack and 12-channel DDR5 bandwidth rather than core count, and its 256-bit SVE2 width matches SiPearl's Rhea1 while exceeding the 128-bit SVE2 common to hyperscaler Arm cores.

NEDO subsidizes Monaka under a green data center program targeting 40% energy savings by 2030, yet the chip's 2nm and 5nm dies come from TSMC rather than a domestic fab. That gap between a made-in-Japan design and Taiwanese manufacturing sits awkwardly against the sovereignty that Fujitsu and RIKEN are seemingly keen to attach to the program.

Japan has committed more than 2 trillion yen to Rapidus for 2nm production in Hokkaido by 2027, and roughly 1.2 trillion yen to TSMC's Kumamoto fabs, and NEDO has separately backed a dedicated 1.4nm AI chip from Fujitsu and IBM Japan to be built entirely in Japan by Rapidus. Monaka predates that domestic capacity, however.

Monaka's successor is already assigned to a flagship machine. FugakuNEXT, the roughly $750 million RIKEN system announced in August last year with Fujitsu and Nvidia, will pair a 1.4nm-class Monaka-X that adds Arm SME2 with Nvidia GPUs linked over NVLink Fusion, the interconnect Nvidia opened to third-party CPUs in 2025. RIKEN targets more than 600 FP8 exaFLOPS within a 40MW envelope and roughly 100 times Fugaku's application performance, with operation around 2030. FugakuNEXT is Japan's first flagship supercomputer to place GPUs at its core, a departure from the CPU-only A64FX design of the original Fugaku.

Fujitsu has firmed up rather than changed the Monaka plan across three years of disclosures, with the core count, node split, and an anticipated launch date of 2027 remaining unchanged since 2023. Fujitsu didn't disclose pricing, and its DGEMM, STREAM, and INT8 figures remain estimates until independent testing at the 2027 launch

Full Fujitsu Monaka Hot Chips 2026 presentation

A Nevada court has found a Las Vegas businessman guilty of 11 counts of wire fraud, two counts of mail fraud, and two counts of money laundering for defrauding over $24 million from hundreds of investors. According to the U.S. Attorney’s Office, Brent Kovar presented Profit Connect to investors as a crypto mining operation that used an artificial intelligence supercomputer for its operations. He promised a fixed rate of return that paid 15% to 30% APR and also came with a 100% money-back guarantee. However, it turned out that the “profits” that the earlier investors made only came from the influx of newer investors, and that the company wasn’t making any money.

Kovar victimized 400 investors, telling them that the company had cryptocurrency reserves worth hundreds of millions of dollars. In reality, it had zero holdings, couldn’t afford to pay investors their interest earnings, and had no way to return their cash despite the offered guarantee. “He used investor money to operate Profit Connect, to buy gifts for employees, to buy a house for himself, and to repay investors as if those repayments came from mining cryptocurrency and verifying cryptocurrency transactions,” the Department of Justice said in its press release.

Profit Connect operated from 2017 to 2021, which is around the same time that Bitcoin made its first major rally, with the cryptocurrency hitting a peak of over $19,000 in December of that year. This, combined with the buzzwords of “AI” and “supercomputer,” made it seem to investors that they were putting money in a groundbreaking new technology that would change the way we do finance. Unfortunately, the venture turned out to be a scam and defrauded investors out of their money with nothing to show for. It’s unclear if the funds that the investors lost will ever be recovered, but Kovar is facing a possibly lengthy jail time of a maximum of 280 years for his crimes.

Another scam rooted in cryptocurrency investment from 2017 saw retail investors lose $2 billion. BitConnect offered a similar guaranteed return on investment for anyone who put up cash on its platform until 2018, but instead of using the deposited money for trading, it was just siphoned off to the organization’s private digital wallet addresses. Even before this, another Ponzi-scheme scammer made off with $6 billion from her victims in China from 2014 until 2017 and converted it into 61,000 Bitcoin. While she has already been arrested, the seized cryptocurrency is still in limbo, so it’s unclear if and when her victims will get their money back.

Microsoft has opened preorders for the Designed for Xbox 25th Anniversary Collection, a set of officially licensed peripherals in translucent "OG Green" marking 25 years since the original Xbox launched on November 15, 2001. Every item comes from a third-party partner rather than Microsoft, and the set leans toward PC and mobile, with three Razer desktop peripherals, two phone controllers, an 8BitDo charging dock, and a PowerA bag built to carry the Asus-made ROG Xbox Ally handhelds. Most units will ship in late October, with the Razer gear dated for October 20.

• Razer Basilisk V3 Pro 35K – XBOX 25 Anniversary Edition
• Razer BlackWidow V4 75% – XBOX 25 Anniversary Edition
• Razer Hammerhead V3 X HyperSpeed – XBOX 25 Anniversary Edition

Razer naturally contributes the priciest pieces, with the Basilisk V3 Pro 35K mouse at $179.99, the BlackWidow V4 75% keyboard at $209.99, and the Hammerhead V3 X HyperSpeed for Xbox earbuds at $119.99, each carrying a $20 premium over the standard model.

The Basilisk pairs Razer's Focus Pro 35K Optical Sensor Gen-2 with up to 140 hours of wireless battery, and the BlackWidow comes with hot-swappable Orange Tactile Switches Gen-3, the same internals as their standard versions. Backbone's Pro controller runs $159.99 in a blind-box format that hides which of three heritage colorways sits inside, GameSir's X5 Lite phone controller lists at $44.99, and 8BitDo's Ultimate Charging Dock lists at $39.99. The translucent green shells revive the look of the original Xbox and its Halo special editions, down to the four-color face-button palette that carries over to the separate anniversary console.

The keyboard carries a dedicated Xbox key that opens Windows Game Bar, the same shortcut the ROG Xbox Ally maps to its own Xbox button, and the collection's other anchors are a bag for that Windows 11 handheld and two clip-on controllers for phones. That composition aligns with the platform that Xbox has become, after Microsoft began rolling Xbox Mode out to Windows 11 desktops and laptops in April, extended the ROG Xbox Ally's full-screen interface across PCs, and started building its next-generation console on a custom AMD chip.

The Xbox Series X25 Limited Edition is the only anniversary product Microsoft makes itself, announced June 7 at the Xbox Games Showcase and due in November alongside a matching Wireless Controller X25 sold separately. It reuses the standard Series X internals with 1TB of storage in a translucent green shell, and Dealabs pricing cited by TechPowerUp puts it near $899, roughly $100 above the current 1TB Series X, though Microsoft hasn't yet confirmed a price.

Finding a good budget laptop under $500 has always been challenging, and rising component costs have pushed even the most basic models over that mark. Chuwi’s UniBook, for $449, looks tempting on paper, pairing Intel's latest entry-level Core processor with a thin metal chassis, a backlit keyboard, and more ports than many of the best laptops. But as with most bargain notebooks, the question is whether spending more is worthwhile, and our overall experience is that the extra you’d spend for the Dell XPS 13 or MacBook Neo gets you a markedly better laptop experience.

Design of the Chuwi UniBook

The UniBook looks more expensive than its $449 price tag suggests. Its aluminum alloy lid and bottom cover give it clean, modern look, while the slim profile helps it blend in with far more expensive ultraportables. It feels solid, with little evidence of flex.

Its compact 0.65-inch profile and 2.64-pound weight make it easy to slip into a bag, and its understated design would look at home in almost any environment. Both the MacBook Neo and the Dell XPS 13 are thinner, and the latter considerably lighter (2.2 pounds), but both are more expensive.

Port selection is the biggest surprise. While many thin-and-light laptops reduce connectivity, Chuwi went the opposite direction, with two USB-C 3.2 Gen 1 ports (both supporting power delivery and DisplayPort), three USB-A ports (two 3.2 Gen 1 and one 2.0), HDMI 1.4b, Gigabit Ethernet, an audio jack, and even a micoSD card slot. Compare this to the USB-C-only selection on the MacBook Neo (which also features a headphone jack) and XPS 13.

Internally, the UniBook’s Wi-Fi 6 and Bluetooth 5.2 standards aren’t the newest, but are perfectly adequate for this laptop’s intended use.

Chuwi UniBook Specifications

Productivity Performance on the Chuwi UniBook

We evaluated the Chuwi UniBook with an Intel Core 3 304 processor, Intel Graphics, 8GB of LPDDR5-6400 memory, and a 256GB PCIe 3.0 SSD.

Our comparison lineup consists of the Apple MacBook Neo and Dell XPS 13. Both laptops start at $699.99 before education discounts, and pack more powerful hardware. The UniBook’s Core 3 304 processor, featuring one performance core and a 4.3 GHz boost, is at a disadvantage next to the Dell’s Core 5 320 processor, which offers two performance cores and a 4.6 GHz boost. Meanwhile, the MacBook Neo uses a smartphone-grade but very capable A18 Pro chip.

The UniBook trailed the XPS 13 and MacBook Neo across the board. Some of this was expected – for instance, its Geekbench multi-core score of 5,889 points was well south of the XPS 13’s 7,952 and the MacBook Neo’s 8,920, a product of its weaker CPU. This followed into its last-place Handbrake showing.

However, its 294 MBps average speed in our 25GB file transfer test was particularly low even for a budget laptop, with less than half the throughput of the MacBook Neo and not even one quarter of the XPS 13’s. You could tell on paper this would happen, givenit's using the older PCIe 3.0 technology.

In daily use, the slow storage drive and single performance core didn’t hamper basic usage. The laptop booted without delay and webpages loaded quickly. With just 8GB of RAM (which can’t be upgraded), multitasking will be limited, but this laptop isn’t aimed at power users.

We normally run 10 loops of Cinebench 2024 or 2026 to stress-test productivity laptops, but both refused to run the UniBook – 2024 cited a GPU memory allocation issue and 2026 crashed at startup. The system’s 8GB of RAM may have been the culprit. As an alternative, we reran our Handbrake video transcoding test. During the test, the Core 3 304 operated at average frequencies of 3.46 GHz on its P-core and 3.1 GHz on its E-cores.

Display on the Chuwi UniBook

The UniBook’s 14-inch, 1920 x 1200 display is serviceable, but it’s also one of the clearest reminders that this is a budget laptop. Though its IPS technology provides wide viewing angles and it benefits from a practical anti-glare surface, dull colors and lack of contrast produce a washed-out picture. Brightness is adequate for indoor use only. In Captain America: Civil War, scenes that should have looked bright and punchy instead appeared subdued, with colors that felt muted rather than lively. For instance, the red on Captain America’s shield skewed more towards orange than red.

The UniBook’s panel covered just 62% of the sRGB color gamut and less than half of DCI-P3, putting it well behind the MacBook Neo and Dell XPS 13. Brightness was also modest at 298.8 nits, more than enough for typical indoor work but not ideal near bright windows or outdoors. For writing, browsing, and streaming casual video, the display is acceptable; for anything else, it is a compromise.

Keyboard and Touchpad on the Chuwi UniBook

The UniBook's keyboard is one of its highlights. Chuwi includes white backlighting, a welcome addition that's often omitted on budget laptops. (Notably, it’s missing from the MacBook Neo.) The keys are comfortably sized and spaced, making it easy to settle into a comfortable typing rhythm. I reached 120 words per minute with perfect accuracy on Monkeytype during my initial run – it felt predictable and responsive, though some keys exhibited occasional rattling that detracted from an otherwise positive typing experience.

The touchpad is compact but responsive and accurate. Its main drawback is the loud mechanical click mechanism, which is noisier than I expected and can become noticeable in quiet environments. Everyone within earshot will know you’re clicking. Otherwise, using the touchpad is comfortable and predictable.

Audio on the Chuwi UniBook

Audio performance is one of the UniBook’s weaker areas. While usable for voice calls, with sufficient volume and clarity, they struggle with entertainment. The sound is thin and lacks bass response, leaving music sounding flat and lacking impact. Listening to Phil Collins’ “Don’t Lose My Number”, for example, I heard almost no bass and the vocals lacked depth. The laptop doesn’t include an audio tuning app with equalizers.

Speaker positioning is also a problem. Because the speakers fire downward from the underside of the chassis near the palm rests, they only perform well when the laptop is on a hard surface. The sound is noticeably muffled if the laptop is in your lap or on a couch or bed.

Upgradeability of the Chuwi UniBook

The Chuwi UniBook offers reasonable upgradeability: the M.2 2280 SSD, M.2 2230 wireless card, and battery can all be changed out by removing the bottom panel. Removing the panel is straightforward, though two different sizes of Philips-head screws are used. The clips on the back edge can then be popped using a plastic pry tool, followed by the sides.

Battery Life on the Chuwi UniBook

Our battery testing consists of web browsing, running OpenGL tests, and streaming videos with the screen at 150 nits while connected to Wi-Fi. The UniBook’s time of 14 hours and 14 minutes is an excellent result for a budget laptop. It practically tied the XPS 13 (14:28) and nosed past the MacBook Neo (13:28).

Heat on the Chuwi UniBook

We normally measure peak laptop surface temperatures while running 10 loops of Cinebench, but that benchmark wouldn’t run on the UniBook. We substituted it with our Handbrake video transcoding test, which fully stressed the CPU for about 12 minutes. The UniBook’s surfaces didn’t stray far from room temperature – we recorded 80 degrees Fahrenheit on the touchpad, 88 F between the keyboard G and H keys, and 92 F on the bottom near the exhaust vents.

Internally, the Core 3 304 ran at an average temperature of 73 degrees Celsius. The exhaust fan was audible but restrained.

Webcam on the Chuwi UniBook

Chuwi’s webcam isn’t great – with just a 720p sensor, images have a soft focus and lack detail. I couldn’t read my wristwatch when I held it more than two feet away. On the bright side, it does offer a privacy shutter, which sometimes even pricier laptops leave out.

Software and Warranty on the Chuwi UniBook

Beyond the usual apps included with Windows 11 Home, Chuwi preloads no other software onto the UniBook.

The laptop is backed by a one-year warranty. Sending the laptop for warranty repairs could prove costly as it will need to be shipped to Hong Kong or Germany at the owners’ expense. (See Chuwi’s warranty policy.)

Chuwi UniBook Configurations

We reviewed the Chuwi UniBook featuring a Core 3 304 processor, Intel Graphics, 8GB of LPDDR5 memory, a 256GB SSD, and a 14-inch IPS 1080p display. It was listed at $449 on Chuwi’s online store at the time of this review, discounted from its $569 MSRP.A second configuration with an Intel Core 5 315, 12GB of RAM, 512GB of storage and a 1080p display that the company claims will cover 100% of the sRGB gamut was listed late in our testing process. As of this writing, the company told us this system is in production but doesn't have a price or release date.

Bottom Line

For buyers seeking a basic ultraportable, the Chuwi UniBook gets a few of the fundamentals right. Solid port selection and respectable battery life stand out in a price bracket where there are typically many compromises. Unfortunately, the compromises stack too quickly: performance that trails far behind machines like the Dell XPS 13 and MacBook Neo, a below-average display, weak speakers and webcam, and the ongoing risk of costly out‑of‑warranty repairs. So while its savings look good on paper, the many downsides and possibility of expensive service make it worthwhile to stretch your budget for a MacBook Neo or XPS 13, both of which are safer long-term investments.

OXMIQ Labs, a GPU IP company, revealed at Hot Chips 2026 that High Bandwidth Flash (HBF) cannot replace High Bandwidth Memory (HBM) across the vast majority of workloads. For some, HBF could emerge as a specialized memory tier for huge but relatively cold datasets. For others, HBF can do more harm than good.

When SanDisk unveiled its High-Bandwidth Flash (HBF) concept in early 2025, the technology pledged to equip AI accelerators with terabytes of relatively inexpensive memory and reduce the need for traditional High-Bandwidth Memory (HBM), a promise that raised a number of doubts from the very beginning.

The emerging HBF specification includes three performance grades. Grade 1 uses an 8-Hi 256GB NAND stack with an 8 GT/s UCIe interface and 384 GB/s bandwidth. Grade 2 uses a 512GB NAND stack with a 16 GT/s UCIe interface and supports 1.536 TB/s bandwidth. Grade 3 reaches 3.072 TB/s using 32 GT/s UCIe 2.0 while retaining the same 512 GB capacity.

Since HBF relies on 3D NAND, it supports read block sizes between 64 bytes and 4 kilobytes, 4KB writes, and 4KB page sizes. While HBF Grade 1 can barely compete against contemporary HBM, HBF Grade 3 can compete against HBM4E, though we have no idea when such memory will be available. However, the main feature of HBF is not necessarily performance per se, but 8 – 16 times more capacity than HBM at roughly the same cost.

Indeed, OXMIQ believes that HBF should be viewed as a high-capacity memory technology rather than inexpensive HBM. Memory economics depend not only on how many gigabytes an application needs to store, but also on how quickly those bytes must be delivered to the processor. As bandwidth demand rises, adding inexpensive but relatively slow HBF eventually becomes less economical than using HBM, according to estimates by OXMIQ.

Cheap memory =/= cheap tokens

OXMIQ demonstrated the trade-off by modeling a 72-GPU rack running the 1-trillion-parameter Kimi-K2 model at FP4. At cost and power parity, an HBM-only configuration provides 20.7 TB of memory and 1,584 TB/s of aggregate bandwidth. Replacing HBM with HBF increases rack capacity by 14 times to a whopping 294.9 TB, but reduces aggregate bandwidth to 922 TB/s. A hybrid configuration with HBM and HBF provides 89.3 TB and between 279 TB/s and 1,418 TB/s, depending on workload conditions. The difference between HBM and HBF bandwidth is the reason why HBF looks excellent when memory capacity limits the system. However, HBF eventually loses when bandwidth/throughput becomes the limiting factor.

In OXMIQ's model, an HBF-only configuration enables each GPU to hold its own Kimi-K2 instance and run 72 model instances per rack. Whereas an HBM-only configuration requires eight GPUs to hold each model instance (meaning compute performance gets wasted) and can therefore run only nine instances per rack. This makes HBF particularly attractive when memory capacity determines the number of GPUs required. However, as the number of simultaneous users and their token-generation rate increase, HBF's lower bandwidth becomes the bottleneck, while the HBM-based rack can make better use of its substantially higher memory bandwidth and ultimately deliver lower cost per token, according to OXMIQ's model.

As a result, HBF can dramatically reduce the number of GPUs needed simply to accommodate a very large model (i.e., enable one HBF-equipped GPU to do the capacity job of eight HBM-equipped GPUs). Nonetheless, if the objective is maximum inference throughput from a fully utilized rack, HBM may remain the better, more economical choice. At the end of the presentation, OXMIQ concludes: 'HBM for the rack, HBF for the box.'

Niche memory?

Although HBF does not benefit all AI workloads and may even harm the performance of many, there are applications that can benefit from a surplus of local memory.

Mixture-of-experts (MoE) models appear to be particularly suitable for HBF. OXMIQ's Kimi-K3 example has 1.56 TB of weights, of which 1.45 TB, or 93%, consists of MoE expert weights. Since only selected experts are activated for each token, this enormous pool is largely write-once and relatively infrequently read. OXMIQ proposes keeping such experts in HBF while placing the remaining, frequently accessed weights in HBM.

Additionally, more local capacity could also reduce communication between accelerators. Conventional expert parallelism distributes experts across GPUs and requires all-to-all communication at every layer. OXMIQ claims that inexpensive HBF capacity could allow considerably more experts to reside locally, reduce the number of expert-parallel shards, and reduce network traffic. In this case, HBF effectively trades memory capacity for interconnect bandwidth and power consumption.

Long-context inference is another potential use case. Sparse-attention models access only a small portion of their large KV cache during each decoding step, which allows the rest to remain in slower HBF memory. OXMIQ believes that HBF could store this large KV cache while the accelerator fetches only the data needed for each step from HBF to HBM.

The HBM-as-cache idea has a serious limitation. Intuitively, one would put popular experts in HBM and cold experts in HBF. Such a strategy works mainly at low batch sizes or when similar queries can be deliberately batched. As batch size rises and queries become more heterogeneous, however, expert popularity flattens, and the workload accesses a broader range of experts, according to OXMIQ. The working set can then outgrow the relatively small HBM cache, which results in more frequent expert transfers from HBF and reduces the performance benefit provided by HBM caching.

The hardest part

OXMIQ does not expect HBF to work simply as slower GPU memory. Instead, it proposes using HBF in place of host DRAM to store large amounts of less frequently accessed data, such as MoE experts and KV cache. Frequently used data would remain in HBM, while even colder data could still be kept in remote memory or SSDs. This certainly contradicts SanDisk's original vision for HBF: sitting next to AI accelerators. Furthermore, adding HBF support will be complicated on many levels.

The software side of HBF is particularly complicated. To achieve maximum bandwidth, HBF requires large transfers — 64 KB reads and 1 MB writes — and data is moved through DMA rather than the CPU/GPU cache hierarchy. When HBF and HBM are used together, software must also decide which data goes into each memory type and manage HBF's limited write endurance.

Meanwhile, current inference software is not ready for such a configuration. OXMIQ says vLLM would need dedicated HBF support to manage memory allocation and data placement, prefetch data before it is needed, and monitor flash endurance, which requires a major software overhaul. An effort like this has to be a joint effort between the HBF hardware vendors, AI accelerator vendors, and inference-framework developers.

At the lowest level, AMD, Nvidia, and other accelerator vendors would need to provide the hardware/driver/runtime mechanisms for efficiently moving data between HBF and HBM. Then vLLM developers, who work with vendors, would implement the higher-level memory allocator and policies that decide which experts/KV blocks live in HBM and which reside in HBF, when they should move, and how to hide HBF latency.

On the one hand, if AMD or Nvidia adopt HBF, they will provide its partners with everything needed to use it, and while this would take time before everything works as intended, this is a straightforward way to add HBF support to AI platforms. On the other hand, the biggest question is whether hardware vendors like AMD or Nvidia need HBF. As per OXMIQ, HBF's advantage is limited to select use cases, so it may not make sense for AMD or Nvidia to support it universally, especially keeping in mind that managing multi-tier memory hierarchy is hard.

SambaNova is perhaps the most obvious candidate to support HBF. Its SN40L already uses a three-tier hierarchy: SRAM => HBM => DDR, with up to 520MB of SRAM, 64GB HBM, and 1.5 TB of DDR. Conceptually, HBF could become another tier or replace some of that DDR capacity. Then again, this is merely speculation.

HBF remains a nascent technology

While we still have a lot to learn about how HBF works, OXMIQ's model suggests that HBF has a much weaker general-purpose value proposition than the original claim made in early 2025 suggested. It is not useless: it is a specialized solution whose strongest applications depend on particular workload characteristics.

The fundamental problem is that HBF solves memory capacity, while modern AI accelerators are frequently constrained by memory bandwidth. OXMIQ's simulation makes this rather obvious: HBF provides about 14X more memory capacity but only 0.6X the aggregate bandwidth of HBM. Once the workload becomes sufficiently bandwidth-intensive, the enormous capacity stops offsetting the bandwidth deficit.

While the hybrid HBM+HBF solution makes sense for some use cases, it is not a magic fix. When HBM is used as an expert cache, heterogeneous requests at larger batch sizes flatten expert popularity, cause the workload to touch more experts, and reduce cache efficiency dramatically.

For now, HBF has three particularly compelling use cases: reduce the number of GPUs required simply to fit huge models, store massive but infrequently accessed MoE expert pools, and keep large KV caches for sparse long-context inference. For MoE models, its large local capacity could also reduce expert parallelism and expensive all-to-all communication between GPUs. In all three cases, HBF makes sense because capacity requirements are enormous while bandwidth demand remains relatively low.

The developer and publisher of the much-awaited game finally released a statement on the leaks of the game that dropped last week. Rockstar Games said on its X account that the gameplay leaks aren’t how it intended for its fans to see the game and also apologized for the massive delay in the release of the title. However, nothing in the post says anything about the alleged leaker’s protest on the pre-selling of digital copies of GTA VI and their other demands.

“It would be an understatement to say that having videos of Grand Theft Auto VI gameplay leak in this way has been heartbreaking for our team, and this is obviously not how we intended for you to see the game after all this time. We are very sorry that everything has taken as long as it has — from getting the game finished (nearly there!) to sharing more details and official gameplay, and providing the community with everything you want to know,” Rockstar said in its statement. It also added, “While it is unfortunate that the intended game experience may now be impacted by some spoilers, we hope that everyone will wait a bit longer to experience the game for themselves on November 19.” Although we still have about three months before the game finally becomes available on Xbox X|S and the PlayStation 5, an extended look at the game is also dropping tomorrow on Netflix.

Cyberleek started leaking gameplay clips, along with a detailed map of Leonidas, last week. While it was initially thought that the leaked clips were already over a year old, another set of leaks revealed that the hacker actually has a complete working build of the game. They even shot out “LEEK” on a wall to prove that they were playing the game and not just downloading or faking clips. Rockstar has been quiet while the leaks were whipping up a storm on the internet, but its parent company, Take-Two Interactive, issued a subpoena to Microsoft to get the Windows device IDs of all the members of three Discord servers.

Gamers just need to be patient a little bit more, as the release is less than 12 weeks away, at least for consoles, as a PC version hasn’t been announced yet. Nevertheless, there are those who are taking advantage of the popularity of the game and have been releasing malware disguised as leaks of GTA VI. Downloading pirated games online has always been problematic, but downloading an unreleased title off the internet is an absolute recipe for disaster. It’s understandable that a few people are so excited about the title that they cannot wait for the official release to get their hands on it, but three months will pass by quickly, especially for those who have been anticipating the next GTA installment for more than a decade now. After all, good things come to those who wait.

Memory and data storage manufacturer Micron announced its exit from the consumer market earlier this year and pledged to honor warranties for existing products. However, according to a recent RMA case on Reddit, the company seems to be struggling to stick to that promise. Instead of replacing a faulty portable SSD, the company offered the customer a refund that was significantly lower than the product’s current retail price.

Explaining their frustrating RMA experience, u/CarefulMeasurement99 said that their Crucial X9 4TB portable SSD stopped working, after which they contacted support, followed the troubleshooting process, and had their RMA approved. Unfortunately, they were later informed that the company did not have any units available for the particular product because the replacement inventory was exhausted. Therefore, instead of providing a 1:1 replacement SSD, the company would issue a refund of $199.99, the same as the original invoice price from last year.

Due to the ongoing NAND and DRAM crisis, almost every memory and storage product has significantly gone up in price. The Crucial X9 4TB is currently priced at over $500 on Amazon, which basically left the customer facing a loss of nearly $300. The customer explicitly refused to accept the offer and told Micron support that they did not want a refund and requested a 1:1 replacement under warranty. They also offered to accept an alternative 4TB SSD model or wait indefinitely until the RMA inventory was restocked.

After a long ordeal, pushing back eventually paid off for the customer. They sent Micron a firm final email rejecting the refund and demanding a 4TB equivalent replacement or alternative. The customer also mentioned that the case was gaining traction on Reddit and pointed out potential action under the FTC and Magnuson-Moss Warranty Act. Within a few hours, the case was escalated to a different representative, who informed them that a “recent inventory update” had revealed limited replacement stock and approved a direct 1:1 replacement.

While the case raises questions about Micron’s initial claim of no replacement units being available, there is no way to independently verify that. That said, this does not appear to be an isolated case. Earlier this month, we reported a similar RMA horror story in which Micron offered a customer a refund instead of replacing their product due to a lack of inventory. Only after further communication did Micron provide an acceptable solution.

d-Matrix presented Raptor, which it calls the first 3D DRAM accelerator for generative inference, at Hot Chips 2026 this week, showing a TSMC 4nm compute die bonded face-to-face at a 36-micron pitch on top of a custom-designed DRAM die that delivers 100 TB/s of bandwidth from 32GB per card.

Co-founder and CTO Sudeep Bhoja put the vertical interface's energy cost at 0.37 pJ/bit against roughly 2.4 pJ/bit for moving data into an HBM4 base die, calling it "a measured number" from working silicon, and the accompanying ISCA 2026 paper, written with the University of British Columbia, projects around 4.7 times higher throughput per card than HBM-based designs. Bhoja, however, didn't disclose who manufactures the DRAM die.

CEO Sid Sheth told CNBC in June that Raptor is slated to launch in 2027, but at Hot Chips the company gave no firm date or information on volume and pricing, and every performance figure shown, including 988 tokens per second per user on the 2.8-trillion-parameter Kimi K3 model at 1M-token context, is a d-Matrix projection built on early silicon.

The custom DRAM die

Raptor inverts the usual 3D stacking arrangement by putting the logic die on top and the DRAM underneath, so a cold plate sits directly on the compute silicon and the DRAM die doubles as the interposer, carrying PCIe and die-to-die signals down through its TSVs. "For the same amount of power, you can drive the bandwidth up," Bhoja said during the session, "and so we were able to drive the bandwidth up here to 100 terabytes per second."

The one-high stack achieves a power density of roughly 0.5W per square millimeter, which liquid cooling can handle, but the DRAM is designed for a junction temperature of 105°C, where retention collapses from a standard 32ms to 4ms, and the memory must refresh eight times more often. d-Matrix absorbed that penalty by shrinking each microbank to 1,366 rows and about 5.33MB, so a full refresh sweep costs only 1.37% of overall bandwidth.

The die carries 840 banks per chiplet, of which 72 (around 9%) are spares wired into a two-level mux chain the company calls bank chaining, letting any two failed banks anywhere on the die be switched out while channels stay symmetric. A [132,128] Reed-Solomon code on the logic die corrects two symbol errors per 128 bytes, with a CRC behind it. The interface has no PHY, no burst structure, and no sideband pins, so conventional data-bus inversion was impossible; d-Matrix instead compares each 128-byte flit to the previous one and stores a 1-bit inversion tag alongside the ECC metadata, recovering roughly 20% of the I/O power DBI would have saved. At full tilt, the vertical interface still burns 296W of the 422W per-package budget the ISCA paper discloses.

The bank geometry, spare-bank mux tree, refresh behavior, and interleaved ECC columns were all co-designed with the compute die. The 0.37 pJ/bit figure exists only because of that pairing, and no memory maker has anything like this die in its catalog.

Who's supplying it?

d-Matrix has named TSMC for the N4P logic die and Alchip as its ASIC design and 2.5D/3D packaging partner, but neither company operates a DRAM fab, and across the Hot Chips talk, the ISCA paper, and every public announcement since the Pavehawk 3DIMC test silicon came online last September, the firm has never identified who fabricates its custom DRAM. Only three companies make leading-edge DRAM at volume, and all three are allocating capacity to HBM4 lines that are effectively sold out through 2026.

J.P. Morgan estimates DRAM prices will have risen more than 400% between the start of 2024 and the end of 2026. In addition, analysts have recorded contract price increases of 90% to 95% in Q1 2026 alone, and SK hynix CEO Kwak Noh-jung told Reuters in July that "customer demand will remain higher than our supply capacity even beyond 2030."

Nvidia, the memory makers' largest and most leveraged customer, is reportedly testing Rubin Ultra configurations with as little as 192GB because it may not be able to source enough HBM4E. A startup with roughly $450 million raised, asking a memory maker to run a bespoke die with non-standard bank geometry on capacity that could otherwise print HBM, is negotiating from a far weaker position than that, and until the supplier is named, Raptor's 2027 volume plan rests entirely on an unknown, undisclosed dependency.

The die's 11.4 MB/mm² density is roughly half of HBM4's 21.9 to 26.3 MB/mm², Bhoja acknowledged during the Q&A: "A lot of the drop for us was also because we used a not-so-advanced DRAM. And so if we used a more mainline DRAM, just like the HBM4 guys are doing, we would be able to push that up almost all the way to the HBM4 numbers." The density penalty therefore tracks back to whatever foundry arrangement d-Matrix currently has.

32GB per card

Raptor's 32GB per card stands against 192GB to 288GB for HBM4-equipped accelerators, so d-Matrix sizes deployments at rack scale instead: 72 cards carry 2.3TB, enough to hold Kimi K3's weights at 4-bit precision with headroom for around 54 concurrent users at 1M context, by its own calculations. "Even with 32 gigabytes of memory capacity, we are able to solve SOTA models in a scale-up network, so no bits are wasted," Bhoja said.

KV cache growth works against that arithmetic over time, and co-presenter Aayush Ankit, who led Raptor's SoC architecture at d-Matrix before joining Meta's MTIA team, explained the failure mode while dismissing SRAM alternatives: "We are making this unit of compute blazingly fast. Communication becomes a bottleneck soon enough." Once models and context no longer fit a single rack, inference spills into the inter-card synchronization overhead that the vertical bandwidth was meant to eliminate, which the ISCA paper concedes.

Cerebras claimed 969 tokens per second on Llama 3.1 405B in November 2024, with third-party firm Artificial Analysis verifying the figure on live hardware, and that remains the closest published reference point for Raptor's numbers. d-Matrix's 988 tokens per second per user on a model seven times larger would be a step change if it holds water, but no third party has measured Raptor, and the comparison points in the ISCA paper are simulations anchored to early silicon characterization.

Asked by an Nvidia employee about multi-layer stacking plans, Bhoja said: "Our roadmap is still a work in progress, and we have a hard enough time trying to get one-high to work and work around all of the thermal issues of that." Samsung brought its own version of the idea to Hot Chips with zHBM, a concept that stacks HBM directly on the processor and carries a claimed 70% power-efficiency gain over an HBM4E setup, with no production timeline before HBM5. The problem for d-Matrix is that Samsung runs its own DRAM fabs; d-Matrix doesn't. Whether it can get wafers at volume remains to be seen.

Full d-Matrix Hot Chips 2026 presentation

While the unprecedented pace of development of artificial intelligence and its impact on human society is being discussed widely among academic circles, Bill Gates says that nations and governments are not doing enough to prepare their citizens for the upcoming upheaval this technology will deliver. The billionaire said in the GatesNotes blog that the world needs a plan to face the changes that AI will bring, proposing measures including reserving jobs for humans and taxing AI tokens. While other experts and academics equate it with other technologies like computers and the internet, Gates argues that people had decades to get used to the latter and incorporate it in processes and workflows, while the former is already widely available today.

Because of this, he pointed out that people today are facing risks brought about by AI, such as job loss, the use of the tool by bad actors, and the replacement of genuine human relationships, especially among children. Gates said that he will focus on other issues in later posts but dived into the issues of job security and income taxes in his commentary. He said that entry-level jobs have fallen significantly, making it harder for fresh graduates to gain experience and increase their skills. The former Microsoft chairperson acknowledged that society needs time to make adjustments but also pointed out that it might be too late for some unless there is some kind of intervention. “The people who need the most time are the ones who have the least — the accounting worker who’s replaced by a bot or the $20-an-hour worker who loses their job to a $10-an-hour robot,” Gates wrote.

Gates said that as AI and robots improve and replace humans in different tasks, some jobs should be marked as “Human Reserved.” He likened the term to nature reserves, places that people could have developed but didn’t “because the loss would be too great.” These jobs don’t have to be static positions that will stay the same for the rest of time but could be a slow evolution where AI tools are slowly introduced across years and decades. This is particularly important for industries whose practitioners would be displaced by AI and wouldn’t be able to pivot to other skills, citing a 55-year-old who has worked in construction their whole life and is not able to transition to the health industry as an example. He also mentioned that some jobs will never be fully taken over by AI as they require some form of human touch, like education and healthcare.

The billionaire philanthropist also noted that the reduction of human employees would also equate to a reduction in the individual income taxes that governments collect. The U.S. Treasury said that more than half of the federal revenue came from individual income taxes in 2025 and 2026, meaning falling employment would also make a huge impact on the government’s coffers. The current tax setup encourages executives to replace people with robots, which are often tax-deductible expenses. He instead suggested that the government should tax AI tokens and robots — aside from helping the federal balance sheet, it will also motivate businesses to prioritize hiring and keeping people instead of buying machines.

Artificial intelligence is indeed a technological marvel, but it also comes with several risks that must be addressed head-on. Bill Gates says that the debate around AI, its use, and its impact on us should be discussed widely among workers, students, community leaders, religious leaders, parents, educators, and more, not just the few who control it.

Montech is a Taiwanese PC hardware brand with deeper roots than its relatively recent public profile suggests. Founded in 2016 by engineers with over three decades of experience in Taiwan's PC industry, the company entered the Western market in 2019 with a value-focused positioning built around PC cases. Those cases sold well, and the catalog expanded quickly into power supplies, air coolers, liquid coolers, fans, and mechanical keyboards. The ambition from the beginning was to become a full-spectrum component brand rather than a niche case maker, and the PSU division is now central to that effort.

The earlier Century series established Montech's credentials in the mainstream segment, but the Titan PLA line represents a substantially higher level of commitment. Rather than sourcing from a mid-tier OEM and competing on price alone, Montech contracted Seasonic. That is not a trivial decision. Seasonic manufactures some of the best power supplies today and is widely regarded as perhaps the most consistently reliable OEM in the consumer PSU industry, responsible for technically accomplished designs under both its own brand and as the manufacturer behind multiple third-party lines. Montech prints the Seasonic name directly on the Titan PLA packaging, which communicates more about the product's engineering pedigree than any marketing copy could.

Specifications and Design

In the Box

The Titan PLA 750W ships in a large, sturdy cardboard box with a restrained and effective visual theme - a black and polished metal aesthetic with a photograph of the unit printed on the front. The Seasonic OEM designation is printed openly on the packaging. For anyone with even a passing familiarity with the PSU market, that detail is more persuasive than any graphic design choice.

Inside the box, the PSU sits in a synthetic pouch surrounded by foam inserts. The accessory bundle is unexpectedly generous. We found mounting screws, an AC power cable, basic cable ties, five quality reusable cable straps, a set of cable combs, and an ATX jump-start adapter for testing. Few companies at any price point include all of that.

The cables are immediately distinctive. Bright white wiring runs throughout, paired with black connectors across the board. The wires feature an embossed braiding across their full length, giving them a slightly textured, uniform appearance that holds up well in windowed builds. The single 12V-2x6 connector is rated at 600W, which is the full ATX 3.1 specification requirement and not a given on a 750W unit. There are two 6+2 pin PCI Express connectors, which covers the most common configurations at this wattage tier but leaves no margin for extremes. It is a practical limitation worth noting before purchase.

External Appearance

At 160mm length, the Titan PLA 750W is marginally longer than the ATX design guide considers standard. Any quality modern tower case will accommodate it without difficulty, but it is worth verifying clearance in compact enclosures before ordering.

The chassis finish is satin white, excellently applied. A diagonally wired, chromed fan finger guard covers the intake, and chrome decorative elements extend to both side panels. The result is refined without being ostentatious. The top panel is home to a black electrical specification and certification sticker.

The front panel houses the standard power switch, AC receptacle, a small decorative plate, and a toggle switch for the hybrid fan mode. The rear modular connector bank is organized logically, with a subtle black legend identifying each port type. The ports are keyed anyhow, and inserting cables into the wrong connector is practically impossible.

Internal Design

Cooling relies on a Hong Hua HA13525H12F-Z 135mm fan with a fluid dynamic bearing (FDB) engine, rated to a maximum of 2300 RPM. That ceiling is high for a 750W unit and this unit should not need it, although the headroom is appreciated. FDB fans are a reasonable choice at a premium tier, balancing acoustic performance with long-term bearing reliability.

As noted, well, everywhere around the packaging and promotional material of this unit, the OEM is Seasonic. Founded in Taiwan in 1975, Seasonic has a vast reputation in the PC market. The company serves simultaneously as a retail brand and as the manufacturer behind several third-party labels, with past collaborators including Antec, NZXT, and XFX. The Vertex platform, launched in late 2022 as Seasonic's response to the ATX 3.0 and PCIe 5.0 standards, underpins the entire Titan PLA line. The Montech collaboration reportedly required 18 months of development before reaching production, and the unit was first revealed publicly at Computex 2024.

The filtering stage comprises four Y capacitors, two X capacitors, and two filtering inductors, which is a complete and adequate configuration. Two rectifying bridges sit on a dedicated heatsink immediately after the filtration stage.

The APFC stage uses two Infineon 6R190P6 MOSFETs and a CREE diode mounted on the unit's largest heatsink, with passive components consisting of a large encased inductor and two Nippon Chemi-Con 330 μF bulk capacitors. The primary inversion stage is where this design distinguishes itself most clearly from the competition, featuring four Infineon 6R230P6 MOSFETs arranged in a full-bridge LLC converter topology. Full-bridge LLC is a configuration far more commonly reserved for units above 1000W.

On the secondary side, four Nexperia PSMN1R4-40YLD MOSFETs generate the 12V rail via synchronous rectification. The tall secondary-side heatsinks are not in direct thermal contact with the MOSFETs, an unusual arrangement for top-mounted chips, though the thermal results indicate it presents no operational limitation. The 3.3V and 5V rails are derived from DC-to-DC converters on a vertical daughterboard, standard practice in any design of this caliber.

Secondary-side capacitors are almost entirely Nippon Chemi-Con, with a small number of Rubycon electrolytics. Both are Japanese manufacturers with long, consistent track records in the PC enthusiast community for durability and electrical stability. The component selection throughout reflects engineering priorities, not cost reduction.

Cold Test Results (25°C Ambient)

Cold Test Results (25°C Ambient)

For the testing of PSUs, we are using high-precision electronic loads with a maximum power draw of 2700 Watts, a Rigol DS5042M 40 MHz oscilloscope, an Extech 380803 power analyzer, two high-precision UNI-T UT-325 digital thermometers, an Extech HD600 SPL meter, a self-designed hotbox, and various other bits and parts.

At 115 VAC, the Titan PLA 750W averaged 91.1% efficiency across the nominal load range. At 230 VAC, that figure climbed to 92.6%. Both results comfortably clear the Platinum certification thresholds under both Cybenetics and CLEAResult's 80Plus methodology. Efficiency stability is a particular strength here, as the curve holds level across most of the range, peaks near 50% load, and performs well even at low loads.

Fan behavior under cold conditions is excellent. The hybrid mode keeps the fan stationary at low loads and engages the fan once load surpasses roughly 30%, which is earlier than several competing designs allow. Audibility, however, remains minimal across most of the load range. There is a brief speed step approaching maximum output, but the fan does not approach its 2300 RPM ceiling under cold conditions. Internal temperatures are well controlled throughout.

Hot Test Results (~45°C Ambient)

Hot Test Results (~45°C Ambient)

Under elevated ambient temperatures, average efficiency at 115 VAC falls to 89.8%, and 91.3% at 230 VAC. The degradation of more than 1% on average is measurable and slightly above the typical expectation, but no signs of thermal instability or output stress appeared at any load point during sustained testing. The platform handles thermal stress composedly.

Fan behavior expectedly shifts under heat but not as intensely as we are used to. Engagement drops to around 20% load, and the speed ramp is steeper than under cold conditions. The more noteworthy finding is that the fan barely exceeds the speeds it reached during cold testing, even at 100% output. That speaks directly to how well the thermal management architecture performs paired with a relatively stable efficiency design. Internal operating temperatures remain genuinely low across the entire load range.

PSU Quality and Bottom Line

Power Supply Quality

The electrical results are exceptional, with the unit delivering spectacular power quality. Voltage regulation sits at 0.5% on the 12V rail and 0.2% on both the 5V and 3.3V rails. Those figures are outstanding by any standard. Ripple suppression is equally clean, just 26 mV maximum on the 12V rail and 20 mV on both the 5V and 3.3V rails. These are numbers we infrequently get from top-tier products.

During our thorough assessment, we evaluate the essential protection features of every power supply unit we review, including Over Current Protection (OCP), Over Voltage Protection (OVP), Over Power Protection (OPP), and Short Circuit Protection (SCP).

All protection mechanisms, including OCP, OVP, OPP, and SCP, were verified and triggered correctly during testing. The OCP thresholds, however, are set very high: 158% on the 3.3V rail, 160% on the 5V rail, and 146% on the 12V rail. OPP triggered at 146% of rated output during hot testing. The unit sustained power delivery well beyond its 750W nameplate rating for significant time periods before shutting down. Rather than this being an issue and considering the platform, parts, and their ratings, it would seem that this unit deliberately carries substantially more headroom than the nameplate implies. The protection circuitry functions correctly - the thresholds simply reflect what this hardware is actually capable of.

Bottom Line

At the $160 MSRP, the Titan PLA 750W sits at the upper boundary of what the 750W segment will typically support. The competition at that price is real. At the $130 sale price it regularly appears at, the value calculation changes considerably. The electrical performance, component selection, and Seasonic engineering together produce a unit that easily competes technically against products priced $30 to $50 higher from comparable brands.

Taken as a whole, the performance profile of the Titan PLA 750W is difficult to fault. Voltage regulation figures comfortably outclass the majority of the competition at any wattage. Ripple suppression stays at or below 26 mV across all three rails under any load condition tested, including sustained full-load runs under elevated ambient temperatures. The efficiency figures meet the Platinum certification threshold with margin to spare and remain stable across the entire load range rather than peaking sharply at optimal points. Hot testing introduces a degradation which is measurable but entirely manageable, and critically, the unit shows no signs of thermal stress or output instability at any point during sustained high-load operation. The fan management is competent and audibility remains low for the overwhelming majority of real-world operating conditions. These are benchmark-level results and reflect the underlying platform more than any cosmetic or marketing consideration.

The advanced platform design and exceptional quality silicon used by Montech and Seasonic are what stands out about this series. The nearly exclusive use of Nippon Chemi-Con and Rubycon capacitors throughout the secondary stage reinforces that this product was not engineered down to a cost target.

Two 6+2 pin PCI Express connectors cover the most common use cases at this wattage, but the count could become a constraint for some builders. It is the most concrete criticism that can be leveled at an otherwise well-considered overall design. The 12V-2x6 connector is rated at 600W, the white cable aesthetic is genuinely distinctive and well executed, and the chassis finish is among the cleanest examples seen at this price point. The chrome accents could register as excessive depending on personal preference, but the underlying build quality is not in question.

The 10-year warranty is among the longest available in the consumer PSU market and reflects the confidence a Seasonic top-tier platform warrants. For a unit that will typically remain in service for about five years before the next significant system build, that commitment is not a marketing footnote but a meaningful feature that factors directly into total cost of ownership. Warranty length in the PSU segment correlates more reliably with platform quality than almost any other single specification, and a decade of coverage communicates something concrete about how Seasonic and Montech expect this hardware to age. The Titan PLA 750W is a product built by people who knew exactly which OEM to call, and the results confirm that the decision was correct.

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When Arm introduced its AGI data center CPU, which it will ship starting in late 2026, the company revealed key specifications but omitted many technical details. It said nothing about the processor's performance at the time. This week at Hot Chips 2026, Arm filled many gaps about the architecture and design decisions of its AGI CPU, disclosed that the processor works as planned, published planned configurations, and said it is on track for commercial shipments in the coming months.

Many cores

Arm's AGI is a dual-chiplet data center processor that packs 64, 128, or 136 Neoverse V3 cores (10-wide frontend and decode, 10-wide dispatch, 8-wide retire, 384+ entry OoO window) running at 2.80 GHz – 3.70 GHz. The processor is equipped with two 128-bit vector engines and 2MB of L2 cache per core, as well as up to 272 MB of system-level cache. Each CSS V3 chiplet consists of 50 billion transistors, contains 70 V3 cores, a six-channel memory subsystem supporting up to 3 TB of DDR5-8800 memory (6 TB per socket), and connects to its sibling using a 16 ×16 UCIe macros running at 32 GT/s with an aggregated bandwidth of 2 TB/s. On the I/O side of things, Arm's AGI has 96 PCIe 6.0 lanes utilizing the CXL 3.0 protocol on top for memory expansion, four PCIe 4.0 lanes, and I3C, I2C, and SPI interfaces. The CPU has a thermal design power of 300W.

At a high level, Arm's AGI does not look too different from CPUs from AMD, Intel, and Nvidia: it has many cores, plenty of cache, a high-performance memory subsystem, and dozens of PCIe lanes with CXL. However, several design choices from Arm buck some usual trends from other CPU makers.

Unorthodox design choices

The first thing that catches the eye is that Arm chose two largely self-contained SoC chiplets made on TSMC's N3P technology, which places both compute and I/O on the same die, and decided not to go with the usual heterogeneous multi-chiplet designs used by AMD, Intel, and now Nvidia, all of whom separate compute and I/O chiplets.

While AMD, Intel, and Nvidia use their heterogeneous multi-chiplet approach to pack more compute capability and deliver more performance, it looks like Arm's decision is fundamental to its combination of enormous memory bandwidth (844.8 GB/s when used with DDR5-8800, though such memory still has to make it to the market) and <100-ns DRAM latency. As AGI's memory traffic does not have to travel to another chiplet with a memory controller, it can reduce latency and potentially achieve higher performance in latency-sensitive workloads, including some single-threaded and agentic AI workloads.

Each chiplet uses an 8 × 9 CMN-S3 mesh (a low-latency interconnect) to connect CPU cores, memory, I/O, and accelerators. It incorporates a 128 MB distributed system-level cache, snoop filtering, and hierarchical caching through HN-S, or Super Home Node, a piece of logic that acts as a distribution center for handling traffic and data through the chip to speed up communication.

The important point is that CMN-S3 is not just an internal CPU mesh, as Arm designed the coherent system to extend outside of the die to extend coherency beyond the die and the socket. The approach is conceptually closer to Intel's distributed Xeon 2D mesh (though Xeon is moving on to a 3D mesh with Diamond Rapids) than AMD's EPYC architecture, where compute chiplets connect to a central I/O die that hosts the memory controllers and Infinity Fabric infrastructure. This essentially proves that Arm appears to have optimized AGI's chiplets for memory locality, bandwidth, and latency, but not exactly for compute performance density, modularity, yield, and ease of manufacturing like AMD.

Arm revealed at Hot Chips that each chiplet physically contains 70 Neoverse V3 cores, but the complete product exposes up to 136 cores, which means that four cores are redundant and are incorporated to increase yield.

Capable memory subsystem

Arm positions its AGI CPU primarily for AI servers and agentic AI systems, in particular. Since memory performance plays a big role in many agentic AI workloads, Arm implemented a capable coherent NUMA memory subsystem. The NUMA subsystem features two six-channel DDR5 subsystems located in each chiplet, which can potentially provide a total of up to 845 GB/s of bandwidth. If a core needs memory attached to the other chiplet, the request can cross the coherent die-to-die connection, though at a cost of latency. Arm's goal is to provide as much bandwidth per core as possible, which is why AGI supports everything up to DDR5-8800.

The DDR5 controllers within Arm's AGI CPU are quite sophisticated too. They support numerous features to maximize performance in real-world workloads, including fully out-of-order command scheduling, bank-parallelism-optimized address mapping, and programmable page policies to improve DRAM utilization and extract more effective bandwidth from the memory subsystem, while anti-starvation mechanisms help maintain predictable service under heavy load.

In addition, Arm also implements memory-bandwidth limiting and monitoring through Memory Partitioning and Monitoring (MPAM) along with QoS-based traffic prioritization and congestion feedback to manage contention when multiple cores and I/O devices compete for DRAM bandwidth. The memory subsystem also features extensive RAS capabilities, including single-DRAM-device failure correction with Chipkill-class protection, memory scrubbing, row-hammer mitigation, repair support, error injection, and RAS error logging.

Capable memory subsystem

Now that Arm has shared so many details about its AGI CPU, the lingering question is the performance of the processor itself. Arm still has not published conventional benchmark results such as SPEC CPU2017, SPECrate, integer/floating-point throughput, or direct socket-to-socket comparisons against current AMD EPYC or Intel Xeon processors in real-world server workloads.

The main performance claim that Arm has made is '2X performance per rack versus the latest x86 platforms' based on estimates, which is not even remotely a detailed performance claim. Perhaps, following Nvidia's lead, Arm prefers to compare the per-rack performance of its CPUs, as they are made to work in racks. However, this is clearly an unconventional way to evaluate processors.

The Dr. Semiconductor YouTube channel only has a few videos, but the man is quickly making a name for himself. He's built a semiconductor cleanroom in his backyard and made his own RAM cells there. Matthew Hartensveld (his real name) needs to figure out a way to package (cut and write) the RAM cells, so he figure he might as well use the opportunity to make some free-range, homemade LEDs while he was at it. This latest adventure is part of his open-source Semiconductor.DIY project.

The entire video is fascinating and information-dense, so we suggest you watch it to get the complete picture. Hartensveld started off with gallium nitride atop a sapphire wafer, that he proceeded to break into shards and clean with a solution including deionized water, acetone, and isopropyl. The primary goal was to etch the top layer of the material in order to define pattern contacts. Chlorine gas would be the obvious answer as that's what's used industrially, but it'd be more than a little hazardous in this setting.

The scientist turned his attention to a household item that makes every nerd happy: lasers. He ordered a powerful one from eBay, and moved it into his bathroom so his dog wouldn't look into it. Hartensveld quickly came across the problem that there was basically no information online about using said laser, and after a few attempts he managed to etch some scribbles on the wafer, and then actual patterns. After this, he test-drove the "LED" shard by smooshing some indium on it and using a 9 V battery, and lo and behold, there was blue light.

Doing so left a deposit of metallic gallium atop the pattern, so he came up with a solution to clean that up to leave the etched parts clearly exposed. Using a vacuum chamber with a two-stage pump that sucked out 99.999999% of the air out, he deposited a nickel/silver layer for the positive contact, then a titanium/silver for the N-contact and top metal, using argon plasma as the top gas. Even still, he later regretted not letting the chamber run for a while longer to draw out yet more air, to reduce residual gas.

At this point, Hartensveld had a fully working blue LED, though still in a wafer. That's right, blue, the color of LED that was a mystery for decades until a dedicated Japanese engineer figured it out. At any rate, the time came to package these LEDs, meaning cut them out of the wafer, and add physical connections. A table saw with very thin blades didn't really worked out, so in pure scientist fashion, Hartensveld turned once again to the laser, focusing it at a specific point to melt away the sapphire substrate. After an adjustment to make this a multi-passs process, he finally had a full cut.

The madlad still needed to add wire connectors, and after evaluating a few options, settled on indium bumps, the same method and material used in digital cameras. Last but not least, since he already had a blue LED on hand, he added a layer of cerium-doped yttrium aluminum garnet (just bought off Alibaba) to add a yellow filter to end up with an ugly but functional white LED.

Buying a pre-built gaming PC remains one of the surest ways to skirt exorbitant RAM and SSD pricing, as AI drives up the cost of DIY components. Right now at Best Buy, you can get an HP Omen 35L Gaming Desktop with an RTX 5080, a graphics card selling for $1,500 right now, 32GB of DDR5 RAM, 1TB SSD, and an Intel Core i7 CPU for just $2,349, a $750 saving.

Get this deal at Best Buy

Thanks to a slightly older but no less capable processor, this is a bit of a sleeper deal that is really good value in the current market. As noted, the GPU alone now costs around $1,500 to buy. 32GB of DDR5 RAM is $400, so you've almost spent $1,900 on just two components, before you factor in the SSD, CPU, motherboard, cooler, PSU, and case.

As you can see, this PC doesn't contain a benchmark-busting X3D CPU from AMD; however, the Core i7-14700F is a capable 20-core Raptor Lake Refresh. It features eight performance cores and 12 efficiency cores, with boost speeds of up to 5.4GHz. Despite being a couple of years old, it still averages 150 frames per second in our 16-game testing suite at 1080p, and, thanks to its price, it offers the best value (FPS/$) of any CPU we've tested.

The RTX 5080 is the headline component here, as the second-best graphics card on the market, only behind the RTX 5090, which will cost you around $4,800 these days.

With 10,752 CUDA cores and 16GB of VRAM, this will chew through any title, even providing a solid base for 4K gaming, not to mention access to Nvidia's DLSS technology for even more frames.

The RTX 5080 makes the raw power of this gaming PC extremely appealing, and more than makes up for a slightly older CPU, a compromise worth taking in the current economy.

Other specs include a 1TB PCIe 4.0 SSD, 32GB of DDR5 6000 RAM, Wi-Fi 6E, Bluetooth, an 850W PSU, and liquid cooling.

If you fancy a cheeky little PC upgrade that doesn't involve raiding your savings for memory or storage, then how about sprucing up your peripherals? Your mouse and keyboard get the most use and abuse on the daily, and although a good clean and maintenance can extend their life by years, sometimes they just get a little too grotty and need to be swapped out. Keyboards, however, can command a good chunk of cash these days too, so finding a great deal like today's really helps you land a winning upgrade. At Amazon, you can clip a coupon and add the code Q2BJETPO at checkout to pick up the Hexgears low-profile Immersion A3 TKL gaming keyboard for just $42.20. That's a saving of $30.59 on the current list price, and a superb value considering this keeb launched at $159 in 2024.

● Check out this deal at Amazon

The Hexgears Immersion A3 features plenty of bright, gamer-ready RGB lighting with onboard profiles and more available via the Hexgears software. The keyboard also features a small TFT screen that gives you current keyboard info, such as a Caps Lock indicator and battery life updates. You can also upload customizable GIFs to personalize your peripheral further.

The Immersion A3 TKL is very thin, and it doesn’t take up much desk space, thanks to its compact TKL layout. There's a choice of switch types, with all versions available for the same price. Choose between Kailh White Rain (silent, linear), Kailh Black Cloud (tactile), or Kailh Hide Mountain (clicky). All of these switches are low-profile and come pre-lubed, with a total travel distance of 2.8mm. The Immersion A3's PCB is hot-swappable, with other 3-pin switches, so you can change up your low-profile switches in the future if you have a preference or fancy something different.

A wireless, low-profile keyboard for just $42.20 is an absolute bargain, especially with so many features. Its versatile connectivity allows the Immersion A3 to connect to multiple devices easily via 2.4GHz wireless, Bluetooth (up to three connections), or wired via a 5.9-foot (1.8m) USB-A to USB-C cable.

Check out our review of the Hexgears Immersion A3 TKL gaming keyboard for more information if this deal piques your interest.

Developer Xusheng Li has uncovered some previously unknown functionality deep within Microsoft’s Paint and Photos apps. The findings concern GUID watermarks. While these programs clearly add Copilot logo watermarks to images where LLM image generation has played a part in the process, Li also uncovered an invisible watermark and the process behind embedding it. To be clear, both these watermarks are merely to identify/verify imagery where AI has had a role in the creation, so GUID should not be confused with the recent controversies over Microsoft’s GDID traceable device-specific telemetry.

Li says that simple curiosity spurred him to investigate the AI features of the Paint app in Windows 11. The image generation functionality being capable of calling a remote API was expected. However, the dev got more curious when four apparent model files were found in the app path for local processing. Specifically, one of the local files was an ONNX-like model, the other 3 were encrypted ONNX-like files…

With all four files unencrypted and open to probing, investigations led to watermarker.dll being uncovered. Initially, Li thought it was just for visible watermarking tooling, leaving a Copilot logo on the bottom right of the image. It calls the AddPerceptibleWatermark function to do this. However, with curiosity stoked by the DLL file’s larger-than-expected size, the dev decided to ask AI to analyze the file. The AI found there was also a function to embed an invisible watermark.

The invisible watermarking function is called WmkWriteWatermark. Microsoft’s embedding process basically insists on this invisible watermark in its Stable Diffusion image generation output. If WmkWriteWatermark fails for any reason, the generation process will result in an error.

Coalition for Content Provenance and Authenticity (C2PA) credentials

Li provides some further technical insight into the hidden watermark. Most strikingly, he asserts that the watermarking information mixes a server-issued GUID into the pixels. That isn’t the end of the identifying data bundling. “Paint does more than alter the pixels,” notes the dev. “It also attaches C2PA Content Credentials to the saved file. The code responsible for this lives in ProvenanceHelper.dll, backed by provenancesdk.dll.”

In conclusion, even on devices where local image generation occurs, the prompt is still sent to Microsoft servers for moderation, says Li. Moreover, the dev indicates this watermarking is mandatory, and if the process fails, Paint will abort the entire image generation process. In Photos, there is the same GUID mechanism in place when AI is used, but it will still return the image and just log an error if the watermarking process has issues.

It is thought that all this hidden processing “might be related to Article 50 of the EU AI Act, whose transparency rules took effect on August 2, 2026, and require AI-generated content to carry a detectable, machine-readable mark—but not a prompt-specific GUID.”

The U.S. government is looking to move a critical transparency step in the approval of data centers behind closed doors. The Environmental Protection Agency (EPA) has plans to remove a requirement that forces states to seek public feedback on applications for air pollution permits, as reported by The New York Times. The change, if it goes into effect, would apply to a subset of air pollution permits. However, the primary concern is around data centers, as local communities seek to delay, or outright ban, AI data center construction.

The latest proposal applies to permits for a "minor source" of air pollution, which includes landfills, laundromats, steel mills, and, of course, data centers. However, recent analysis of the expansion of AI data centers suggests that environmental impact is growing. The Harvard School of Public Health estimated that the Vantage data center in Loudoun County, Virginia, contributed between $53 million and $99 million in annual health damages, the largest estimate for any single facility ever.

In another example, the analysis conducted by researcher Michael Cork directly influenced a proposal in Fluvanna County, Virginia, to expand a fossil fuel plant. The expansion was ultimately denied based on the findings from the analysis that "shaped the public discussion leading up to the... vote."

The proposal from the EPA would remove, or at least create the opportunity for states to remove, the public discussion that shapes these decisions. The EPA's stance has been that these minor sources don't require federal oversight. However, there's a clear gap when a federal agency decides to stop enforcing federal rules and asks states to step in to make their own decisions.

The Attorneys General from 14 states and three cities signed an open letter opposing the change, writing, "despite the name, even 'minor' sources can pose significant health and environmental impacts." The letter suggests that removing the required publicization of permit applications and 30-day comment period "sidelines these fundamental transparency and democratic safeguards in the name of administrative expediency and relieving regulatory burdens, particularly for accelerating the buildout of data centers and other artificial intelligence (AI) infrastructure."

Although the EPA requires a public comment period after an air pollution permit application, the process has been circumvented in some cases.

In Gilroy, California, an Amazon data center began construction last month without the knowledge of many residents. A years-long approval process between the city and Amazon meant the public comment period had lapsed two years before construction began, leaving the public little time to react. In another case, residents of Festus, Missouri sued the city, arguing that it didn't give the public enough time to review a data center proposal before moving forward with it.

With the EPA's proposed change, responsibility for regulating data centers is placed on the states. Current environmental law allows states to impose stricter environmental standards than the federal minimum, provided the state does not fall below it. The EPA change would allow a state to eliminate the public comment period, since it's not required by federal law.

Currently, a public comment period is still required. The proposed change would go into effect within the next year.

A family from Pierce County in Washington, Seattle, claims that thieves broke into their home and stole a haul of retro Nintendo hardware worth more than $50,000. Amongst other items taken, Fox13 reports the perps made off with more than 850 NES games, collected by the family over the span of some forty years.

According to the report, the thieves simply forced entry to the home by breaking in through the front door. The stricken family told the outlet that it appeared the thieves knew the layout of their home, targeting the master bathroom and making off with a tennis bracelet, amongst other things.

"They’ve been in our home. They know our home layout. They’ve been in our master bathroom. They took my wife’s tennis bracelet," Roger Holler said. The prize haul, however, seems to have been the aforementioned bundle of retro video games.

Holler told the outlet he had begun collecting Nintendo games from 1984, and had spent some 30 years "driving around on the weekends from game store to game store." Holler's son reportedly said the father and son duo would spend up to 10 hours on car journeys, collecting and cleaning the cartridges.

All in all, the collection amounted to 850 NES games, some of which are said to have been worth a small fortune. An image from the report shows five games that appear to be from the collection, likely not worth more than $25 a piece as loose cartridges. They are:

• The Legend of Kage (1987)
• Ice Climber (1985)
• Raid on Bungeling Bay (1987)
• Rygar (1987)
• Donkey Kong 3 (1986)

Depending on the condition and the game, NES titles can indeed fetch astonishing amounts of cash in today's retro market. One lucky scavenger recently discovered a collection of 97 untouched Duck Hunt and Super Mario Bros. cartridges, thought to be worth $10,000 each. In 2024, an ultra-rare NES Gold Nintendo World Championships cartridge sold for $207,400.

Given the stated value of the stolen collection, Holler told the outlet that it "was going to be my grandkids' inheritance or college tuition." Beyond the financial value, Holler lamented the loss of decades of family history.

A fake Israeli think tank credited migration-policy research to an Australian professor of food science, ran a "sovereignty index" that scored Russia above the countries criticizing its war, and filled its site with academic papers lifted from their real authors. OpenAI banned the cluster of ChatGPT accounts behind it on August 25, tracing the operation back to Russia, where operators used VPNs to reach models blocked in the country and drafted mostly English-language posts promoting the outlet across Substack, Telegram, X, Facebook, and LinkedIn. Of 36 articles OpenAI sampled from the International Burke Institute, as the outlet called itself, 34 were copied from real academics, some misattributed to figures including Francis Fukuyama and Noam Chomsky.

OpenAI says that the operators used ChatGPT to write the posts and replies that directed audiences towards the IBI website, which was registered in February last year, while the articles, the country reports, and the index itself weren't produced with OpenAI's models.

Instead of using ChatGPT to write the articles, the operators used the chatbot to strip any wording that might mark them as Russian speakers, yet the non-AI material carried obvious tells. One IBI article referred to Germany's traffic-light coalition as "the Svetofor coalition," a literal rendering of the Russian word for traffic light that no English or German writer would use. A profile that OpenAI tied to the operation described a U.S.-focused Telegram channel as offering "a totally unhackneyed perspective."

This is OpenAI's sixth public threat report since it began making disclosures in early 2024. Since then, its Intelligence & Investigations team says it has disrupted more than 40 malicious networks. Similar findings recur across all of them, with operators bolting AI onto existing workflows for efficiency and distribution, not content generation.

OpenAI placed this campaign at the lower end of Category Three on the Brookings Breakout Scale — a six-category model that measures the real-time impact and spread of disinformation campaigns — with individual posts drawing low view counts and the operation's Telegram channels running between 10,000 and 20,000 followers each.

OpenAI ran the same enforcement against China-linked clusters in June, when it banned accounts behind the "Data Center Bandwagon" campaign that amplified U.S. data center electricity backlash. The ban lands on operators who were already reaching OpenAI's models via VPN, since Russia is blocked from its services, for a campaign that barely reached anyone.

"The significance of the operation lies less in the audience it reached, however, than in the infrastructure it had built," said OpenAI. Anthropic's own AI-capability disclosures drew scrutiny in April, when its claim that Claude Mythos found thousands of severe zero-days proved to rest on 198 manual reviews.

About a month ago, techie Redditor Alternative-Panic69 (Panic) had the idea of acquiring a Radeon 7900 XT with 20 GB of VRAM for LLM use, a significant undertaking in their home country of India. They found one for $550, an excellent deal even by Western standards, and they were off to the races with Qwen and GLM in tow. Panic intended to use this card in a Lenovo M910Q desktop, but the machine wouldn't cooperate, so recently they turned to the next logical thing: a Lenovo Yoga laptop.

As Panic themselves put it, they took the "completely sane decision to perform surgery on [their] laptop." Armed with an ADT-Link PCIe external cable connector wired to the laptop's M.2 slot, a DeepCool PL750D power supply, and some quick bottom-panel removal, they managed to wire everything together in a manner that mostly worked.

With the M.2 slot now unavailable, the first challenge was having somewhere to boot from, a task accomplished by a USB SSD. Getting a picture with the main display hooked into the 7900 XT "worked beautifully", with Furmark doing 500 FPS at 1080p. This arrangement had to go, though, as the display framebuffer(s) were eating into precious VRAM necessary for the models, so the integrated graphics silicon was back to its original assignment.

Panic mused at how "[their] laptop was sitting there looking like it had been converted into a PCIe development board," but ended up having some harsh realizations about trying to wrangle fairly large LLMs (Qwen 35B A3B, GLM-4.7 Flash) exclusively on the GPU, all with a 128K-token context window.

Another Redditor pointed out that was a classical "pick two out of three" situation, and indeed that was the case, as Panic found that ultimately his laptop's memory (or lack thereof) proved to be a serious bottleneck. The large context window needed lots of associated data in RAM, and eventually the available DRAM was exhausted, and the laptop began using swap space, grinding performance to a halt, to the point where the GPU was "being idle, occasionally in bursts."

The story ends in somewhat predictable fashion: as interesting and visually striking this project was, they needed their laptop as an actual mobile device again, so they elected to buy the cheapest AM4 machine they could find to fit the Radeon 7900 XT and the power supply in. As Panic themselves said, "buying an old office tower would have been the sensible solution. I wasn't here to be sensible."

A special edition of the Commodore 64 Ultimate made in collaboration with CD Projekt Red has been announced today. The new Commodore 77 takes its stylish inspiration from Cyberpunk 2077, of course, and is skillfully melded into the game's universe. This isn't just a paint job. Commodore has packed in a faster FPGA, for one thing. It also comes with an exclusive 2MB demo cartridge, a themed Commodore 77 BASIC 2.0 boot screen, and several further flourishes of sheer finesse.

• Check out the new Commodore 77 at Commodore.net

Commodore says that the C77 is the first franchise hardware partnership in the firm's 68-year history. "I have been a fan of the game since its release, and it was rewarding to learn in my discussions with their Joint CEO, Michał Nowakowski, that he was a lifelong Commodore 64 fan in turn," said Peri Fractic, President, CEO, and Chief Product Officer, Commodore, discussing this collaborative product. "For us both, this has been a collaboration where art imitates life, and in turn, life for the denizens of Night City now imitates that art. We hope they enjoy their new terminal."

There's plenty to be said about the artistic style of the new C77, but we'll let the video and images do the talking. It might be worth pointing out some of the finer points, though. Like, yes, that is a brushed metal plate to the right of the keyboard deck, which is distinctly different from the original beige bread bin.

Turning on the C77, there are new experiences to be had. In addition to the custom Cyberpunk 2077-themed Commodore 77 BASIC 2.0 boot screen, there are new menu colors, fitting in with the game universe. Moreover, there's a 2MB Cyberpunk 2077-inspired demo cartridge in the (themed) packaging. You can plug in this cart to enjoy an exclusive, distinctly Cyberpunk demo created by the long-running Polish C64 demo group Arise, with an original soundtrack by chiptune legend LukHash. The audioscape makes use of the C64U's 24 channels of stereo SID chip music, while demo viewers can watch the LEDs behind the Commodore 77’s smoked translucent base light up and dance in time.

Inside the new C77 you won't find the AMD Xilinx Artix‑7 XC7A50T, as used in the C64U. The system instead employs the next-tier Artix-7 XC7A100T with twice the logic, twice the block RAM, and more I/O.

It is interesting to learn about the change of processor, but we don't get any firm hints about how the extra compute resources will be used in the C77. "Word on the neon-soaked street is it will achieve speeds of 77 MHz, vs. the original Commodore 64’s 1 MHz processor speed," teases the official press release, in the only section which mentions the new FPGA. "This is achieved by utilizing the more powerful AMD Artix XC7A100T processor, compared to the 50T found in the Commodore 64 Ultimate."

The C77 is up for preorder from today, at $377. We've already mentioned, and you can see some of the obvious enhancements, but buyers will also get a "custom neon power light, stickers, pins, collectible themed packaging, a custom Cyberpunk 2077 spiral-bound user guide packed with lore, exclusive Cyberpunk 2077-themed type-in BASIC programs, and more." Product shipments will begin in "early 2027."

Incidentally, on Monday, Commodore released the Alien Breed 35th Anniversary Collection for PCs and consoles. That's six classic titles beloved of Amiga gamers, and PC users can wishlist them now on Steam.