PsiQuantum, a quantum computing company, announced in a release earlier this week that it has turned its low-loss silicon photonics into an optical switch for use in AI data centers. Every optical switch typically comes with loss of light: the more light a switch receives, the less reaches the other side, with the amount of light lost measured in decibels. Typical data center optics can tolerate half the light (3 dB) being lost. PsiQuantum's switch debuted with a demo that typically loses only 1.2 dB of signal, or about a quarter of the light in lab tests, and the all-optical switch can achieve it without an amplifier. The prototype is, however, comparatively small, with eight inputs and outputs (8x8) against competitors’ 32 to 300, but the company says larger devices are in development.

The optical circuit switch (OCS) passes light straight through, from fiber to fiber, without converting it to or from electrical signals. Regular data center switches make that conversion at every hop, which takes power and adds latency. An OCS, instead, uses a fixed light path that it maintains until a change is ordered. Its main penalty comes down to how much light is lost when passing through the switch. PsiQuantum’s switch, which uses circuits etched on a silicon chip with no moving parts, unlike Google’s and Lumentum’s tiny mirrors, or microelectromechanical systems (MEMS), would be used alongside regular packet switches.

Skipping those conversions matters more as AI clusters grow, because moving data between GPUs is quickly becoming a bottleneck. Optics are a high-bandwidth improvement over copper, which has limitations in reach, although a shortage of lasers is a problem for the alternative. Improving communication bandwidth and distance is the goal, so that different racks of GPUs can talk at speed and act as one.

Switches similar to PsiQuantum’s made by Salience Labs and iPronics use built-in amplification and, at least in the former case, are also rated at under 2 dB, or under 37% of the light lost. Google’s mirror-based switch, Palomar, can also typically stay under 2 dB and has 136 ports. Salience’s switch sits at 32 and Lumentum’s at 300, which leaves the eight-port prototype a ways behind on how many fibers it can handle.

PsiQuantum's switch is an 8x8, meaning eight in and eight out, and “every input can reach every output.” It’s also “strictly non-blocking,” so a new connection never disturbs ones already in place. In the lab, it measured a typical 1.2 dB insertion loss, 40 dB of crosstalk (about 0.01% of the light leaking to other ports), and under 3 ms of reconfiguration time, all of which exceed “initial targets set for these devices.” The loss figure is measured fiber to fiber, which includes getting light onto and off the chip but not connector losses, PsiQuantum told Tom’s Hardware Premium.

The platform loses very little light at each component, which matters because a quantum computer can tolerate only so much lost light. PsiQuantum’s 2025 Nature paper reports silicon nitride waveguides losing as little as 0.5 dB per meter (about 11% of the light per meter), splitters 0.5 millidecibels, and crossings 1.2 millidecibels, the last two a few hundredths of a percent of the light or less each.

The company also says chip-to-fiber coupling has reached about 60 millidecibels, roughly 1.4% of the light lost, in its best published lab results, although without a full fiber array attached. Those figures set a best case, not a worst case, for a 64-port switch, since light crosses more components as ports are added and the losses add up.

How much loss is acceptable varies with the optics at the end of each link. Coherent, an optics maker, says that using an OCS can add “up to 3 dB additional insertion loss,” but its transceivers are able to work with that. Lumentum told Gazettabyte that 3 dB is good enough to work with off-the-shelf FR4, DR4, and DR8 optical modules using the switch; those are the standard plug-in options used to link GPU servers and switches, at distances of 500 meters to 2 kilometers. PsiQuantum’s 8x8 is built for the O-band, the wavelength range those modules use. PsiQuantum told Semafor that, to be workable, its switch needed insertion loss under 2 to 3 dB.

The 8x8 prototype was designed for conventional data center traffic and comes in well below the 3 dB loss target in a request from Nvidia for proposals, the company says. The port count marks it as a demonstration vehicle, but PsiQuantum has a 64x64 version taped out with a 72x72 underway and is “actively developing larger devices.” With crossings that lose so little light, the scale-up might hold. PsiQuantum declined to share details on future products, though it says the loss, which may be slightly higher, will stay very low.

Speed remains less important than insertion loss for how OCSes are currently used. Google, which has the most deployments, numbering in the “tens of thousands,” uses its switches to resize TPU pods or to route around failed chips, a job its “millisecond-scale” MEMS switches handle. PsiQuantum’s under 3 ms is within that class, but Salience and iPronics claim less than 300 microseconds for their amplified chip switches, which iPronics highlights for mid-workload network rewiring to hide the switch time while chips compute.

There is quite a lot of room for speed to improve, and PsiQuantum already has a material for that, used in an existing 64-port switch for its quantum systems, which need to switch light at gigahertz rates. The material, barium titanate (BTO), changes how it bends light when a voltage is applied, and the company chose it over lithium niobate, a more common selection, because BTO had the strongest effect it could find. This keeps switching elements short and losses low at speeds roughly a million times faster than the demo. BTO is “not required for the demonstration vehicle or initial products,” which use a thermally switched silicon nitride platform, but it is “an additional path to faster, more advanced future products.”

An argument for starting now is that building thousands of such switches for conventional applications would reduce the risk for the packaging, assembly, and supply chain of parts PsiQuantum builds for quantum computing. AI and quantum computing are “complementary technologies,” it says, and this is the chance to put its technology into action. When PsiQuantum hired Rob Soderbery as an executive vice president in July, it said he would advance its silicon photonics platform, “accelerating its application across AI networking and other next-generation computing infrastructure,” hinting at the roadmap. The company’s CHIPS award was finalized in September for “high-performance optical switches,” among other components.

Co-founder Pete Shadbolt praised Google’s alternative MEMS switch but would prefer to have similar-quality switching on a monolithic chip that needs no moving parts. Nvidia has a stake through its venture arm, NVentures, and collaborates with PsiQuantum on “GPU-QPU integration,” that is, graphics and quantum processing units, as well as silicon photonics. Nvidia also joined rival iPronics’ $125 million round in September to develop its second-generation optical switch.

Right now, inside Google, OCSes rewire AI clusters, and research firm Cignal AI expects most deployments to stay there through the end of the decade; outside of Google, OCS is still at “proof-of-concept and early trial.” The firm raised its market estimates earlier this year, though, roughly threefold for 2026 and by more than 40% for 2029. An opening exists for PsiQuantum in GPU networks, which ties into Nvidia’s interest, though adoption there is expected to be limited until then. The company says samples and testing kits have already gone to several computing companies, including GPU vendors and hyperscalers.

PsiQuantum should have commercial products “announced in the coming months,” along with updates on larger devices, while competitor Salience has already demonstrated 64 ports at this year’s European Conference on Optical Communication. Salience’s roadmap already has 128- and 256-port products. Scaling is always an issue, but if PsiQuantum’s figures, which haven’t been independently verified, hold up at 64 ports without amplifiers, then larger monolithic OCSes are on the way.

You’ve probably heard TCL’s name associated with jumbo TVs sold in big-box stores, but it also makes a line of gaming monitors that offer excellent performance for the money. It’s also one of the few companies putting effort into Mini LED panels where others have gone all-in with OLED. Mini LED delivers the brightest imagery you can buy among the best gaming monitors, with HDR peaks of 2,000 nits or more.

TCL’s 27C2A Pro is such a panel, with an honest 2,000 nits from a 3% window in HDR mode and 650 nits full field for SDR. It also sports a full-array local dimming (FALD) backlight with 2,304 zones, which delivers tremendous contrast. A Quantum Dot layer pushes the color gamut past 100% of DCI-P3. And a set of accurate picture modes promise precise color rendering. It’s speedy too, with 160 Hz in 4K and 320 Hz in FHD resolution, with available overdrive and MPRT backlight strobing. This fully loaded package can be yours for $600 as I write this. Let’s take a look.

TCL 27C2A Pro Specs

The 27C2A Pro’s core tech is an IPS variant TCL calls HFS Shoot. It combines an ultra-bright FALD backlight with Quantum Dot color and very quick response. Native resolution is 4K 3840x2160 pixels for a density of 163ppi. This is about as fine as it gets unless you move up to 5K or more. And those panels are still quite rare and expensive.

Brightness is honestly reported here, with my tests matching TCL’s claims. I saw almost 650 nits from a full white field pattern in SDR mode with the dimming turned off. HDR ran that up to almost 1,700 nits when I measured a 25% window with dimming on High. At that setting, I couldn’t measure the black levels at all. With 2,304 dimming zones, you won’t see any halo artifacts like panels with just a few hundred zones.

The Quantum Dot layer delivers just over 100% of the DCI-P3 gamut. You also get accurate picture modes with presets for DCI-P3, sRGB, Rec.709, and Adobe RGB, which is something rarely found outside the professional realm. Accuracy is superb with little to no need for calibration. I found similarly good performance in HDR mode, where you can adjust grayscale tracking and brightness if you wish.

The 27C2A Pro offers premium gaming too, with 160 Hz in 4K and 320 Hz in FHD resolution with a dual-mode switch. And there’s precise overdrive, Adaptive-Sync, and MPRT backlight strobing. This is one of the few monitors that lets you use all its video processing options at the same time. You can have MPRT, Adaptive-Sync, and overdrive all active at once.

Styling is outside the norm as well, with a white aesthetic for the stand and panel’s backside. LED lighting shines in the back with two large bracket shapes that show an array of colors and effects. There are gaming aids like reticles, timers, and a frame rate indicator. And you get a full complement of I/O ports with HDMI 2.1, DisplayPort 1.4, and a USB-C, plus USB for your peripherals.

Assembly and Accessories

The 27C2A Pro ships in blocks of crumbly foam in its three main parts: base, upright, and panel. They assemble without tools. Under the snap-on fulcrum is a 100mm VESA mount with fasteners included if you'd rather use an arm, and you might want to after you read the next section of this review. The power supply is an external brick, and you get cables for DisplayPort, HDMI, USB-A/B, and USB-C.

Product 360

I said the 27C2A Pro is a white monitor, but from the front, everything is black except the base. This is good for distraction-free use, especially in low-light situations where you want to stay focused on the image. There’s plenty of white plastic in the back, though, on the stand and on the panel.

The back features ventilation across the top and two large LED banks shaped like reversed parentheses. The shapes are angular with soft corners and no sharp edges. The screen has a flush bezel 9mm thick on the top and sides with a 15mm strip across the bottom. All you see there is a tiny TCL logo. The control joystick is underneath the center and is backlit.

The stand offers full ergonomics with 5/15 degrees tilt, 20 degrees swivel, and a 90-degree portrait mode. The height range is 4.8 inches. This was my only complaint about the 27C2A Pro. Though the base and upright are solid together, the panel’s fulcrum point is very small and wobbly, which makes positioning it difficult. If you game intensely and have your keyboard on the desk, it will jiggle a bit.

Up and under the center is a well-stocked I/O panel with one DisplayPort 1.4, two HDMI 2.1, and a USB-C with 90 watts of power. You also get one upstream and two downstream USB 3.0 ports. There is a pair of five-watt internal speakers, and you get a 3.5mm headphone jack.

OSD Features

In 4K resolution, the 27C2A Pro’s OSD is very small. It was hard to read the tiny text unless I got up close. The FHD version is larger, of course. Press the joystick and click left to summon it.

Player mode refers to the nine picture modes you see in the first photo above. All you need is the default one, Standard, and I’ll tell you why in a moment.

Moving on to Game Settings, the 27C2A Pro has every video processing option currently available for an LCD panel. The overdrive has three speeds, with fast being the best. MPRT-Plus (backlight strobe) has two levels, which correspond to pulse width. The higher one cuts brightness by around 50%, but you can easily compensate with the brightness slider because there’s more than enough luminance headroom. You can use both with Adaptive-Sync if you wish, which is something very few monitors can do. I found the best motion quality with overdrive on Fast and Adaptive-Sync engaged. MPRT reduces motion blur to almost nothing but introduces a slight phasing artifact, so I didn’t use it for my gaming tests. Also in this menu are a set of aiming points, timers, and controls for the LED lighting.

The Display menu starts with Local Dimming and its three levels. Standard barely does anything, only increasing contrast to around 1,800:1. Medium and High are more aggressive, and they shut off the backlight completely at light level zero for theoretically infinite contrast. Dimming is available for both SDR and HDR content.

In the Color menu, you’ll find color temp presets and a gamut selector. It offers sRGB, Rec.709, DCI-P3, and Adobe RGB. All are very accurate, more so than the Original option, in fact. You’ll see how this works in my tests on page four. The 27C2A Pro is a very accurate monitor when set up correctly.

The remainder of the OSD is focused on conveniences like PIP/PBP, KVM options, OSD controls, and the like. If you want to reset everything to factory defaults, that option is in the Settings menu.

TCL 27C2A Pro Calibration Settings

I was initially disappointed with the 27C2A Pro during my first round of tests. It comes set to Standard mode, Original color space, warm color temp, and gamma 1. Gamma and grayscale measured well, but color tracking was a bit wonky. Luckily, you can select any of the available gamuts, sRGB, Rec.709, DCI-P3, or Adobe RGB, and get much better accuracy with near-perfect gamma and grayscale. I made small tweaks to the RGB sliders, and that was it. My settings are below.

In HDR mode, you get two presets, Display and Cinema. Their color is the same, but the EOTF curves are slightly different. My pick was Display, and with that, you get RGB sliders and a brightness control. You can also change the local dimming, though I recommend High for the deepest blacks and best contrast.

Gaming and Hands-on

Gaming on the 27C2A Pro was a treat. I’m spoiled by premium OLEDs, but TCL has found a great combination of brightness, color, speed, and smoothness that mostly replicates that experience with an LCD panel. The only thing you won’t get is those infinite blacks, but the 27C2A Pro comes close with its 2,304 dimming zones and extremely bright highlights.

You’ll see later in the test notes just how bright it can get, and that capability is used to full effect. I always set up review displays to 200 nits, and that was only a third of the 27C2A Pro’s SDR brightness range. I found that I could use its local dimming for both SDR and HDR content, which was nice. Often, there is only dimming for HDR.

Color was also a strength, with vivid hues and excellent accuracy. I found some unusual behavior during initial tests, but once I discovered the secret, it was pro-level color all the way. Just pick the gamut you want and avoid the Original option in the OSD’s color space menu.

My GeForce RTX 4090-equipped PC had no problem maintaining 160fps in 4K. I tried the FHD/320 Hz option and found that it worked fine and delivered that frame rate. Since the downscale is an exact multiple, the picture wasn’t as soft as you might expect. The dual-mode feature is a viable option for users with consoles or less stout video cards.

I also noted that I could use overdrive and MPRT backlight strobing in concert with Adaptive-Sync. That’s something only a few high-end monitors offer, so it was nice to see from a $600 display. If you’re relegated to frame rates below 144 Hz, MPRT is a good option for eliminating motion blur. The overdrive on its own is also very effective at its middle setting, Fast.

Lag was low enough that I didn’t perceive any delay in control response. Backgrounds and moving objects stayed sharp, and my inputs were instantly translated to action. The 27C2A Pro has that addictive quality that all fast and smooth monitors have.

For productivity, the 27C2A Pro shines as a photo and graphics display. Its color modes include sRGB, Rec.709, DCI-P3, and Adobe RGB. That last one is a rarity outside the professional realm. They are spot-on, as you’ll see in my test results; no calibration needed. If you need a print reference monitor, this one qualifies.

I also noted that the 27C2A Pro is a complete package with LED lighting, internal speakers, USB ports with KVM, and a slick white aesthetic. My only complaint is that the stand is too wobbly. The base and upright are solid, but the panel’s attachment point has some jiggle. I would probably spend some of the money I saved over an OLED on an arm.

Takeaway: The 27C2A Pro is a complete package for gaming and productivity. It’s fast and smooth with excellent video processing and the ability to run Adaptive-Sync along with overdrive and backlight strobing. It’s colorful and sharp and has deep contrast thanks to 2,304 local dimming zones. It’s also much brighter than any OLED. It delivers tremendous bang for the buck.

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27-inch 4K LCDs like the 27C2A Pro are not as common in my database as OLEDs, so I had to go back further in time to come up with five screens. They are TCL’s 27R94, AOC’s U27G4XM, ViewSonic’s VX2730D-4K, Acer’s XB273K V5, and Titan Army’s P2712V.

Pixel Response and Input Lag

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

The P2712V and XB273K V5 weren’t tested with the Nvidia LDAT, so their results are omitted from the response chart. The remaining panels are close in performance, with the 27R94 being a bit slower than the rest. This manifests in moving test patterns where you can see more blur from the slower monitor. The 27C2A Pro is a tad quicker at 320 Hz than it is at 160 Hz, but not by a visible amount. It isn’t a particularly fast panel, but it delivers good motion resolution thanks to its precise overdrive. You can have even less blur by turning on MPRT, but that has a slight phasing artifact.

In the lag test, the 27C2A Pro was about average at 160 Hz and took the top spot at 320 Hz. In practice, you’ll barely notice the difference. The dual mode feature is best used when you can’t maintain 160fps in 4K. Either way, it’s a fast monitor that’s quick enough for gamers of all skill levels.

Test Takeaway: The 27C2A Pro is a competition-worthy display with speed comparable to other 27-inch 4K LCDs. It gains a little performance with its dual-mode switch to FHD and 320 Hz. Though it doesn’t have quicker panel response than its competitors, it has an excellent overdrive that eliminates most motion blur. And you can use the backlight strobe along with Adaptive-Sync, which is a standout feature.

Viewing Angles

The 27C2A Pro has the off-axis image quality of a premium IPS display. The horizontal color shift is slightly green in tone with no visible loss of brightness or gamma. The top view goes cool with a reduction in brightness and gamma. It is a very shareable monitor.

Screen Uniformity

To learn how we measure screen uniformity, click here.

My 27C2A Pro showed exceptional screen uniformity with an average deviation of just 3.75% from the center zone. It’s one of the best LCDs I’ve reviewed in this test. There was no bleed or glow visible in any test pattern regardless of brightness or color. It doesn’t get much better than this.

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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

The 27C2A Pro has many strengths, but peak brightness is its greatest one. It hit nearly 650 nits in my SDR test when measuring a full white field pattern with local dimming turned off. You can use dimming in SDR mode if you wish. On the High setting, a 25% window was around 1,600 nits, and black levels could not be measured. On Standard, the contrast ratio is around 1,800:1. The native contrast result of 1,204.1:1 is above the IPS average.

After Calibration to 200 nits

My calibration was a simple one. I set brightness to 200 nits, selected the DCI-P3 color space, and tweaked the RGB sliders. That gained me a few contrast points, which is awesome. This is still with local dimming turned off. In the ANSI test, I saw an even better score of 1,270.3:1. It’s rare that any monitor’s intra-image number is higher than its static one. This is exceptional performance.

Test Takeaway: The 27C2A Pro has excellent native contrast that’s broadened with local dimming, which is available for both SDR and HDR. The two top settings have black levels so low, they can’t be measured. Peak brightness is extremely high at almost 650 nits for a full-field pattern and 25% windows topping 1,500 nits. With 2,304 dimming zones from the FALD backlight, it can render images almost as deep, sharp, and textural as a premium OLED.

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The 27C2A Pro comes set to its Standard picture mode, which has both good and bad attributes. Color isn’t great out of the box, but there’s an easy fix that delivers pro-level accuracy.

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.

The 27C2A Pro’s first chart looks a little blue out of the box but has visually perfect gamma tracking. Normally, I’d say tweak the RGB sliders and call it a day. But wait, there’s more to the story.

In sRGB mode, grayscale has no visible errors and just a slight dip in its gamma tracking at the 10% brightness step. This is typical performance for the gaming monitors I’ve tested. Since the 27C2A Pro has an Adobe RGB mode, I tested it too and found excellent grayscale and gamma tracking. You won’t need this mode for gaming, but users creating content for print will appreciate its inclusion.

After calibration, grayscale is excellent, and gamma is only a tad lighter. How I achieved this is detailed in the next section. I couldn’t take the usual approach.

Comparisons

The 27C2A Pro’s grayscale starts at 3.67dE, not too bad but a little blue in tone. Calibration fixes that issue to where it’s on par with the other screens at 0.74dE. Gamma tracking is exemplary whether you calibrate or not. The range of values is super tight at 0.04 from lowest to highest, and the average deviation is 1.82% with an actual value of 2.16. It doesn’t get much better than that.

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.

The color gamut test is where the 27C2A Pro gets interesting. Out of the box, gamut tracking is wonky with obvious hue errors, undersaturation in some colors and oversaturation in others. After the solid grayscale results, I was a bit flummoxed.

Relief came when I tried the different color space options. Normally, I’d just pick sRGB as an alternative, but the 27C2A Pro also has DCI-P3 and Adobe RGB on offer. All of them let you calibrate grayscale. The only control grayed out is gamma, and that’s OK because it measured perfectly in my tests. So, to get accurate DCI-P3 color, choose the DCI-P3 gamut option and adjust the RGB sliders the way I specified earlier. You can see that the sRGB and Adobe RGB choices are very accurate as well. Only Adobe comes up a tad short in green, which is typical of LCDs with LED backlights.

Comparisons

Any monitor that can average below 1dE in the gamut test is qualified for color-critical applications. The 27C2A Pro is in that club for sure. And it beats the other excellent screens. Just be sure to use the color space option for the gamut you want and avoid the Original mode.

In the volume test, the 27C2A Pro is charted in its most accurate mode, DCI-P3, post-calibration. It’s a little less colorful in the blue part of the gamut than the AOC, which is also a Quantum Dot display. But at 101.38%, it’s still very colorful and easily beats non-QD monitors. The sRGB volume is a nice 97.02%, though it comes up a tad short in red.

Test Takeaway: The 27C2A Pro has pro-level color accuracy if you use its gamut modes. Avoid the Original option for the best gamut tracking. Grayscale and gamma are visually perfect as well. Aside from the gamut selection, calibration is unnecessary. It is unusual in that it offers a usable Adobe RGB mode, which is great when creating content for print.

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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 27C2A Pro supports HDR10 content with two similar picture modes. You can calibrate grayscale and adjust brightness. Also, the local dimming remains available so you can control contrast. The mode switch is automatic.

HDR Brightness and Contrast

The 27C2A Pro’s peak of 1,663 nits demonstrates why one buys a Mini LED monitor. No OLED can touch this number for a 25% window. TCL’s claim of 2,000 nits for a 3% window is completely valid. For my test, I left local dimming on high, which shuts off the backlight under zero signal conditions. That means black levels and contrast cannot be measured, just like an OLED. In practice, the 27C2A Pro won’t quite match an OLED, but it comes closer than any other LCD technology thanks to its 2,304 dimming zones and well-engineered dynamic contrast feature. It doesn’t get much better than this.

Grayscale, EOTF and Color

In the HDR grayscale test, I noted that the RGB sliders were adjustable, so I applied the same settings from SDR to HDR. The result was visually perfect grayscale with almost all errors below 2dE. I measured both Display and Cinema modes and found slightly different EOTF tracking between them. Display was my preference. Cinema looked a tad lighter and less textured, but the difference was small.

In the color test, the 27C2A Pro is slightly off hue in magenta, green, and yellow, but not too far. The triangle is generally oversaturated, but no more than the other HDR monitors I’ve tested. This is a typical response in my experience. The DCI-P3 gamut is fully rendered with a little bonus blue and magenta. In the BT.2020 test, the 27C2A Pro runs out of color at 88% red and 75% green but has a little more than 100% blue.

Test Takeaway: The 27C2A Pro is a stellar HDR monitor with deep contrast, accurate color, and very bright highlights. It stands out with adjustable color temperature and brightness. And you can change the local dimming as well. Most monitors have no HDR options other than picture mode. It isn’t quite as deeply textured as an OLED, but it is much brighter.

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For gaming in a small space, there’s nothing sharper than a 27-inch 4K monitor. Its 163ppi pixel density is the finest structure you can buy outside of 5K, 6K, or 8K screens, which are still in the realm of the rare and exotic. The TCL 27C2A Pro brings that sharpness along with high brightness, accurate color, and premium video processing.

Once I cracked the code of its color modes, I found extreme accuracy with extras like Adobe RGB and Rec.709. You don’t find these outside the professional realm too often. And brightness? The 27C2A Pro has lots of that. TCL claims 2,000 nits for a 3% window, and since I measured almost 1,700 from a 25% window, there is no reason for doubt. The local dimming is very effective with 2,304 zones and very deep black levels. It was nice to have for both SDR and HDR content.

Gaming had that addictive quality that every OLED delivers, but only a few LCDs do. TCL lets you run the overdrive, Adaptive-Sync, and the backlight strobe all together if you wish, which is something very few monitors allow. I enjoyed smooth motion resolution and quick control response with the 4K refresh rate maxed at 160 Hz. Users with consoles or mid-priced video cards will find flexibility in the dual-mode switch, which runs the 27C2A Pro at 320 Hz in FHD resolution.

My one complaint was about the stand. Though it is stable, the panel wobbles a bit. I would likely invest in an arm if I were going to use the 27C2A Pro every day. I appreciated the inclusion of LED lights, internal speakers, and USB ports, though. This is a fully loaded display.

If you want OLED-level gaming performance, deep contrast, and vivid color, but don’t want an OLED, the TCL 27C2A Pro is a good alternative. It’s definitely worth checking out.

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The UK’s Investigatory Powers Commissioner’s Office (IPCO) has flagged “a very significant number” of General Communications Headquarters (GCHQ) storage drives that have been missing for years and have yet to be found. According to The Register, the lost storage media have been known to GCHQ officials for years now, but they’re still unsure about the scope of the problem.

"I remain concerned that GCHQ has not yet established a sufficiently clear understanding of the scale, location, and recoverability of lost removable media that may contain IPA-related data, notwithstanding that the underlying issues were first drawn to my attention several years ago. While I recognize the technical and practical challenges involved, the passage of time without a comprehensive account heightens the importance of sustained senior focus and assured remediation. Given the potential sensitivity of the material involved, I will continue to monitor progress closely,” investigatory powers commissioner Lord Brian Leveson wrote in the report.

GCHQ acknowledged to The Register that the tracking system for its removable media had been insufficient, but it has since taken steps to prevent it happening in the future. "We recognize the importance of security of removable media in our systems and have a range of measures to mitigate the risks posed by these items. The findings in the report have been addressed comprehensively, and all necessary steps to correct this issue have been taken," a GCHQ spokesperson told the publication. It has also launched a program to help locate as many of the lost storage media as possible and will have an automated asset-tracking system up and running before 2027.

The GCHQ is the UK’s equivalent to the National Security Agency (NSA), making it the country’s primary signals intelligence and cybersecurity agency. Its primary mission is governed by the Investigatory Powers Act (IPA), which was passed in 2016 and lays out the legal framework for government surveillance, data collection, and intercepting communications. While neither agency has exactly confirmed what data the missing drives contained, it could potentially have information about GCHQ operations, administration, and other crucial data.

While the UK’s premier signals intelligence and cybersecurity agency losing thousands of removable drives may sound alarming, it’s not alone in running into blunders like this. The publication reported that Britain’s Ministry of Defence has lost 87 classified USB drives from 2003 to 2008, and that the UK police have also dealt with officers losing their assigned tech gadgets. Other governments have also made considerable tech missteps — for example, the South Korean government lost 858TB of data after a catastrophic fire destroyed a data center, and it discovered that it had no backups. Indonesia discovered the same issue when it was hit with a ransomware attack, with one government official calling the lack of backups “stupid.”

As hardware for running local AI models continues to move to the forefront, such as Nvidia's RTX Spark, a new set of acronyms, definitions, and terminology is popping up. Given the pace of releases, we've put together a list of AI's new nomenclature to familiarize you with new concepts and pieces of terminology that you'll likely see in Tom's Hardware Premium articles.

This primer should get you started on the world of AI and be handy the next time news on the topic comes around, and possibly when you're downloading some models to try out and tweak for yourself. For the context of this article, we're going to stick with notions related to text LLMs, or in common parlance, chat, programming, and most agentic bots.

Model

This is probably the easiest one: a model is the LLM itself (a Large Language Model), computed from training sets of mostly text data on the scale of terabytes to petabytes. That training information comes from the internet, books, video and audio transcripts, and other sources.

The model is roughly analogous to a set of highly advanced layers of statistical analysis of relationships between tokens — words or parts of words, computer code, punctuation, and other symbols. The tokens themselves aren't part of the model, but they are packaged along with it. An AI model generates tokens in its outputs.

Weights

Those computing layers within an AI model would be useless without corresponding weights, which represent how strong the connection is between sections of tokens, sort of like how a pathway between neurons is more traveled than others. When the model runs, these values end up establishing how tokens end up relating to each other.

Example: the weights of the connections between "na" and each next part for "naval", "nachos", or "naruto" will all be different, and the higher the weight, the more often that word appears in training data. Importantly, weights cannot change without re-training the model, or parts of it.

When you see LLM specs online, you'll see something like "4B", "8B+", "20B", and so on and so forth. That shows how many parameters, or weights, the model uses, and it's generally measured in billions for consumer-grade models, and trillions for frontier models like GPT, Claude, and Kimi.

Note that a model having more weights than another doesn't automatically mean it's smarter. Just like with CPU clock speeds, it's not just about getting a higher number — bigger is usually better, yes, but you want those weights to be meaningful for the output.

You may also have heard about "open-weight" models versus "closed" models. Those refer to models like Llama, DeepSeek, and Kimi, which are effectively free-to-use, since the entities behind them freely publish the weights online. You can tweak the weights to your heart's content, if you wish, and come up with your own spin on things, as long as you have the hardware to run it. Note that downloading a free model doesn't reveal anything about how it was trained or how the weights were calculated — think downloading a free customizable application, not open-source software.

Data types and quantization

FP16, INT8, FP4, all sound like acronyms in some military handbook, but they're simple to interpret. All programs use numbers of varying width and precision, depending on their purpose: a simple money counter in your favorite game might be fine with a 32-bit integer that can represent 0 to 2^32 (INT32), while a value held in memory for a highly precise scientific measurement may need floating-point 64-bit arithmetic (FP64).

As memory is an exceedingly finite resource, good developers try to use the smallest units they can get away with, a notion that's particularly important in the VRAM-limited world of GPUs. Predictably, these chips became particularly adept at handling lower-precision values as a tradeoff between processing speed and numerical accuracy. A particular datatype isn't better or worse, as that assessment will depend on what it's used for.

When training an AI model, the labs will want to use as high a precision as they can for the weights, but keeping that in the final version would make for a prohibitively large and slow LLM. That's where the concept of quantization comes in, which is a fancy word for "clever rounding." As one of the steps after training, several passes are made to transform highly precise weights. Each AI lab has its own secret sauce about how to do this, as it's a critical step.

Frontier models predominantly use relatively high precision for their weights (BF16), in order to maintain response quality, nuance, and reasoning ability (an analogy would be deciding how accurate you want a GPS location to be — miles or inches). Open-weight models often have several versions with different quantization levels depending on what your hardware can bear — for example, Llama 3.3 70B can be run in full BF16 or quantized INT4 flavors.

The lower the precision, the more likely it can be run on consumer hardware. AI models listed on Hugging Face may have several quantization levels to fit certain levels of hardware, trading response accuracy for size. Consumer-grade models use smaller data types so they can fit on your Mac Studio, RTX Spark, or graphics card.

Context window

"Context window" is another term you've heard a lot and probably seen firsthand, likely in Claude Code's UI as a broad indicator of how long your session can go before the bot starts acting a little dumb. It's measured in tokens, with one million being a common number for frontier models, and it's almost directly analogous to RAM in a computer.

The context window is the LLM's thinking space, and it needs to contain everything associated with your session, not just the current question. Along with all your questions and its replies, it contains system-level prompts, all files you uploaded, the content of links it looked up, plus any associated data.

Crucially, while for you all these things are distinct concepts, for the LLM, they get reduced to tokens in one large pile: the context window. This is also why separating "data" from "code" is technically impossible — the best that modern models do is an approximation of that distinction.

LLMs don't think — when answering a new question, they re-run a statistics program over all the tokens and predict the next handful, so having a larger context window is beneficial (at the cost of VRAM), hence the one-million figure that's currently popular. But even statistical algorithms have their limits.

Even if you increase the window to, say, three million tokens, you may find that your LLM's prediction starts breaking down even though it hasn't run out. This is amusingly called the "lost in the middle" effect, where the bot will pay attention to the start and end of the conversation but will forget things down the middle. Thus, each model is finely tuned to the size of its context window.

KV Cache

If you've looked into using LLMs locally, or by way of running a remote GPU, you may have heard about the "KV cache" time and again, and that you need to reserve VRAM for it, or store it on a fast SSD if you absolutely must. This is a set of data that's created when the model runs, but that's actually reusable between queries in the same session.

The data that's saved and reused can be described as "the relations between tokens for this session." For example, the ever-popular sentence "you're right to push back" can be tokenized as "you-'re-ri-ght-to-pu-sh-back". The dashes between each token represent a vector (or array), called KV for key-value, that is used for determining which token gets picked next.

Since the text generation is linear, the math for calculating each new token needs every KV preceding it. Thus, if you're asking repeated questions in the same session, you'll want the KVs reused to avoid the expense of recalculating them over and over again not just with each round of conversation, but with each additional token.

Remember, an LLM doesn't simply answer the question you just posted — it always analyzes the entire conversation from the top to eventually generate tokens for your response. In a long conversation with 300,000 tokens, not having the cache would mean recalculating the KVs for all of them, a slow operation that gets disproportionately slower still as the interactions grow. This excellent blog post has a more detailed example.

Remember quantization? That's also applied here — modern models reduce the precision of the KV cache's content compared to their initial values, trading a minor accuracy loss for all new tokens in the session, in exchange for keeping VRAM usage down. In fact, with large context windows and conversations, the KV cache may well eat more space than the model and weights themselves.

Mixture-of-Experts (MoE)

Previously, AI firms touted how many more parameters their newest model had. Now, the race seems to have shifted to who can include more experts in any given model. That description may conjure a mental image of many tiny scientists living inside a knowledge laboratory, each handling a specific topic, but that's not quite how it works.

Mixture-of-experts, or MoE, is essentially a set of sub-models within the main model. When training starts, all sub-models get the same dataset, but the choice of weights is randomized ever so slightly for each one. Each sub-model will value tokens a bit differently from the rest, and due to how the training process reinforces patterns (called the "rich-get-richer" effect), related tokens will tend to converge together, so you end up with "experts" on sets of tokens.

A common misconception is to think that these experts are related to topics (like programming), but they're only establishing relations between tokens, meaning they'll be more accurate for certain words and text patterns, but devoid of any concept of what they're about. The fact that an expert might offer pretty good responses in regard to landscape painting is a happy accident, not a feature.

You may be thinking that this makes the models larger, and that maybe just the one generalist expert may prove a wiser choice. That's correct, but the trick is that the models only activate certain experts, meaning that only a portion of the model is actually running, thus at higher speed than a generic LLM.

This model selection happens on a per-token basis, too, as a router re-selects experts for each incoming token. For visualizing this, think of brain scan patterns where different areas light up depending on words heard. In practical terms, it means the model can answer more accurately in certain sections, while answering the question faster — albeit at the cost of higher VRAM usage.

Retrieval-Augmented Generation (RAG)

RAG is a concept that's pretty easy to describe, and yet one of the most powerful ones when used with any given model. In short, it can be described as: your chat application runs a few Google searches, feeds them to the model, and voila, there's now much more and fresher information to answer your question. This additional information can come in most any form: company documents, reports, emails, or spreadsheets — anything that you might supply to the model yourself, directly tokenized into its context window.

While broadly speaking most contemporary LLM chat applications run searches, activating RAG, usually triggered by selecting a "web search" switch or sometimes enabling "thinking" or "high effort" mode, will explicitly instruct the application to find as much data as it believes necessary to answer a query. Given that pure context is often key to answering many questions, and do so in an informational manner, RAG is among the most powerful tools available.

Fun fact: the search queries the application will search for are usually generated by a smaller, separate model that will, for example, say what web searches are bound to give the best results, or what to look for in a linked Office 365 account.

The main drawback is that it can be extremely computing-intensive, as the text for your questions and responses may consume, say, 10,000 tokens, yet the information in the retrieved webpages can take hundreds of thousands of tokens all on their own. For example, the Perplexity service was built with an emphasis on RAG usage, and judging from personal experience and user reports, it's usually much more competent and context-aware when answering questions, even though it's underpinned by the same models as everyone else.

Inference, Prefill and Decode

Inference is the act of using a trained model to generate outputs or tokens. It happens every time you send a prompt, and it's where all of the elements that we've discussed above come into play. Inference can be split into two primary parts: Prefill and Decode.

Prefill is the first phase of inference. Essentially, all of the input history (or your prompt) is tokenized and then processed by an AI model. This can include documents, code, and more. These inputs also dictate what gets put into your KV cache. Prefill is processed all at once in parallel, which often means that prefill times are often faster than overall outputs.

Prefill is a large part of the overall workload when measuring how long it takes for a specific chip or piece of hardware can take to output its first token, also known as "time to first token" (TTFT), a common AI benchmark, usually reported alongside prefill tokens per second. Longer prompts take longer to prefill, and context, KV cache, and everything else we've discussed are taken into account.

Decode is the second phase of inference. After a prefill has processed your input and has built the KV cache, the AI model then generates its response one token at a time, which must be done sequentially in order to determine the ones that come after, which means that the step cannot be performed in parallel, unlike prefill. Decode is usually also dictated by context, and reads from a model's KV cache during this step. Decode is usually limited by memory bandwidth, instead of raw compute, as streaming single tokens from weights and reading the KV cache is a bandwidth-intensive task, meaning memory speeds matter more. You'll usually see Decode speeds referred to as tokens-per-second (TPS).

Speculative Decode is a technique that makes those single tokens in the decode step stream faster, without changing the overall output. A drafting mechanism guesses several upcoming tokens, and then the model attempts to verify them in a parallel pass (almost like an additional prefill step). The model then verifies the pass itself; if the guesses are good, then the model's outputs are indeed correct, you get tokens for less of a computational cost during a given decode pass, and when they're bad, they are rejected by the main model during verification, since the main model's output is always what's kept.

Multi-token prediction (MTP) attempts to predict the next set of tokens at once, rather than only serving the next token. This is often built into AI models when they are trained with multiple steps. This can serve as the built-in drafting mechanism required for speculative decode.

This is a non-exhaustive list of concepts critical to understanding how AI models and LLMs work, but it is by no means complete. We will continue to update and refine this article as we add more.

This budget-focused desktop gaming PC is on sale for a steal at Walmart. Right now, you can save a tidy $350 when you purchase the ABS Cyclone Aqua gaming PC for just $849. A superb deal when you consider the current pricing for PC hardware in general. The original list price of the Cyclone Aqua was $1,199.99, so you're saving just shy of 30% and getting a current-generation graphics card to bring all the latest games to life on your screen.

● Grab this deal at Walmart

This particular SKU of the Cyclone Aqua gaming PC from ABS includes an Intel Core 5 Processor 120 (6-core, 12-thread CPU), an Nvidia RTX 5060 graphics card with 8GB of VRAM, 16GB of DDR4 RAM, and a 1TB NVMe SSD. This is a DDR4 memory system, which does date the PC a little, but it also means that getting hold of any extra RAM is also much, much cheaper.

Because this system uses an RTX 5060 inside, you have access to Nvidia's latest DLSS upscaling and multi-frame generation software technology. This helps lower-end systems get a lot more grunt out of their combined parts. The ABS Cyclone Aqua will tear through games at 1080p, but if you want to step up to 1440p resolutions, you may need a little help to reach higher and more stable frame rates. See our GPU Hierarchy chart for where the RTX 5060 sits in comparison to the performance of other graphics cards at 1080p resolution.

The wallet-friendly price of the ABS Cyclone Aqua means you get to spend more money on games, helping to make the most of the PC. Even though this may be a budget-oriented PC, it still comes with all the valuable features you'd like from a modern PC. There's built-in WiFi for easy network connectivity, and plenty of USB ports for your peripherals on both the front and rear IO of the case.

Some 700 Micron employees in Taiwan will receive five years’ worth of salary in bonuses as the company announced three bonuses in a single payout. According to Digitimes, this accounts for about 17% of the over 4,300 production-line employees working at Micron Taiwan. The remaining staff will not come out empty-handed, though, as they will receive three bonuses in the month of October. The news comes as Micron continues to battle worker unrest and unions fighting for increased pay.

The lump-sum payout consists of three incentives — an annual performance bonus that’s estimated to be between 7.5 and 12.5 months of salary for the majority of workers, a mass-production bonus amounting to 20% of their annual salary, plus a one-time $31,000 (NT$1,000,000) appreciation bonus which will be prorated for employees who haven’t reached a full year of service. Micron workers across the globe also expect a new quarterly profit bonus which will be paid 50% in cash and 50% in stock. This bonus will start in January 2027, with the first one to be paid 100% in cash.

Micron is following in the footsteps of Samsung and SK hynix, which have recently paid out massive bonuses to their workers of up to $400,000. This is a direct effect of the AI-driven memory chip boom, which saw the big three memory chip manufacturers — Micron, Samsung, and SK hynix — raking in massive orders from AI hyperscalers. The record profits that these companies are drawing from the appetite for AI data centers have led workers on production lines to demand a slice of the pie. Samsung workers voted to accept a $340,000 average bonus in May 2026, while SK hynix workers are set to receive $477,000 in bonuses this year, which could potentially grow to almost $900,000 in 2027.

Despite this seemingly massive payout, the workers’ union at Micron Taoyuan is still in the midst of a dispute with management regarding their pay. Some officials of the group have already launched a one-day strike as they call for clearer profit-sharing arrangements and terms for bonuses, with nearly 70% of members at the Taoyuan site turning down management’s latest offer. The two sides are tentatively set to meet on November 6 again for further negotiations. On the other hand, the union at Micron Taichung has already agreed to a 10% employer’s contribution towards worker pensions and a tripling of their day-shift allowance.

These bonuses allow workers to get a share of the massive profits that the chipmakers are raking in at the moment, allowing them to stay competitive for chip talent. However, their reliance on performance and profit bonuses also means that these incentives could fall precipitously if AI demand falls and the demand cycle returns to normal.

After Intel let loose its Panther Lake chips earlier in this year, followed by Wildcat Lake in April, the firm has been pretty quiet about upcoming consumer CPUs. Known leaker Jaykihn just let a tiny cat out of the bag, though: the base specs for Moon Lake. The diminutive chip is expected to have four low-power-efficiency (LP-E) cores, and zero performance cores. These are rumors, so have your salt shaker at hand.

Given the all-efficiency layout, it's a fair guess that the cores will be Darkmont LP-E silicon. The leaker additionally notes that the chip layout is likely to be the same as Wildcat Lake, with the efficiency cores in their own island. According to Jaykihn's table, the expected TDPs range from 6 W to 15 W, within the ballpark of phone and tablet SoCs on the low side.

The specs seem to imply that Moon Lake is the successor for the Intel N-series chips present in a great deal of ultra-budget offerings. The set of specs suggests the chip will find a home in laptops and mini-PCs in the $300 to $500 range, though those amounts are naturally variable depending on how much RAM and storage the AI world will continue to slurp. Furthermore, the new silicon is only slated to arrive in 2028, and supplies of NAND and DDR may well shift further until then.

There's no word on clock speeds, though. As a reference point, the N150 had a 3.6 GHz turbo. This year's 18A Panther and Wildcat chips broadly have clock speeds matching whichever silicon they've replaced. They do seem to be both faster and more power-efficient.

Some of the newer models pack extra cores, but Moon Lake keeps the same 4-LPE count and 6 W TDP as matching N-series designs. In turn, we speculate that while some performance gains are expected thanks to the new architecture's higher IPC, machines with Moon Lake inside will likely offer markedly improved battery life compared to their predecessors.

In a world where youth unemployment is growing as AI replaces more and more entry-level jobs, Cisco president and chief product officer Jeetu Patel is going for a different approach. According to a report by Fortune, he's given a 27-year-old ex-startup founder — brought into the company by an earlier acquisition, and previously languishing in a role five levels below — a new job to refactor 98% of the company's legacy code within 24 months.

The employee, Padmanabhan Krishnamurthy, joined Cisco after it acquired EzDubs, a translation startup that Krishnamurthy helped build in 2023. While having executive experience, Patel found that "Paddy" was left "buried away in the organization doing something unstrategic."

According to the report, Patel spoke to Krishnamurthy in Cisco's office in San Francisco, where Krishnamurthy told him that he could rewrite Cisco's legacy code in a team of just two people, and that "even the most AI-forward people on [Patel's] own team weren't thinking that way."

As a result, he's now been tasked with rewriting 98% of Cisco's legacy code within 24 months, with four weeks as a proof of concept. "I've given him unlimited tokens because he's going to refactor code," according to Patel, telling him "you can go nuts."

AI coding has skyrocketed in recent months, with GitHub forced to deploy 4 million extra CPU cores and 160 petabytes of storage to cope. However, the practice is not without its pitfalls, with rogue AI agents notorious for taking unsolicited actions including deleting entire company databases, TV show edits, and production setups.

Patel hopes that there are more young hires in the Krishnamurthy mould that he can find, both in the company and externally to hire. He's looking for hires to "challenge the system and the status quo," to be "an energizer bunny that just goes and kind of revs everyone up."

Patel believes that current corporate structures work against this kind of thinking. "If you don't hire the early-in-career [workers], and you just let your workforce continue the way that they were, where do fresh ideas get injected? How do you challenge the status quo?"

He acknowledges that this will create friction, putting Gen Z employees among decades-old employees. "Tension is good.... I actually try to create tension in the teams because you want to have enough friction on the ideas. You don't want to have friction with the people and personalities."

Patel believes that a combination of experienced, scale operators at Cisco, alongside external hires and "entrepreneurial founders, CEOs and engineers who are AI-native" will bring results. "Either of these groups by themselves aren't nearly as potent as when all three work together. It's what creates magic."

Bastian Recha, “Unskilledcast,” is a German former CS:GO commentator, tech reviewer, and 3D printing enthusiast. On October 28, he posted a demo of his “Mausstudio” project on X that generates a customizable mouse shell around an imported 3D model of a mouse PCB intended for 3D printing — all running locally and not generated using AI.

Bastian posted on X after six days of inactivity, stating that he’s been busy working on Mausstudio, his custom 3D-printed mouse shell generator. His post shows a video of him demonstrating the software, playing around with the ways users can customize their mouse.

Mausstudio works by importing an STL 3D file of a mouse PCB, like the Razer Viper V2 Pro and Zaunkoenig M2K PCBs in Bastian’s demo, from which the software detects the location of several key components: Mouse button switch locations, sensor location, scroll wheel location, and screw hole locations. It then generates a shell around those key components, placing stems to reach the switches, cutouts for the scroll wheel, buttons for the side switches, and base cutouts for the sensor.

Bastian’s demo also showed how users can adjust the width, length, and height of the mouse, including the location of the hump. Further customizations include weight-reducing cutouts for the upper and lower shell, with options to have a fully enclosed shell without the cutouts. To demonstrate the level of customizability, Bastian also showed a battery shelf option, allowing you to customize the location of the battery and the shelf’s size by inputting the battery’s dimensions — this feature, Bastian emphasizes, is lacking in many custom 3D-printed mice, and battery placement is treated as an afterthought, often just using hot glue or double-sided tape to adhere it.

Bastian also boasts that Mausstudio runs completely locally and without generative AI, just math and algorithms.

Bastian has not yet announced a release date for this project, nor has he announced whether this is paid or free, but he has posted an update on what has been changed since his demo, including being able to edit detected component assignments, more side button control, saving shell designs to be used on other PCBs, and cleaner geometry. We’re not sure if it can handle split PCBs like on the G Pro X Superlight, but support for that may come in the future as well.

This project looks to be a great way for users to create a mouse that’s closer to their ideal shape; good gaming mice are not cheap, and it’s a costly endeavor to buy many mice just to try out various shapes. This project may be able to let users experiment with more shapes more affordably with their existing mouse without needing CAD skills or hoping that someone has designed a mouse shell for their PCB that fits their needs. We will have to see how good the software is when it releases, and how it will improve and mature with future updates.

Nvidia’s Vera CPU is an important next step in the company’s years-long spearheading of AI infrastructure buildout. It’s arriving just as the shift to agentic AI workflows has created unprecedented demand for data center CPUs, and it’s designed specifically for this moment, at least according to Nvidia’s marketing bullet points. Although the idea that anyone developed a CPU for a shift in demand that arose just six months ago is reaching, as our benchmarks below show, Nvidia’s claims about Vera aren’t full of hot air.

Based on the workloads we have now — there aren’t standardized end-to-end agentic benchmarks focused on the CPU right now — Vera has strong indicators about its usefulness in agentic workflows. It offers chart-topping performance in Python, for instance, which makes up much of the backbone of the AI software ecosystem.

It also shows commanding memory bandwidth, both at the socket level and system level, with the high-speed C2C interconnect between Vera chips. In contentious agentic environments where agents may be querying large databases, that bandwidth is critical.

Specialization is one thing, but Vera is arriving in a hotly contested server market with custom Arm chips penetrating the data center and the old guard of x86 chips from AMD and Intel, which not only have a foundation in the data center, but also an extensive list of clients that work with their systems. Regardless of how Vera performs, that’s no small undertaking.

What’s surprising about Vera isn’t that it performs well in these agentic-adjacent workloads, but that it offers competent performance in general-purpose server workloads as well, often managing to match or beat the comparable AMD Turin and Intel Granite Rapids-AP chips. The scales are more balanced when looking at frontend workloads, with the core density available on x86 CPUs often the winner, but Vera trades that density for strong performance in backend, data-rich operations.

It’s an impressive showing for Nvidia's first custom core, called Olympus, and a stark improvement in nearly all aspects over Grace, which used an off-the-shelf Arm core design. The 88-core CPU is built on the Armv9.2-A architecture, including two threads per core through Nvidia’s “spatial multithreading.”

The chip is characterized by a 10-wide decode, similar to Arm’s AGI, as well as Nvidia’s second-gen Scalable Coherency Fabric (SCF). You can read more about the SCF and the Olympus core in our Vera deep dive, but SCF is critical to Vera’s performance. It’s a mesh that distributes L3 across the die, offering up to 3.4 TB/s of bandwidth across the die.

Vera has high memory bandwidth as well, leveraging replaceable SOCAMM2 LPDDR5X at 9600 MT/s, offering up to 1.2 TB/s of total memory bandwidth or around 14 GB/s per core, more than double that of AMD’s dense Turin designs.

We’ve heard a lot about the speeds and feeds of Vera, however. It’s time to understand its role in the modern AI data center, as well as see how it performs in our benchmarks against the x86 competition before AMD’s Venice and Intel’s Diamond Rapids start rolling out.

Understanding the CPU's role in agentic AI

Depending on where you’re at in the fast-moving world of agentic AI, you probably have a different definition of what exactly an agentic CPU is. Given the rise in prominence of agentic CPUs, the understanding of what makes a CPU tick for this type of workload has been largely defined by two companies: AMD and Nvidia. Although the points they’ve driven about concurrency and the varied work of agents are correct, it’s useful to derive a definition from how we’re seeing agentic platforms deployed right now.

Let’s back up from there and look at real agentic workflows in action to better understand where CPUs fit into this chain. As we’ve seen with Meta Muse and OpenAI Dots, there’s a class of persistent agent that gives you a slice of compute in the cloud. These CPU-only racks communicate with inference servers to actually run the models, but their workload is defined by concurrency. It’s not about the performance of an individual sandbox but instead how each sandbox performs when the chip is fully loaded.

Concurrency provides important context to some of the workloads companies like Nvidia and AMD have described. Some tool calls are more demanding, like code compilation and large database queries, but reading/writing files, running code tests, and going online in a headless browser is not demanding CPU work in isolation. When the chip is handling potentially thousands of these tool calls, however, slight advantages start to pile up. And, the CPU topology plays a bigger role in how performance is sustained as agent sessions increase.

That’s one major tenet of an agentic CPU, one that can maintain performance with high concurrency. The other is variability, which touches on the CPU, but also the larger infrastructure available. Mark Lohmeyer, VP/GM of AI and computing infrastructure at Google Cloud, described an example to Tom’s Hardware about planning a vacation with an agent. Using AI to plan a vacation is just about as trite as examples get, but Lohmeyer sheds some light on the unique challenge of agentic workloads from the hyperscaler perspective.

In the example, Lohmeyer described planning a vacation over the course of a month, where he would suddenly prompt the agent to do a bunch of work, but may not return to prompt again for another week. “The actual use of the underlying compute, it's happening in basically bursts, right? There's like a burst of use at the start, maybe for an hour, and then it goes away, and then a couple days later, [it] bursts again for some other purpose and goes away,” Lohmeyer said. “They're very long-running workloads, effectively long-running, but often idle, basically.”

Agentic calls for a lot of power on demand, as the right alignment of this bursty work on a server with hardware contention can bring everyone’s sessions to a crawl. This factors into the type of work the CPU is doing, as well, which can include heavy code compilation, graph traversal, and large database queries. Scale that up across several agents, all with their own independent loops that don’t neatly align with each other, and the CPU problem for agentic AI becomes clear.

In the August 2026 study, Architectural Implications of Agentic AI Workflows, from researchers at The University of Texas at Austin and Microsoft Azure, the paper describes three tasks of the CPU in an agentic flow. In addition to a scheduler for GPU inference, which is how CPUs function in one-shot-style LLM queries, CPUs also serve as orchestrators for maintaining agent states and communication, and as runners that call tools and execute agent logic (often in Python).

The paper also shows this contention that can happen when multiple agents are running at once, passing requests to the GPU, receiving responses, acting, and then passing requests back. We’re looking at a cross-section of four agents over less than half a minute, and you can already see how small CPU-based tasks can align, as well as create hangs between GPU time.

Nvidia has been clear that Vera is an agentic CPU, though defining the performance of an agentic loop is still a work in progress. “The industry built a pretty good way to benchmark traditional workloads,” Lohmeyer said. “I think this is happening now for GPUs and TPUs for inference, for example. I think we're going to need the same type of industry-level benchmarks for agentic workloads that are probably still emerging.”

Standardized benchmarks that look at the full agentic loop aren’t available yet. We have our own internal tests that we’re running and hope to have data for soon, and we’ve seen some vendor-level benchmarking from companies like Modal, but the broader industry hasn’t come forth with a set of benchmarks for the end-to-end agentic loop along the lines of a SPEC or TPC yet.

Although those tests aren’t available yet, we can still look at CPU performance through the lens of an agentic workflow, prioritizing aspects of performance that are important for agentic loops. We can also look at common workflows for agents, including code compilation for coding agents and database performance for retrieval agents. We can look at things like JSON parsing through the simdjson library, evaluating how these CPUs parse the large JSON files agents use to communicate. And we can look at the foundation of much of the AI ecosystem with Python performance.

That’s the focus of our benchmarking here, to establish where Vera stands among the x86 competition from AMD and Intel in well-known benchmarks. Hopefully we’ll have the opportunity for further testing in the near future to evaluate full agentic loops once that testing procedure is finalized.

Although we’re evaluating performance through an agentic lens here, Nvidia did not limit what tests we could run nor place any stipulations on our coverage. We’re thankful to Nvidia for providing us with remote access to a dual-socket Vera system for the purposes of testing, but beyond configuration checks to ensure the platform was operating as expected, we were left to run the tests we wanted to run. That includes several tests that are applicable to agentic workflows, but also tests from our general server CPU benchmarking suite, including Java, cryptography, compression, and rendering performance.

A gentle reminder for readers

Servers are not client PCs. You have much smarter operators with deeper access to the hardware, and often a software stack that’s unique to the specific workload at minimum, and highly tweaked and optimized for that workload at a maximum. With client CPUs, we can say that an AMD X3D chip is the best for gaming, or that Intel’s latest flagship offers the highest single-threaded performance. Servers depend much more on use cases.

In a typical review, we’d say that X is better than Y for Z purpose, and back that up with data. Here, that commentary is more muted. The reality is that hyperscalers maintain large fleets with a variety of hardware that can fit any purpose, and enterprises need to choose the hardware best suited not only for their operations, but also for TCO and a list of at least a dozen other variables that are specific to that business.

Nvidia Vera SPEC CPU 2026 estimates

So far, the primary benchmark we’ve had for Vera is SPEC CPU 2026 intrate, so we ran the full SPEC CPU 2026 suite to check Nvidia’s performance claims, as well as look at floating point performance. For anyone who hasn’t read SPEC CPU benchmarks before, there are a few disclaimers before heading into the results.

All of the runs here are estimates, and that’s due to how SPEC reporting works. There are strict reporting rules, and we’re not reporting these results to SPEC directly. The charts say “estimated,” but the numbers aren’t a guess. We ran SPEC CPU 2026 following the standard testing procedure, using the same compiler and flags for each CPU.

For the compiler, we used GCC 15.2 with a few common flags, including link-time optimization and march/mcpu to expose the correct instruction support to the compiler. We also used the general-purpose jemalloc memory allocator across all of the tests we ran.

Starting with the overall intrate, this is a series of applications primarily dealing with integer operations instead of floats, and the SOP is to run a copy of the application on each thread available. The test looks at what a CPU is capable of with many applications running at once, measuring throughput.

Nvidia’s numbers aren’t dissimilar from our own. In the company’s white paper, it showed Vera ahead of the EPYC 9755 by 3%, and with our tests, Vera is ahead by 1.5%. Keep in mind the considerably higher 512-thread count of the 9755 system here against the 352-thread Vera system. Vera absolutely runs away with performance when breaking down the score per-core.

Despite the core density of Intel’s 512-thread Xeon 6980P in a two-socket configuration, it’s not in the conversation here. Retesting with oneAPI represented an impressive 10% increase in performance with Intel-optimized binaries, but even against optimized code, Vera is 8.6% ahead with generic GCC.

There are some tests where Vera steals the show in the above album, as well as some where it falls behind, reflective of the testing we have later in this article. You can see that GCC compiler performance and Python performance are particularly strong points, with Nvidia even beating out AMD’s top-of-stack EPYC 9965.

Elsewhere, there are some weaknesses. SEAL crypto provides a look at the weaker cryptography performance of Vera, which is reflected in our other testing. Zstd compression also shows Vera trailing AMD and Intel, which makes sense. In our separate Zstd testing, Vera offered impressive compression performance with Zstd but fell behind on decompression.

We were curious about float performance on Vera given that Nvidia led with the standard intrate suite in its initial benchmarks. Vera tops the charts here on the back of some particularly strong showings in some of the tests. Our estimated PALM numbers, for instance, show off Vera’s second-generation Scalable Coherency Fabric (SCF), while ROMS pushes on memory bandwidth. This strong memory performance shows up in Marian and LBM, as well. There are a couple of misses, notably FemFlow and CACTUS, but the overall performance picture is clear.

The throughput benchmarks are what matter here for chips in this class, but we also ran SPEC CPU 2026’s intspeed suite for some estimated results, scaling up to use every thread available. No one is realistically running a single application on servers utilizing every thread available like this, but the numbers are there to browse if you’re interested.

Nvidia Vera Python and AI benchmarks

If there’s any direct benchmark for agentic AI right now, it’s Python performance. There isn’t one universal language for AI agents, but Python (and, to a lesser extent, TypeScript) make up the foundational agentic frameworks like LangGraph, CrewAI, and AutoGen. A strong agentic CPU right now is defined in part by strong Python performance, and Vera has exactly that.

Starting with a broad overview, you can see the total average test times for PyBench above, with Vera leading the pack. It’s 4% ahead of the EPYC 9755, which itself leads Intel’s Xeons and the larger 9965. Python is largely single-threaded, especially for these basic, built-in functions, so we see the 9755 pair leading the 9965 pair throughout all of our Python benchmarks.

PyPerformance provides a much more granular look at Python performance, evaluating start-up times for the Python interpreter, regular expressions, and even full Python applications. The story doesn’t change, however. Outside of a couple of misses in web server-focused tests, Vera tops the charts across the board.

The AI ecosystem right now is largely built on Python, so strong performance here is a good early indicator of strong AI performance in the data center. Of course, this space is rapidly evolving, and other languages are working their way into the ecosystem. For this moment, however, Python is critical, and it’s an aspect of performance Vera nails.

We also ran Llama.cpp with a CPU BLAS backend, checking text generation and prompt processing (at different token lengths) on the CPU using gpt-oss-20b-q8. For prompt processing, much of the process is handled on the GPU, so this pure-CPU benchmark isn’t totally representative of a real deployment. However, the CPU still plays a role here, particularly in tokenization.

Unsurprisingly, we can see the single 6980P topping the charts in prompt processing, with a 2P Xeon 6980P coming up in second place, likely on the back of Intel’s AI-specific ISA extensions. We expect AMD to further extend support for these instructions in Zen 6, which should level the playing field.

Nvidia Vera memory and compression benchmarks

Nvidia has touted the memory bandwidth of Vera as one of the chip’s strongest points, and the industry-standard STREAM memory bandwidth benchmark backs up those claims. Vera comes with a memory bus width more than twice as large as Grace, plus a large jump up to LPDDR5X-9600. It’s capable of up to 1.2 TB/s of memory bandwidth through the LPDDR5X interface, and up to 1.8 TB/s of bandwidth between Vera sockets.

AMD’s support for DDR5-6000 is relatively slow, and although Intel offers more bandwidth with MRDIMMs reaching up to 8,800 MT/s, Vera is in a league of its own here. That’s true without optimization, as well. After my initial STREAM pass, Nvidia suggested passing the OpenMP “spread” policy, which distributes processes as far away from each other as possible to reduce cache/memory contention.

With AMD and Intel, we see a slight regression with this setting, but it offers Vera a massive jump in memory bandwidth. Undoubtedly, the scales will start to level off as AMD and Intel roll out their next-gen Venice and Diamond Rapids parts, both of which tout much higher memory bandwidth than their predecessors.

Flipping over to the simple MBW bandwidth benchmark, we can see similarly strong performance from Vera, regardless of copy size.

The classic CacheBench benchmark flips the script, with Vera trailing the pack in the combined read / modify / write test. This weaker cache performance here is largely overshadowed by the memory bandwidth. The performance level is slightly below Nvidia’s contemporaries, but not by enough to make a significant difference.

Vera loses some points in compression workloads, which we see echoed in our SPEC runs in dedicated benchmarks. Using Zstd, Vera offers chart-topping compression performance at the default level 3 compression, as well as the maximum level 19 compression, across both standard and Long Mode runs. However, in decompression, the Turin chips take over, with Vera sitting alongside or below a dual 6980P configuration.

7-Zip traditionally favors Zen, so it’s no surprise to see both Turin configurations at the top of the charts. Vera loses out to the dual 6980P system in both compression and decompression performance via 7-Zip.

Nvidia Vera Java, database, and crypto benchmarks

Python is the language of choice for the AI ecosystem right now, but JavaScript/TypeScript are a close second. We ran three standard Java suites to evaluate performance: Java SciMark, DaCapo, and Renaissance.

Java SciMark is the best place to start. AMD wins out with the Zen 5-based 9755 in the composite score, but Vera maintains surprisingly strong positions in straight scientific operations, including FFTs, sparse matrix multiplies, and a Monte Carlo simulation. It falls behind in dense LU matrix factorization and Jacobi SOR.

DaCapo and Renaissance are both Java suites with a wide variety of real Java workloads, focused on native performance in DaCapo and VMs in Renaissance.

Database performance for Vera is strong across our ClickHouse and SQLite benchmarks. In max-thread SQLite, Vera is only beaten by a single-socket 6980P server, and it jumps into the lead in our like-for-like 64-thread and 128-thread tests. ClickHouse provides some interesting data that goes beyond a steady-state SQL database.

On the second and third runs of ClickHouse, Vera is competitive with a dual EPYC 9755 server, but from a cold cache, Vera is much faster, racking up an advantage of nearly 40% in the first run.

For bringing that data in, we measured JSON parsing speed with simdjson, which, as the name suggests, relies heavily on modern SIMD instructions. Turin has a natural lead here with its native AVX-512 execution.

In cryptography workloads, Vera falls behind the pack significantly in the John The Ripper password cracker, while trailing the massive core arrays of its competition in OpenSSL.

Nvidia Vera HPC and compilation benchmarks

HPC is usually the domain of dense CPUs, but Vera performs well in a variety of HPC tasks that we tested. In Discrete Fourier Transforms (DFT) using FFTE, Vera tops the charts, as it does in Octave and CloverLeaf. Although not strictly HPC, Vera falls in the middle of the pack in intensive mathematical operations like m-queens, and it trails chips with massive core arrays in the heavily threaded Primesieve.

Code compilation is a more contentious workload, with Vera trading blows against AMD and Intel depending on the workload. Starting with the compilers themselves, Vera takes a strong lead in a timed GCC compilation, beating out a dual 9755 system by 12%. It falls into last place in our LLVM compilation tests, however, both using standard Makefiles and Ninja. These tests measure the time it takes to compile the compiler itself, not the time it takes to compile an application with it.

Elsewhere, Vera shows up strongly in ImageMagick and PHP compiles while losing out slightly to the dual 9755s in Godot. Compiler performance is important to agentic AI given the prominence of coding agents, which often need to build software from source as part of an agentic coding workflow.

Nvidia Vera encoding and rendering benchmarks

Encoding and rendering workloads aren’t the focus of Vera, but we ran our standard list of benchmarks, which includes some rendering and encoding tests. Vera has strong performance here in some applications, though it certainly takes a backseat to the headline Python and memory performance shared earlier in this article.

The fight is between AMD and Nvidia here, with the 9755 and its full Zen 5 cores topping the charts in our AV1 encoding tests. With x265, Vera comes out on top, though by a margin lower than the 25%+ we can see the 9755s hold with AV1.

We normally run Blender, though there isn’t an official Arm Linux version available, so we skipped the test here. Instead, we have the cross-platform C-Ray and POV-Ray. Vera manages to outclass even the 768 threads available on a 2P EPYC 9965 server, both at 1080p and 4K. Vera also leads in POV-Ray, though on a technicality. Every server we tested was tightly coupled short of the 2P 6980P server.

For a more granular look, we have OSPRay, where Vera tops the charts in real-time path tracing, though falls short of the AMD and Intel flagships in real-time scientific visualization. The “dim_512” subset is worth drawing attention to. Strong AVX-512 performance allows AMD to offer three or even four times the performance of Vera in this workload.

Nvidia makes a delayed debut

Although Vera isn’t Nvidia’s first data center CPU, it’s the first one that the company is aggressively pursuing with the same fervor that it carries with its accelerators. It’s an impressive chip, often punching far above its weight class, especially in those workloads that can show off its unique capabilities and topology.

Still, it’s a single 88-core chip arriving in a market characterized by granular SKU lists to meet a wide range of specific workloads. Vera won’t be the right choice for all of them, which is something that Nvidia itself has already stated. “The world is not going to be served by one SKU of CPU. And that's not our intention,” said Nvidia’s Ian Buck when Vera was revealed in March.

That’s not the intention with Vera, but the performance against contemporary x86 CPUs shows that Nvidia’s new push into standalone data center CPUs has legs, more so than the various building-block-style Arm chips we’ve seen from hyperscalers, certainly. If nothing else, Vera demands some attention from AMD and Intel.

The x86 crew is responding shortly, with Zen 6 queued up to roll out through the end of the year and Intel’s Diamond Rapids targeting a 2027 release. As you can imagine, we'll be putting those chips through their paces as well.

MORE: CPU Benchmark Hierarchy

MORE: AMD vs. Intel

MORE: Intel Core Ultra 7 270K Plus review

MORE: AMD Ryzen 7 9850X3D review

Woot! has a brilliant deal right now on Nintendo's latest handheld gaming console. You can pick up the family-friendly Nintendo Switch 2, along with a free digital game code, for as low as $459 right now if you're a new Woot customer with the SAVETOBER coupon code. If you're not, you can still grab it for $479.

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Woot! is Amazon's discount sister site, so you've got the full backing of the huge global retailer behind this deal. $459 is an awesome price for a Switch 2 and a free game, and the good news is, you get to choose the game. Options for you to choose include Mario Kart World, Donkey Kong Bananza, or Pokémon Pokopia, which you'll need to download straight onto your Switch 2's storage.

This is a limited-time deal, with only four days left to run at the $519 sale price, but the coupon code is expected to run out tomorrow, November 1, at 11:59 pm ET, but don't expect it to be around even that long. This is a popular console, and as we approach the holiday season, this is the time that consoles like these become tricky to buy. With The Legend of Zelda: Ocarina of Time launching in just a few days, expect to see a huge spike in demand for this console, too.

The Nintendo Switch 2 is Nintendo's second generation of its popular Switch console. It has a 7.9-inch 1080p touchscreen, designed for you to hold in your hands. That said, if you prefer the big-screen treatment, it also has a dock that'll let you play games on a TV for 4K gameplay at up to 120 fps. You're also getting Joy-Con 2 controllers, which you can use attached or detached, depending on if the console is docked or not.

There's 256GB of internal storage included here for games, but the Switch 2 does have support for microSD Express cards to boost this further. If you're planning to install a big game collection, a card upgrade might be a good option. Right now, for instance, you can pick up this 512GB Samsung P9 Express microSD card for $99 at Amazon, another limited-time deal that saves you 34% on its usual price.

As we've mentioned, you also get a free digital game with this Switch 2 deal. Buying Mario Kart World, Donkey Kong Bananza, or Pokémon Pokopia separately would cost you between $69.99 and $79.99 at their usual prices, so with these discounts, you're getting a great deal.

The $459 sale price for this Woot! Nintendo Switch 2 bundle is a great option ahead of the holiday season. Don't expect to be able to wait for a better discount in a few weeks, as this is a popular console, and with a huge release like Ocarina of Time on the way, demand might outstrip supply. Just remember to use coupon code SAVETOBER before 11.59 am ET on November 1 at checkout to get the full $60 discount if you're a new customer, or $40 if you've used Woot! before.

A criminal investigation into the environmental damage caused by Google’s Finnish subsidiary has been launched. Tuike Finland Oy is being investigated after five complaints were received regarding the Muhos data center project, according to an official police statement published today [machine translation].

The police say that the investigation is backed by Finland’s Licensing and Supervision Authority (LVV). Google/Tuike Finland Oy is accused of suspected breaches of environmental law and environmental damage. Other lesser criminal offenses related to ambiguities and shortcomings in the drainage and water management are also being investigated.

Since Google representatives have previously publicly apologized for the unauthorized forest clearings near Muhos, which have turned 816 acres of forest into an expanse of mud, there seems little doubt it is guilty of some wrongdoing. Rather, it is the scale of the punishment and any proposed remedies that will probably be of most concern to execs from the tech giant now.

As well as the awful mess Google has made near Muhos, it seems to have also jumped the gun and plowed ahead with data center development by clearing 500 acres of forest near Otanmäki.

Though the horse has already bolted, Reuters reports that a Google rep said the company would cooperate with the ⁠police investigation and continue to focus on maintaining compliance at its sites. Maintaining?

Last week we reported that wolves had descended on Finnish villages after their habitats had been destroyed to make way for the Google AI data centers. Data center developments were already a thorny issue with locals. Potential construction and operating noise, pollution, utility competition, and environmental damage concerns were overshadowed – for some – by the presence of wild animals that had moved out of the forest.

Google sees Finland as an attractive location for data center development with its cool climate, affordable power, and government endorsement of AI projects. It has also benefitted from being able to develop in low population density areas and from a populace with a low propensity for protest. However, Google is testing the Finnish public with its trampling of the environment. The wolves, pollution, potentially 40% higher electricity bills, and other impacts weren’t helping its case, even before this criminal investigation.

Finnish government representatives have yet to comment on the case.

From hereon, this month shall be called SpookITober. In yet another high-profile cybersecurity incident this month, Binance.com, the world's largest cryptocurrency exchange, has been hacked, to the tune of $5.6 billion taken in various cryptocurrencies. As of this writing, withdrawals at the exchange are suspended until the company sorts out the situation, though it has stated repeatedly that "user funds remain safe," adding that it "will cover all losses." The U.S.-specific Binance.us website is so far reportedly unaffected. At least two security agencies, including TRM, have pinned the attack on an elite North Korean hacking unit.

"Our initial investigation suggested that the security incident may have involved a compromised laptop, however our subsequent investigation has uncovered additional points of breach," the company said in an update on Friday. "This was a complex attack, and we are doing all we can in coordination with law enforcement to resolve it. We are still gathering the facts, and will share more directly as we have them."

"TRM attributes this attack to TraderTraitor, North Korea’s premier hacking unit," the security firm said. "We base this assessment on robust on and off-chain intelligence sources," continuing to note that the group has been named responsible for a $1.5 billion Bybit hack last year. Per TRM, this latest instance bears many of the same hallmarks as earlier North Korean heists. According to CNN, an unnamed analyst at Pangolin Intelligence corroborated this story by "Analyzing the on-chain activity related to this hack and correlating with information gathered from previous known incidents [involving the North Korean cyber unit]."

Preliminary info breaks down the stolen amounts like so: $1.57b in Tether dollars (USDT), $1.45b in Bitcoin (BTC), $875m in Ethereum (ETH), $823m in Circle dollars (USDC), $234m in Tron (TRX), $88.5m in Ripple dollars (RLUSD), $75M in Wrapped-Beacon Ethereum (WBETH), $50m in Tether Gold (XAUT), and $447.5 million in other cryptocurrencies. The "dollar" currencies are all stablecoins, meaning they're pegged to $1 to represent dollars in the virtual space. Meanwhile, Tether Gold is pegged to the value of one troy ounce.

The stablecoin issuers all moved to blacklist the thief's main wallet (you can see it here on the Ethereum blockchain) in their smart contracts, a move that means the amounts in USDT, USDC, and RLUSD are frozen in place, totaling around $2.4 billion. It's possible, but not guaranteed, that the gold-pegged XAUT will also be frozen.

Note that this does not mean those funds have been recovered — the thief simply cannot do anything with those tokens, be it moving or exchanging. Normally in these situations, restitution from the frozen funds happens only after complainants provide proof of ownership, something that usually means copious time spent in courts.

The situation is a lot more grim for the amounts in pure cryptocurrencies like Bitcoin and Ethereum, among others. Since there is no governing entity for their blockchains, the most likely prospect is that the remaining $3.2 billion or so is forever lost in the ether — though some networks incorporate clawback mechanisms. Exchanges may block the thief when he eventually tries to cash out his virtual dough, but historically this hasn't been a reliable means of recovery.

Note that this is a developing story, so the tallies are subject to change. Should that amount be final, this heist would exceed the $1.5b stolen from Bybit in February 2025, of which only a paltry portion has so far been recovered. There's little info so far on the miscreant's means of larceny. Apparently, the Binance 14 , Binance 16, and Hot Wallet 34 hot wallets, which hold pooled customer funds, were drained. It's currently not known if this happened via stolen private keys, a compromised signing process, or another type of intrusion.

Binance has assured users that their funds are safe, and most importantly, that it will cover all losses. For what it's worth, the company has a means of insurance and a reasonable track record in these situations, though the amount of bullion pilfered today is exorbitant.

The company created its SAFU reserve fund in 2018, where it deposits 10% of its trading fees to cover any losses incurred from security issues. That fund currently sits at 15,000 BTC in it (verifiable here), or about $1.17b at the current BTC-USD price of $77,858. SAFU is a cold wallet, meaning its private keys are stored offline.

Back in May 2019, thieves made out with 7,000 BTC from Binance, amounting to around $40 million at the time. The company covered the entirety of the losses with SAFU funds.

However, while $1.17b is certainly a tidy sum, 'tis but a morsel of what currently looks like a multi-billion loss. Binance co-founder, shareholder, and former CEO Changpeng Zhao (CZ) stated in a tweet that he too expects that users will be made whole.

As a final note, Binance is alerting everyone to be on the lookout for contact attempts impersonating the company and/or referencing the current situation. Expect a flood of emails, SMS, and phone scams in the coming months. Be especially wary of any "compensation" or "withdrawal restoration" offers, and never click any e-mail links or otherwise offer any account information — always use the official website or applications yourself directly.

South Korean authorities have identified 80 Tesla drivers who allegedly forced Full Self-Driving (FSD) onto cars that were never cleared for it, Electrek reports. The Seoul Metropolitan Police Agency says it confiscated 21 of the devices used to switch the feature on. The unlock modules reportedly connect to the car’s CAN bus (the internal network that links its electronic control units) and get around the regional software lock that keeps FSD disabled. That works because Tesla builds the same computer into every car, whether or not the feature is approved locally.

Some suspects bought ready-made units from Europe and China, and one reportedly plugged a European-made module into his own Tesla. Others skipped the shopping. One suspect, an IT worker, built his device using information he found on GitHub, which suggests the barrier to entry for this kind of modification is lower than it sounds. The group also traded tips online, including how to stop the car from keeping any record that FSD had been activated. Concealing the activation history is likely an attempt to avoid detection by Tesla, which has been looking for these devices itself.

Then there is the legal risk, where prosecutors are using the Motor Vehicle Management Act, which bans unauthorized software changes that could affect vehicle safety. In Korea, the maximum penalty is two years in prison or a fine of up to 20 million won, or roughly $15,000, a steep price for a driver-assistance feature that many of these owners had already paid for.

South Korea’s transport ministry first flagged suspicious activations to police in April. “Safety risks were confirmed, including the possibility that FSD may fail to properly recognize driving conditions or operate differently from the driver’s intentions,” a police official said then.

Notably, the root of the problem is a certification gap. Tesla began offering FSD (Supervised) in South Korea in late 2025, but only on US-built Model S, Model X, and Cybertruck units, which skip local safety certification under the Korea-US free trade agreement. The Model 3 and Model Y arrive from Giga Shanghai and have yet to pass. Nobody agrees on how many Korean Teslas are eligible, although Carisyou Data Lab figures cited by MK last month indicate 47,458 of 229,311 cars (20.7%).

The South Korean investigation follows Tesla’s own efforts to stop unauthorized activation. In April, the company began remotely detecting unlock devices and disabling FSD access on affected vehicles. It has also warned owners that unauthorized modifications could void warranty coverage and leave them liable for crashes. Moreover, the restrictions are not the first time Tesla’s software protections have come under scrutiny. In August 2023, researchers exploited a hardware security flaw in the AMD processor powering Tesla’s media control unit to unlock certain paid features. The exploit targeted the car’s AMD-based system-on-chip, although researchers did not confirm that every premium feature could be unlocked.

Tesla has also faced regulatory scrutiny over software restrictions. In December 2021, the US National Highway Traffic Safety Administration investigated the company after an over-the-air update allowed drivers to play certain games while their vehicles were moving. The case highlighted how software changes can raise safety concerns when features are accessible under conditions regulators consider unsafe. Hardware reliability is another challenge for Tesla. Last year, the company detailed how it identifies defective processor cores in its Dojo AI supercomputers, where a single faulty component can compromise lengthy training runs.

The explosion at Intel's Fab 28 near Kiryat Gat, Israel, that happened earlier this month forced Intel to shut down the whole facility for two weeks, and now the company expects that it will cost it approximately $600 million in lost revenue over two quarters. The disruption is expected to affect server processors manufactured using Intel's 10nm-class process technologies and will reduce the company's shipments in both the fourth quarter of 2026 and the first quarter of 2027.

"In Israel, we have been shut down for about two weeks," said Dave Zinsner, chief financial officer of Intel, during the company's earnings call. "We feel like we are going to be starting up production here by the end of this week."

The explosion occurred in mid-October, killed two, seriously injured one, and caused an extended shutdown of Fab 28, one of Intel's key manufacturing facilities for chips produced using its 10nm-class and Intel 7 process technologies (Intel 7 was formerly called 10nm Enhanced SuperFin). Intel expects the incident to reduce its Q4 2026 revenue by approximately $300 million, and a similar impact is anticipated in Q1 2027. The company also expects the accident to reduce its fourth-quarter non-GAAP gross margin by approximately 2%.

Intel has not identified the affected Xeon processor families, disclosed the number of wafers damaged, or specified when Fab 28 will return to full production capacity after it restarts this Friday.

Although Intel's latest Xeon 6 processors use compute dies produced on Intel 3 fabrication technology, they still use I/O dies fabricated on Intel 7 (aka 10nm ESF) fabrication process. Furthermore, Intel continues to sell previous-generation Xeon 5 Emerald Rapids (and even Xeon 4 'Sapphire Rapids', though it stopped accepting orders on these CPUs in late September) that are made on Intel 7 manufacturing technology.

While Intel has plenty of 10nm-capable fabs — including Fab 12, Fab 22, Fab 32, Fab 42 in Arizona as well as Fab 28 in Israel — the disruption comes as Intel struggles to satisfy strong demand for server processors, so Intel cannot completely offset the lack of output at one facility by adjusting manufacturing at the others (i.e., it has done everything it could by now). For sure, Intel will not repurpose product lines it uses to make I/O dies for Xeon 6 to make previous-generation CPUs. In addition, other fabs may lack product-specific manufacturing qualifications, or the time and costs required to transfer production between facilities are too high, keeping in mind that Fab 28 is about to restart.

In addition to lost production time, Intel faces potential losses from wafers that were undergoing fabrication when the accident occurred. The company is assessing work-in-progress inventory to determine which wafers can be recovered and which must be discarded and written off. Zinsner expects most wafer write-offs to occur in Q4 2026, which explains why the gross-margin impact will be concentrated in that quarter despite the disruption to shipments extending into early 2027.

"Please note almost all of the $300 impact from Fab 28 is related to 10nm server products," Zinsner said.

Although Intel stresses that the shutdown primarily affects its 10nm-class server processors, the company has not disclosed whether the incident will also disrupt shipments of client processors manufactured using its Intel 7 (10nm ESF) process technology. Intel uses this node to manufacture a wide range of client CPUs, including its 12^th, 13^th, and 14^th Generation Core processors, such as Raptor Lake. However, it is unclear whether Fab 28 manufactures any of these products and, if so, whether Intel can compensate for the loss of production by increasing output at its other Intel 7-capable facilities. Consequently, the shutdown could potentially affect the availability of some consumer processors as well.

Intel's next-gen Nova Lake desktop processors will be the first client product to use Intel's enhanced 18A-P node, CEO Lip-Bu Tan confirmed in the company's Q3 2026 earnings call. The chief executive also said that the company intends to share more about Nova Lake at CES in January. The company's chief financial officer shared that Intel intends to take Panther Lake capacity offline to build more Granite Rapids data center CPUs.

"Nova Lake, our first Intel 18A-P client product, is tracking ahead of schedule, with design wins expecting to be twice those of Panther Lake at launch," Tan said in his prepared remarks. "We look forward to sharing more at CES in January."

There has been a lot of speculation about what node Intel's Nova Lake CPUs will use, with the rumor mill ping-ponging between Intel 18A and TSMC's N2P. Current speculation says the chip will use 18A-P for the SoC tile while using TSMC's N2P for the compute tile. It seems unlikely that Intel would use its most advanced node for an SoC tile, though it's possible.

Regardless, Nova Lake chips will use 18A-P in some regard. Intel announced in June that 18A-P had entered risk production, and at Hot Chips in August, Intel revealed its next-gen Xeon 7 Diamond Rapids will be the company's first server products to use the enhanced node.

Compared to 18A, which is available in Panther Lake and Wildcat Lake products currently, 18A-P comes with a 9% improvement in speed at iso-power, or an 18% reduction in power at iso-speed. Intel has described 18A-P as a drop-in replacement for 18A, allowing designs built for 18A to instead leverage 18A-P without any extra engineering work.

For 18A products, Intel says its new Wildcat Lake products that have shown up in devices like the Framework Laptop 12 are increasingly popular. "Demand for our Wildcat Lake is outstripping supply," said Tan. Intel says that 18A products now make up a third of client CPU revenue, up from less than 5% in Q1.

It makes sense that those products would be popular given the current environment. Tan said that Intel's OEM partners are "aggressively managing inventory to match softer sell-through," which means buying less to match the demand in the market. Intel's David Zinsner said the company expects the company's client business to decline by "more than 20%" in the next quarter."

Zinsner warned of a further contraction in the PC market, describing a sustained decline in the size of the market over the next year, at least. "We expect the 2026 PC unit [Total Addressable Market] to decline approximately 12%, with second half units down more than 20%. We do not expect a sustained recovery until memory availability improves and pricing begins to normalize," Zinsner said. "For 2027, we're planning a PC unit TAM to decline roughly 10%."

Taking the comments together, Intel is saying that it expects the market itself to shrink significantly over the next year due to memory prices. It's not a situation of demand outstripping supply, at least not directly like the case of a component shortage. Rather, it's a pullback from direct customers, OEMs, and IT departments from spending, which is more significant.

Although Intel says the client PC market is shrinking, the company says that its long-term outlook remains positive. "We think next year, and in future years, we're a market-share-gain type of environment... we do think the PC is going to through a renaissance despite [memory shortage]," said Zinsner.

Despite the optimism, Zinsner said that Intel is taking some of its client capacity offline to build more data center products. In response to a question about reallocating wafers from client products, here's what the executive said: "Answer: Yes... What we're taking offline to put Intel 3 into Oregon is 18A wafers, which were generally going to build Panther Lake products, so we see the opportunity with some of that capacity freed up to go produce more data center wafers."