Preorders for the Nintendo Switch 2 The Legend of Zelda 40th Anniversary Edition went live shortly after yesterday's Nintendo Direct and sold out instantly. Walmart has listed the handheld gaming console but has enabled preorders. Meanwhile, you can preorder it on Amazon for $519.99, so you may still have a chance to get one at MSRP before the scalpers.

• Pre-order the Switch 2 The Legend of Zelda 40th Anniversary Edition on Amazon
• Pre-order the Switch 2 The Legend of Zelda 40th Anniversary Edition on Walmart
• Pre-order the Switch 2 The Legend of Zelda 40th Anniversary Edition on Best Buy
• Pre-order the Switch 2 The Legend of Zelda 40th Anniversary Edition on Target
• Pre-order the Switch 2 The Legend of Zelda 40th Anniversary Edition on GameStop

Major U.S. retailers, including Target, Best Buy, and GameStop, were the first to enable preorders for the Switch 2 The Legend of Zelda 40th Anniversary Edition, which instantly sold out. It did not take long for scalpers to hit eBay to resell preorders for as much as $1,000, almost 2X over Nintendo's MSRP. Zelda-exclusive consoles have always been prime targets for scalpers.

The Switch 2 The Legend of Zelda 40th Anniversary Edition, the first limited edition of the Switch 2 and a The Legend of Zelda-themed edition celebrating the franchise's 40 years, makes the handheld console even more desirable to hardcore fans and scalpers looking to make a quick profit. Plus, the console's $519.99 price tag, which is only $20 over the vanilla Switch 2, gives gamers extra incentive to purchase one. The problem is finding one in stock.

Along with the console, Nintendo also gave the Switch 2 Pro controller and carrying case with an included screen protector the same The Legend of Zelda 40th Anniversary Edition treatment. Best Buy currently has both up for preorder at $99.99 and $39.99, respectively.

The Switch 2 The Legend of Zelda 40th Anniversary Edition officially launches on October 29. Historically, The Legend of Zelda-themed consoles are available for purchase on launch day. However, stock will likely be extremely limited, which is why many fans like to lock in their preorders early.

It remains to be seen whether Nintendo will produce many units of the Switch 2 The Legend of Zelda 40th Anniversary Edition. At least for the previous limited-edition consoles, like the Nintendo Switch OLED: Tears of the Kingdom Edition or Nintendo Switch Lite: Hyrule Edition, stock was not a problem since you can still buy them today despite being last-generation consoles.

Following an early access period that included over 60,000 participants, auto-overclocking tool Hypertune has released its Gaming Performance Engineering platform, which is built on top of Intel's Extreme Tuning Utility (XTU) SDK and developed in partnership with Intel. The company claims the utility can boost frame rates by up to 60%, though you shouldn't expect that as the norm. The tool includes automated CPU and GPU overclocking, as well as customizable Windows features, network optimization, and game-specific optimizations.

Hypertune partnered with Intel to build the tool, which the company says "evaluates each supported system individually" before optimizing rather than relying on generalized profiles. In its press release, Hypertune says it collaborated with famed overclocker SkatterBencher (Pieter Plaisier) to refine the software. We've reached out to Plaisier to confirm their involvement.

Automated tuning programs usually don't work as well as advertised, and we haven't had the chance to test Hypertune ourselves yet. Especially on more recent hardware, expect performance gains to be minor. Hypertune shared some of its internal benchmarks to back up the claim, showcasing the actual test systems it used, the numbers it gathered, and what each step of Hypertune contributed to the performance increase.

Hypertune tested two systems: one with a Core Ultra 9 285K and an RTX 5090, and another with a Core i7-14700K and an RTX 3080. For the 285K system, the team saw an 18.9% improvement in Homeworld 3 and a 28.2% improvement in Tomb Raider. For the 14700K system, the boost was up to 9.8% in Rainbow Six Siege and 4.3% in Marvel Rivals.

Notably, these results are with Hypertune's Game Hub disabled. Game Hub automatically applies a graphics settings profile to select games, leading to massive increases in performance. Naturally, tweaking your own graphics settings in the same way leads to the same result.

In Homeworld 3, you can see how each step in the process impacted performance, with CPU tunning contributing the single biggest increase in performance. As shown by Marvel Rivals in Hypertune's data, some games will see little to no benefit from Hypertune, though select titles with certain hardware may see a significant performance increase. In this case, the Core Ultra 9 285K has plenty of room for overclocking, and Homeworld 3 is particularly sensitive to the CPU, so the uplift makes sense.

Elsewhere, the gains aren't as pronounced. In Rainbow Six Siege, you can see that Hypertune contributed about a 9.8% jump in performance, though the vast majority of the improvement comes through Game Hub, where Hypertune changes in-game settings.

In a press release, Hypertune founder Austin Copeland wrote that the team was "not trying to build a tool for overclockers," suggesting it's aimed toward users who may not know about specific settings (i.e., the Balanced power plan on dual-CCD X3D CPUs, or HAGS for DLSS Frame Generation). Copeland was previously a coach for eSports organization TSM, coaching Valorant teams under the name "Apex."

Hypertune works through Intel's XTU SDK, and the company says its optimizations are non-destructive and fully reversible. The software is mainly targeted toward competitive titles (naturally, given Copeland's background), but it can apply optimizations globally across the system. Hypertune says it's safe to use with anti-cheat software, including Riot Vanguard, Easy Anti-Cheat, and BattlEye.

Although there are plenty of free tools that claim to optimize your system, Hypertune isn't among them. It's a subscription service, available for either $9.99 per month or $59.99 per year. In addition to software, Hypertune offers its "expert tuning" service for $80, where a technician will remote into your machine and manually tune it. On the subscription front, Hypertune offers a 7-day free trial.

Hypertune looks like one of the more robust automated overclocking tools we've seen, but it's worth highlighting that, in most cases, these tools don't do anything you can't accomplish yourself. If you're looking for a starting point, make sure to read our guides on how to overclock your graphics card and how to overclock your CPU.

Modular laptop manufacturer Framework has just announced that it's dropping some of its memory pricing for its new Framework Laptop 13 Pro after previously doubling it back in July, including retroactively for orders that have already shipped. Posting on social media and in an update on its blog, the company says that it's been able to source new inventory at a reduced cost.

"We have a positive update today on LPCAMM2 pricing! We were able to source a limited quantity at lower cost, and are retroactively reducing prices for orders that shipped at higher price, as well as offering the lower price to orders through Batch 10" according to the company's recent X post. A further update, posted on the company's blog, goes on to explain that Framework's Supply Chain team "has been working continuously to source additional inventory at better prices."

The lower prices are due to a "limited quantity" of Micron 32GB and 64GB LPCAMM2 memory modules that the company has secured at "lower cost," and that as the company is "committed to passing on savings whenever we can," it is passing the price reductions back to customers. LPCAMM2 memory is available for the Intel Core Ultra Series 3 version of the Framework laptop. Other models, like those shipping with AMD Ryzen AI 300 CPUs, are using SO-DIMMs instead.

The price reductions are being applied to "both already shipped and a subset of pending pre-orders," according to chief executive Nirav Patel. This improves the situation for consumers compared to the last price update on July 22, which saw Framework almost double its prices for 32GB and 64GB LPCAMM2 memory in the Laptop 13 Pro. While these new prices are "still above our original pre-order launch baseline, they are lower than the last cost update that drove our current pricing for new pre-orders," according to the company.

This pricing isn't expected to last for long, however. As Framework notes, only a limited quantity of new modules has been acquired at lower cost. Framework explains on X that these are being made available "first-come, first-serve to customers through Batch 10". If the company is able to source more, then it'll "open it to later batches and then to new pre-orders."

Framework is automatically refunding the difference if your order has already shipped and you paid the higher price. Pending pre-orders through to Batch 10 have also been adjusted to reflect the better pricing. If you're waiting on a pre-order and want to update the RAM capacity you've ordered to take advantage of the lower prices, that option is now available via the order summary page, with instructions in Framework's Help Center if you run into trouble.

Those with a DIY Edition Framework 13 Pro that has already shipped and who want to switch to the 32GB or 64GB LPCAMM2 modules at the lower price can also fill out this contact form to organize a swap.

If you play hard to get, you could get Xbox Game Pass for cheaper; at least that is what numerous users are reporting on a Reddit thread. One Xbox Game Pass subscriber shared a screenshot of Microsoft offering them the Ultimate plan for $16.09 for a month, 30% lower than the standard $22.99. Their secret? Cancel Xbox Game Pass almost every month.

Dynamic win-back discounts are perhaps the oldest trick in the book, but they remain widely used because of their effectiveness in re-engaging customers who have stopped buying a product or, in Microsoft's case, canceled their Xbox Game Pass subscription. These targeted offers, often disguised as "We Miss You" or "Welcome back" deals, aim to win back lost customers with exclusive or time-limited discounts.

According to the Redditor, they have used Xbox Game Pass on and off for about two years. However, instead of committing to an annual plan, the user frequently paused and resubscribed. In the Redditor's words, they were "not doing any sort of tricks lol...just got the offer randomly because I cancel gamepass almost every month, so maybe this was their way of keeping me."

This kind of customer behavior is not uncommon among subscription services, since buyers seek to maximize value and avoid unnecessary spending. Apparently, Microsoft seemingly noticed this user and proactively offered a 30% discount, dropping the Xbox Game Pass Ultimate price from $22.99 to $16.09 for a year. And it worked. The Redditor confirmed that they took the offer and locked in for the entire year.

At least one other Redditor confirmed they also received the same $16.09 offer. The thread quickly filled with additional posts as users shared their experiences with Xbox Game Pass discounts. For example, one Reddit user said that when they logged in with a secondary account on their Xbox console, they saw an $18 offer for 12 months of Xbox Game Pass Ultimate. Their main account allegedly never receives offers like this. Meanwhile, other users chimed in to report receiving $ 20-per-month offers.

It is unclear what criteria Microsoft uses to distribute these win-back discounts. Many speculate that the company may be considering activity, subscription history, or even multiple accounts. The discounts do not seem limited to the U.S., where the Redditor lives. Users in the thread have reported similar discounts in regions like México.

Despite Microsoft slashing Xbox Game Pass pricing earlier this year, many gamers still resort to digital key marketplaces such as Eneba, G2A, or Loaded, where one month of Xbox Game Pass Ultimate can cost as little as $9.28. By running targeted annual promotions, Microsoft can encourage subscribers to stick with Xbox Game Pass, secure long-term revenue, and avoid the risks of month-to-month cancellation.

However, these targeted discounts raise an important question: should Microsoft not reward loyal Xbox Game Pass subscribers with similar exclusive discounts? Win-back promotions are great for bringing back former subscribers, but long-time users will feel overlooked and see no reason to commit, since the generous deals go to those who cancel and return.

OpenAI is expanding its relationship with Samsung beyond memory supply and enterprise software as the AI giant plans to outsource production of at least some of its processors to Samsung Foundry, Harrison Kim, General Manager of OpenAI Korea, revealed this week. If the information is accurate, then OpenAI will source its AI accelerators from both TSMC and Samsung Foundry, which suggests massive volume requirements.

"One of the areas where we have made the most progress and gained the most recognition with Samsung Electronics is our joint production and ​research on the next-generation chips we are developing," said Harrison Kim, General Manager of OpenAI Korea, at ​a press conference in Seoul, Reuters reports.

OpenAI already has its own AI ASIC program that relies on Broadcom's design services as well as TSMC's wafer processing and advanced packaging services. So far, the company has introduced its first inference AI accelerator called Jalapeño that was defined by the company's engineers, then co-designed with Broadcom, then made by TSMC, all in less than 18 months.

OpenAI did not explain whether Samsung's participation concerns a second production source for Jalapeño, another processor under development by OpenAI, a chip jointly developed by Samsung and OpenAI, or some other aspect of chip production and development. Samsung and SK hynix already supply memory for OpenAI's Stargate data center initiative, though joint chip development and production barely have a relation to DRAM supply.

OpenAI's 1^st Generation Jalapeño will unlikely be double-sourced from TSMC and Samsung because the chip is already in mass production at TSMC and OpenAI is talking about 'next-generation chips,' not the ones that are in mass production at the moment. Furthermore, development of Jalapeño's successor is well underway and is approaching tapeout, which means that its mass production is not far away either. Since OpenAI's claim clearly involves 'next-generation chips,' it is entirely possible that OpenAI will indeed produce its 2^nd Generation inference ASIC at Samsung Foundry.

Back in late July, Samsung Electronics and Broadcom announced a strategic partnership valued at over $200 billion through 2030 to collaborate on advanced foundry, memory, and packaging technologies for AI infrastructure. Hence, as OpenAI has an agreement with Broadcom to procure 10GW of custom AI accelerators, it will be able to produce these accelerators at both Samsung and TSMC. Of course, if it needs silicon produced at Samsung, and pays Broadcom for appropriate design porting.

Perhaps, OpenAI will take a page from Tesla's book and will double-source Jalapeño's successor from TSMC and Samsung to get higher volumes. However, Tesla's volume requirements may be different from those of OpenAI.

Tesla needs extraordinary AI5 volumes because it intends to use the processor across three very different high-volume applications: AI data centers, vehicles, and Optimus robots. Therefore, Tesla could potentially need millions of AI5 chips for cars alone, on top of robots and data-center deployments. Therefore, paying for separate TSMC and Samsung physical implementations gives Tesla not only supply-chain resilience but also aggregate capacity necessary to supply several product categories.

Yet, data center accelerators tend to be vastly more silicon-intensive per unit compared to ASICs for vehicles or robots. If OpenAI/Broadcom's next ASIC is a large leading-edge processor with multiple dies and OpenAI wants gigawatts of these processors, wafer requirements could still become too high for TSMC alone (which is fully booked by the likes of AMD and Nvidia). In that situation, OpenAI may need another foundry to get enough ASICs. Still, we are speculating.

Whether you are into content creation, 3D animation or modeling, or visual effects creation, you need the right tools to succeed. Autodesk's software is second to none in these fields of work, and for a limited time, you can save up to 40% on the company's Media and Entertainment Collection. This is a great opportunity to save hundreds of dollars on software vital to your work.

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The Autodesk Media and Entertainment Collection is a comprehensive software bundle that includes industry-standard programs, such as Maya and 3ds Max. Professionals and aspiring students aiming to thrive in the 3D animation or 3D modeling industry are likely already familiar with this software and understand how significant the investment in their subscriptions can be.

While Autodesk offers individual subscriptions for its vast portfolio of software, the Media and Entertainment Collection bundle is a far smarter investment for most users. By picking up the bundle, you can save up to 40% compared to purchasing each piece of software separately. It is more affordable for professionals and students and offers the convenience of having all the necessary software in one package.

Autodesk presently offers the Media and Entertainment Collection in four unique variants that target different industries. There are software options for modeling, motion capture, animation, and rendering. Pricing remains the same across all four packages, as does the 40% discount. You are not just receiving an excellent piece of software at a great price, but you also have access to Autodesk's 24/7 online support and resources.

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

The Goodram PX700 is a budget PCIe 4.0 drive that delivers everything you need: good performance, great power efficiency, and all in a single-sided package that runs cool. Sometimes it really is as simple as picking one drive to meet whatever your storage needs are at the moment. It’s not the first model that comes to mind for this segment, but in our testing it performed better than expected as a good drive that may otherwise run under the radar.

Goodram is not a new or no-name company, even if the “good RAM” moniker seems a little too on the nose. Instead, it’s a memory assembler in the EU with quite some history. This, in fact, makes it more approachable in comparison to a lot of the new names we have seen on AliExpress and increasingly on Amazon. We expect reliability or at least good support from the company, and the TBW on this one seems to reflect that this isn’t an ultra-budget drive.

On the contrary, performance was not just what we expected – considering we’ve reviewed many drives in this segment, many with the same or comparable hardware – but above. Sometimes only by a small amount, but the gaps are real. Anywhere performance mattered, it delivered. One expects trade-offs for that, but in fact the PX700 is very power-efficient, and the drive provides an excellent storage experience. The only downsides are sustained write performance – which is of little importance in most cases – and, unfortunately, the price. At the right price point, this would be a great pick for any device.

Goodram PX700 Specifications

The Goodram PX700 covers the most popular capacities of 1TB, 2TB, and 4TB. Smaller drives can be popular for upgrades and basic boot usage, especially with how prices are now, but modern flash scales best at 1TB and up. 8TB drives are great as a larger storage solution but require a significant investment. The PX700 sticks to what will sell, predominantly 1TB and 2TB. At the time of review, these are priced at $346 and $418, respectively. These prices are high and probably point to a lack of stock more than anything else. While the 1TB price is double what it should be, at 2TB a drive of this type should be closer to $300. Naturally, one has to take into account the pricing volatility in an SSD market besieged by a NAND shortage, but we recommend looking in that range for this drive or one comparable to it with similar or the same hardware.

The drive is full-fledged Gen 4 with performance up to 7,400 / 6,500 MB/s for sequential reads and writes and up to 1 million random read and write IOPS at 4TB with only 820K write at 1TB/2TB. This is standard for the class and is more than enough performance for everyday tasks, gaming, productivity workflows, and more. The drive has the standard five-year warranty with up to 600TB of writes for every TB. The last part of that is worth a second glance because it indicates this drive can come with TLC rather than QLC flash, since TLC, aside from being faster, has better endurance than QLC. However, we have reasons to believe our sample has QLC, which we will point out where the evidence supports that reasoning. We want to emphasize that you should not assume what flash a drive uses based on TBW alone.

Goodram PX700 Software and Accessories

Goodram does not offer any specialized or custom software for its SSDs. You will have to rely on third-party solutions. Software for SSDs essentially falls into three categories: health management, performance, and data backup. SSD toolbox applications from manufacturers will usually cover the first two, with, typically, an OEM version of Acronis True Image for the last. For free alternatives, we recommend CrystalDiskInfo for health information, CrystalDiskMark for basic benchmarking, and MultiDrive for data backup and imaging. MultiDrive is made for Windows and has a bootable WinPE image, so for other operating systems you can opt to use Clonezilla instead.

Goodram PX700: A Closer Look

The PX700 is a single-sided drive at all capacity points. This makes it compatible with a wider range of systems and, if you need to cool the drive, it’s easier to tackle just one side of components. As it turns out, the drive runs cool enough that you probably don’t have to worry about this – check the Power Consumption and Temperature section below – but it’s still worth pointing out. The data on the rear label is standard, but we do want to say a little bit more about the listed manufacturer.

Wilk Elektronik is a Poland-based memory company, which makes this interesting, as that means the PX700 is a European-assembled SSD. Goodram covers the mainstream parts and IRDM enthusiast, although this is the first SSD from them that we’ve reviewed. As we are going to dig into the hardware in this section, we will say that the drive's silicon is sourced from Asia, as with virtually every SSD. If we say a drive is made in Poland, this means the drive is assembled, tested, and covered by warranty in Poland with an EU anchor. For some, that might be a positive factor in any purchasing decision.

Identifying the hardware here is half simple and half challenging. The controller is clearly the Maxio MAP1602, a four-channel, DRAM-less part that’s been a budget champion for quite some time. There is not much mystery there aside from maybe this being a later revision, typically used for higher capacities.

The flash is more difficult. We can ascertain that these are 512GB NAND flash packages from the “512G” – given modern flash is 1Tb or 128GB, four dies per package for a total of sixteen dies. That’s perfect for maximizing performance out of this controller. The “W2533” is likely the manufacture date of week 33, year 2025. All this indicates is that this is, at least, not older flash, although we already know it has to be 2,400 MT/s at a minimum to hit the specified performance.

It makes a lot of sense for the PX700 to use TLC flash, though, given the TBW and the fact that we’ve been seeing this controller with 232-Layer Micron TLC more frequently lately. The Biwin Black Opal NV7400 comes to mind. However, Goodram has access to YMTC flash, and that includes QLC. YMTC rather than Micron QLC is more likely, given the latter is in high demand in enterprise. Normally, you could use certain utilities to identify the flash, but firmware for this controller is often encrypted to avoid revealing the flash ID. It’s still possible to get around this with leaked MP tools, but otherwise the best way to identify it is simply to look at the performance. We’ll be doing that in this review with comparison drives to help back up the investigation.

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Comparison Products

The Goodram PX700 is in a tight spot with ample competition in today’s market. This has been true for some years now, but some more recent releases have turned up the heat. These would include the Sandisk WD Blue SN5100 with its fantastic random read latency, the Crucial P310 with surprisingly good all-around performance, and even the Samsung 990, which was a very late arrival. Existing drives with comparable hardware to the PX700 include the Acer FA200 with QLC flash plus the Biwin Black Opal NV7400, Addlink A93, and Inland TN470 with TLC. The last of these also uses a Phison controller rather than the more common Maxio MAP1602. Lastly, we have the budget-minded Kingston NV3, which is always looming in the background.

Trace Testing — 3DMark Storage Benchmark

Built for gamers, 3DMark’s Storage Benchmark focuses on real-world gaming performance. Each round in this benchmark stresses storage based on gaming activities including loading games, saving progress, installing game files, and recording gameplay video streams. Future gaming benchmarks will be DirectStorage-inclusive and an evaluation for future-proofing is included where applicable.

The PX700’s 3DMark results are spot-on for a drive of this caliber, hanging out at around 40µs for latency with bandwidth over 700 MB/s. This level of performance is excellent for gaming, and the only drive that wouldn’t fall into that category is the NV3. The NV3 is still fast enough for a games drive and remains a good budget choice, but there are faster options like the PX700. We want to emphasize that the PX700 is better than it looks in this lineup because it’s effectively within range of all drives but one, the WD Blue SN5100.

Among the QLC-based drives, the WD Blue SN5100, 990, and P310 all notably score near the top. The BiCS flash in the first drive is known for its responsiveness and, yes, it will give you a small edge with game load times. However, the 990 is right up there with the P310, which means there is no shortage of viable options. Most slower drives, from a gamer’s perspective, will be last-generation or slower PCIe 4.0, which means it’s not hard to find drives that are very fast for games. This means that the deciding factor might lie outside of 3DMark performance, such as drive reliability.

Trace Testing — PCMark 10 Storage Benchmark

PCMark 10 is an industry standard trace-based benchmark that uses a wide-ranging set of real-world traces from popular applications and everyday tasks to measure the performance of storage devices. The results are particularly useful when analyzing drives for their use as primary/boot storage devices and in work environments.

While the PX700 is merely mediocre against the competition in 3DMark – its absolute performance is quite good, though – its showing in PCMark 10 is better. This is again a scenario where the drives with TLC flash, including the TN470 with Phison’s controller, are underwhelming. This can be confusing to buyers who have relied on the mantra that TLC is better than QLC. There are reasons for the surprise here, although the general idea that benchmarking is often looking at ideal scenarios is perhaps the easiest answer.

These drives, whether TLC or QLC, rely on pSLC cache as much as possible to give you the best performance. This pSLC mode is not equivalent to native pSLC and is generally the same speed regardless of the flash type. QLC, however, tends to lead TLC in some ways and follow it in others. For example, QLC gets larger benefits from optimizations, so it may see advanced techniques first. This includes improvements to 4K latency. On the other hand, QLC has four planes when TLC has advanced to six. As we’ve seen with BiCS flash, which is four-plane against six-plane TLC from Micron and YMTC, fewer planes can lead to lower effective latency. The advantage of a higher plane count is in bandwidth but does not come without costs and drawbacks.

The PX700 is also faster than a QLC-based drive with the same controller, like the FA200. In that case, the FA200 uses twice as many dies – thirty-two – which puts more load on the controller. This can increase latency. In general, the idea has been that bigger drives are faster, which is certainly true in many cases. Larger drives will have more bandwidth and can reach higher IOPS due to improved parallelization. However, past four dies for each channel – this is where the PX700 falls, and the FA200 exceeds – you are increasing power consumption and overhead. So, the PX700 is in a sweet spot for how this benchmark tests.

We would go on to say that the P310, WD Blue SN5100, and 990 all beat the PX700. These are all QLC-based drives with a range of controllers. We’ve already explained the BiCS factor. Samsung’s newer QLC is also very good. The P310 has always been a bit of an outlier, as on paper its hardware shouldn’t perform as well as it does. Considering Crucial used to rely at least partially on proprietary controllers, we think this is a situation where they did a lot of optimization. WD and Sandisk also did this with the Black SN8100/Optimus Pro GX 8100 so there’s some “special sauce” at play. The PX700 being the best of the rest is a point in its favor, as those are the drives it’s competing with in the real market.

Console Testing — PlayStation 5 Transfers

The PlayStation 5 is capable of taking one additional PCIe 4.0 or faster SSD for extra game storage. While any 4.0 drive will technically work, Sony recommends drives that can deliver at least 5,500 MB/s of sequential read bandwidth for optimal performance. Based on our extensive testing, PCIe 5.0 SSDs don’t bring much to the table and generally shouldn’t be used in the PS5, especially as they may require additional cooling. Check our Best PS5 SSDs article for more information.

Our testing utilizes the PS5’s internal storage test and manual read/write tests with over 192GB of data both from and to the internal storage. Throttling is prevented where possible to see how each drive operates under ideal conditions. While game load times should not deviate much from drive to drive, our results can indicate which drives may be more responsive in long-term use.

For the PS5, we can recommend almost any drive if it meets the PS5’s minimum requirements in any real way. The PX700 significantly exceeds that, so the question is more about value. What does this drive offer that others don’t? For this category, this often means leaning towards drives with heatsinks and/or higher reliability. Reliability is a difficult quality to measure, although a warranty gives some peace of mind. As for heatsinks, you can always add your own. So drawing lines here becomes more difficult.

We would point to our Power Consumption and Temperature section below for some additional support. The PX700, as we will see, runs coolly, which is a big positive for the PS5. Performance-wise, it’s right up there with the other drives and only really behind the Blue SN5100. You don’t have to go with a big brand to get good results here, and the PX700 will serve you well.

Transfer Rates — DiskBench

We use the DiskBench storage benchmarking tool to test file transfer performance with a custom 50GB dataset. We write 31,227 files of various types, such as pictures, PDFs, and videos to the test drive, then make a copy of that data to a new folder, and follow up with a reading test of a newly written 6.5GB zip file. This is a real-world type workload that fits into the cache of most drives.

The PX700 does very well here, and we wanted to give some background on this test and what it means to put that into perspective. Whether you’re copying between drives or copying to and from the same drive, there are some universal principles. One is that there will be a bottleneck, and usually this will be on the write side of things, as drives write slower than they read. Flash memory, in particular, has to erase cells before they can be written again, which leads to some curious behavior under certain workloads. More generally, though, a write has higher latency – with flash again, this is due to the technology and how multi-bit cells usually need multiple write passes – and writes must be acknowledged, while reads just come from usually static media.

If you have drives that are of different classes – NVMe and SATA, or PCIe 4.0 and PCIe 3.0 – then you would expect the slower class of drive to be the bottleneck even if it’s on the read side. This is also universal, going back to the days when HDDs got faster as they got bigger. SSDs also generally get faster with capacity, but flash is so fast that these rules are no longer absolute. This is complicated by other facets of the technology like pSLC caching and TLC versus QLC flash, where with sustained writes you can have dramatic changes in performance. This is a problem because many people buy drives thinking mostly about transfer performance, and when they hit a low that’s nowhere near the box-rated speed, they panic.

Our DiskBench testing takes all of this into account as it’s closer to a real-world workload, including using different file sizes. Transferring a bunch of large files is fast. Copying lots of small files – and any veteran of hard drives will know this from backing up Windows or documents manually – can be very slow, even on SSDs. So we do a mix. Moreover, we’re doing an on-drive copy, which helps rule out external factors. Much of what you do on your system can be cached in system memory, which can give misleading metrics depending on what you’re doing.

What we’re leading up to here is that the PX700 is the fastest drive on the list for reads and not far off first place for copying. Considering it’s up against some of the best budget drives out there, that’s no mean feat. This is, again, a real-world workload and probably more meaningful than something like 3DMark is for game load times.

Yes, the difference between the best and the worst drive for reads is under 10%, but nobody wants to buy a drive and feel bad about how fast it feels. The PX700 feels like a premium drive – the weaker write performance here is real, but most of what you’re doing will be read-heavy – and if you’re building a PC for someone with it over, say, the Samsung 990, they won’t know the difference. That is a common refrain regardless, but it helps underline the value of reliability. People buy Samsung and WD/Sandisk for a reason. Given Goodram’s European basis, there is some reason to celebrate the performance here while also acknowledging it’s not a no-name brand from AliExpress.

Synthetic Testing — ATTO / CrystalDiskMark

ATTO and CrystalDiskMark (CDM) are free and easy-to-use storage benchmarking tools that SSD vendors commonly use to assign performance specifications to their products. Both of these tools give us insight into how each device handles different file sizes and at different queue depths for both sequential and random workloads.

The PX700 has very steady performance in ATTO. We have no real complaints there. If we turn to sequential performance in CDM, we see more of the same. We would mostly look at QD1 reads for that, being a real-world metric, and it’s roughly on par with the NV7400 there. If we turn to random 4KB read performance at QD1, it’s a little bit further down the list but still hangs with excellent drives like the P310. Anything below about 45µs here is solid in our book. This drive should be quite responsive. If we were forced to be critical, we would point out that high QD 4KB write performance is low. Luckily, this is not a real-world workload for a drive of this type.

Sustained Write Performance and Cache Recovery

Official write specifications are only part of the performance picture. Most SSDs implement a write cache, which is a fast area of pseudo-SLC (single-bit) programmed flash that absorbs incoming data. Sustained write speeds can suffer tremendously once the workload spills outside of the cache and into the "native" TLC (three-bit) or QLC (four-bit) flash. Performance can suffer even more if the drive is forced to fold, the process of migrating data out of the cache in order to free up space for further incoming data.

We use Iometer to hammer the SSD with sequential writes for 15 minutes to measure both the size of the write cache and performance after the cache is saturated. We also monitor cache recovery via multiple idle rounds. This process shows the performance of the drive in various states, including the steady-state write performance.

The PX700 starts off in the pSLC cache mode, writing at 5.8 GB/s for over 86 seconds. Speeds are slightly higher for the first two seconds, something we have seen before with this controller, but ultimately we’re looking at a 500GB cache. This is Exhibit A of why we would identify the flash as QLC. 4-bit QLC flash operating in a 1-bit pSLC mode is trading four times the capacity for performance, which means a 2TB drive will generally have a 500GB cache at most. A 3-bit TLC drive could have a larger cache, although there is nothing precluding it from having 500GB, even if this seems hard to be coincidental.

A TLC drive with a smaller-than-maximum-sized cache would probably be able to maintain some speed, though. The PX700 immediately falls into a folding mode – it’s waiting for data to be moved from pSLC to the native flash – with an average speed of 163 MB/s. TLC flash can at times be this slow, but it’s less likely, and especially less likely if it has space to spare. Even the FA200, with the same hardware, is faster. The FA200 does have twice the flash, of course, but the controller is essentially saturated. Rather, the FA200’s cache speed of 5.54 GB/s is slower than the PX700’s – and this could be from having more flash – which means the post-cache speed will be higher. This reinforces the position that the drive is using QLC. Of course, we could have thrown the 2TB HP FX700 into the mix to further prove this point, but we think comparing a 4TB drive is a more valuable approach.

It goes without saying that this is the first real failing of the PX700 in our review. However, it’s expected of a QLC SSD with a massive cache. We don’t consider it disqualifying. You are not buying a budget SSD to hammer it with writes. On the other hand, the 600TBW per TB warranty makes you assume you could do that to some extent. Also, the lower performance here could creep up in a fuller-drive experience. We also can’t exclude the fact that some PX700s come with TLC flash. Which means, taken as a whole, you should assume QLC will perform like the TLC competition 99% of the time.

Power Consumption and Temperature

We use the Quarch HD Programmable Power Module to gain a deeper understanding of power characteristics. Idle power consumption is an important aspect to consider, especially if you're looking for a laptop upgrade as even the best ultrabooks can have mediocre stock storage in terms of capacity and performance. Desktops are often more performance-oriented with less support for power-saving features so we show the worst-case for idle.

Some SSDs can consume watts of power at idle while better-suited ones sip just milliwatts. Average workload power consumption and max consumption are two other aspects of power consumption but performance-per-watt, or efficiency, is more important. A drive might consume more power during any given workload but accomplishing a task faster allows the drive to drop into an idle state more quickly, ultimately saving energy.

For temperature recording we currently poll the drive’s primary composite sensor during testing with a ~22°C ambient. Our testing is rigorous enough to heat the drive to a realistic ceiling temperature but real-world temperatures will vary due to the environment and workload factors.

The PX700 comes back into our good graces with very good results in our power testing. It’s essentially tied for second with the FA200, which is hardly surprising given the hardware similarity. Only the BiCS8-based WD Blue SN5100 pulls away from the pack. We would consider every drive on this list to be sufficiently power-efficient for laptop use, although the 990 is closer to the lower end of that range. Certainly 550 MB/s per watt or higher is an excellent result and indicates a drive that can be run without a heatsink most comfortably. As for idle pull – we test under desktop conditions, which can also match poorly-optimized power settings – the PX700 is not unusually power-hungry and, in fact, better than the WD Blue SN5100. That’s a bonus.

Temperature-wise, we have good news. Goodram touts the graphene label on this drive, which, in our experience, does actually help with cooling, especially on a drive with four flash packages. The PX700 in our testing peaked at 61°C, nearly 30°C below the initial throttling point, well beyond the 20°C of headroom we like to see. The MAP1602 does tend to be a hotspot more than alternatives like the Phison E27T on the P310 and TN470, but a heat-spreading label does help. In our estimation, this is a cool-running drive that would be great anywhere.

Test Bench and Testing Notes

We use an Alder Lake platform with most background applications such as indexing, Windows updates, and antivirus disabled in the OS to reduce run-to-run variability. Each SSD is prefilled to 50% capacity and tested as a secondary device. Unless noted, we use active cooling for all SSDs.

Goodram PX700 Bottom Line

The Goodram PX700 is a model budget PCIe 4.0 SSD. It matches or exceeds all of the expected performance points; it's power-efficient, and it uses a simple single-sided design without a heatsink. The only problem is that a “budget” drive has to be affordable. Current prices in the U.S. for this drive leave something to be desired. However, if it can match or beat the prices of other drives in this class, it’s probably one of the better choices available. In part, this is because we’d be liable to trust Goodram over non-names – the TBW alone shows it means business – and, on the other hand, the drive actually outperformed our expectations. Aside from the sustained write performance, this drive really feels premium. You don’t need a Samsung, WD, Sandisk, or even Crucial drive to feel like you have next-gen storage.

Let’s look at what we mean by that for your use. Game and app loading times are heavily reliant on random and sequential reads at low QD. The PX700 does well there. You also may be transferring files a lot, and the DiskBench results are mighty. For your laptop or HTPC, you might want something that’s power-efficient and, if space is a concern, something that stays cool without a heatsink, and this applies to PS5 use as well. The PX700 ran cool in our test with just the graphene label and was very efficient. You’re not getting a shorter warranty with lower TBW, as with Samsung’s 990 or some QLC-based drives, and we know where this drive is assembled. It’s hard to look at this combination and not feel like it’s a stellar drive if you can find it at a normal price.

Aside from price, our only complaint would be sustained write performance. It’s pretty dissapointing, although we should point out that it’s also expected. The drive also has trade-offs here. Namely, its standard pSLC write performance is better than the 4TB FA200, and in the 2TB HP FX700’s ballpark, so you’re not getting nothing for that later drop. The PX700, in fact, is faster folding than the FX700, too. In the grand scheme of things, this isn’t a big deal, as you should never hit that state – this drive should effectively be fast enough for a 1GbE network, if that’s a bottleneck – and having a bit faster peak performance does translate to better marks on some of our benchmarks. The drive could take a hit in some cases, such as when it's very full, in which case getting a drive with DRAM and guaranteed TLC flash could assuage your fears.

For most use cases, though, the PX700 is more than good enough and, in fact, punches above its weight in our opinion. It would be a good option for just about any type of machine. PS5, laptop, HTPC, desktop, you name it. Primary or secondary drive, just fine. You don’t even need a heatsink. The capacity range is solid – you’re not going to find 8TB on a drive like this – and you can bring your own software. Performance is good enough to overlook any QLC flash use, so our main problem remains the price. Get it down to the same ballpark as the other drives in our list, and you have a contender. We hesitate to put an exact dollar amount for that because SSD prices are volatile, to say the least, so compare it to comparable drives such as the ones we matched it against today.

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Most anyone involved in computing has heard about the P-NP problem, but fluid engineers and mathematicians would love to know if the Navier-Stokes equations have smooth, globally defined solutions. Both questions are part of the Millennium Prize Problems, solutions to which are worth a cool $1 million and eternal renown. OpenAI is claiming that its staff and internal models have solved the conditions of Navier-Stokes solutions set forth in the Millennium Prize. But the company's shouting from the rooftops is being met with a chorus of boos over claims it might have plagiarized the work of a research team that had been toiling on a related, stepping-stone problem for a year.

Tristan Buckmaster (a scientist at NYU) and Levent Alpöge (a member of Anthropic's staff) had been quietly working on proving Euler's equations — another long-standing mathematical problem, and one that is generally acknowledged to be a stepping stone to solving Navier-Stokes.

According to Buckmaster, his work with Alpöge was "a purely personal collaboration, free of any institutional agreements or official involvement by either of our employers." The researchers used Anthropic Claude and OpenAI Codex as assistants, as is apparently now common in the field, to perform busywork (documentation, searching, etc.) as well as running through logic steps. The substantial amount of compute time the project required was paid from Buckmaster's own pockets, too.

The pair worked for roughly a year until August 15, 2026, when it obtained "the blowup results, with smooth forcing, for both Boussinesq and Euler." Buckmaster says the novel approach was based on previous work by Diego Córdoba and Luis Martínez-Zoroa, and he believes Zoroa should be eligible for a Fields Medal.

Although the team was presumably happy with these achievements, Buckmaster said that the LLM-generated proof was "the most horrendous" he'd seen, calling it "AI slop," and meaning to rewrite it for clarity. Nevertheless, they verified it on August 22 using Lean, a standardized programming language designed specifically to verify mathematical proofs.

Come September 3, Alpöge told Buckmaster of rumors going around that Anthropic had solved an important mathematical problem. This almost certainly alluded to the team's work, and some apparently took it to mean the company itself was working on the problem. The rumor-mongers even theorized that the problem that Anthropic had solved was Navier-Stokes. Alpöge further believed that OpenAI had gotten wind of the news.

This prompted Buckmaster to email an unnamed "prominent mathematician" at OpenAI, clarifying that the effort was a personal collaboration between him and Alpöge and was unrelated to Anthropic. The mathematician replied asking for details, saying "it would be useful to avoid competing," and offering OpenAI compute time. After a few days, on September 6, Buckmaster, the unnamed person, and OpenAI's Sébastien Bubeck talked twice, without Alpöge. He was told that OpenAI had proven a finite-time blowup for the forced Navier-Stokes equations, a subset of the problem.

Alpöge asked by text for the precise statement and was told "existence of forced blowup in R³ and T³", and that "the forcing function is smooth option [C] and [D] in Fefferman," referring to one of the four possible categories established by the Millennium Prize, with any one of them being valid as eligible for the prize, but not constituting a full solution for all scenarios, a distinction remarked on by other scientists.

This is where the story becomes interesting. Buckmaster claims that that idea (forced blowup) was exactly the same one his team had "quietly" chosen, and that nobody else he knew was working on it. Perhaps most importantly, he says that that was "not the direction one arrives at in a few days by giving a model the problem statement," indicating that running the general problem through a bot wouldn't quickly reveal that potential approach.

In fact, Buckmaster claims that over the calls, Bubeck ultimately revealed that instead of just AI models and agents with a couple of handlers, there was an entire team of live humans working on Navier-Stokes. The OpenAI team first had the models try to work through easier paths, and the text prompt that generated the Navier-Stokes proof had itself been generated by prompting Codex, with an "insane" amount of computing needed.

Buckmaster then asked when the initial prompt was issued, and OpenAI's response of "in the past few days" did not arrive until "some time" passed. He proceeded to ask if the model "had been trained on, or had access to, our sessions in Codex," and was told by OpenAI that Codex does not access user data. Finally, he asked if the data was used for model training more generally and, crucially, apparently did not get an answer.

OpenAI allegedly offered Buckmaster two options: one, that Buckmaster and Alpöge publish their Euler proof first. The following day, OpenAI would post its Navier-Stokes proof, giving the two priority. The second option was that Buckmaster alone, without Levant, was to write a paper with the Navier-Stokes proof, acknowledging that an internal OpenAI model resolved it. Bubeck was apparently adamant about Levant's removal from the Euler proof, as his employment at Anthropic was "annoying." Buckmaster opted for neither, and told OpenAI that if it chose the first option, he'd go public with his findings, as has since occurred.

This prompted what Buckmaster interpreted as a threat from Bubeck, who asked him "why [he] would ruin [his] career." After Buckmaster asked why that would happen, Bubeck told him, "If you don't want me to be nice, then I don't have to be nice." Bubeck then allegedly reached out to Alpöge, questioning Buckmaster's sanity, to which Alpöge responded with a refusal, pointing inquiries back to his colleague.

The entire story raises pointed questions about what OpenAI (and others) are actually doing with user data collected via its LLMs, despite the toggle switches that are supposed to disable it. Not only has OpenAI neglected to tell Buckmaster whether it used his team's data for training, in its PR about Navier-Stokes, the company says while it "no specific user data was accessed in order to solve this problem," it "cannot rule out that de-identified data derived from their usage of our products helped improve [its] models."

OpenAI's proof still needs to undergo a likely years-long peer review before any party can take the Millennium Prize home. The firm has stated it does not intend to claim it. As for Bubeck, he predictably paints the story in a very different light, but insists that his pushing away of Alpöge is justified on the basis that "it would be inappropriate for an Anthropic employee to author OpenAI's work," a puzzling statement that some could take as meaning a double standard regarding scientific authorship, based solely on corporate rivalry.

For his part, OpenAI CEO Sam Altman claims his team was well-intentioned and cooperative, and supported Bubeck, saying "it was challenging to offer [the same publication options] to Levent." Neither person opted to discuss the matter of whether OpenAI used the research of Buckmaster and Alpöge as training data, or offered any further explanation of why Alpöge didn't deserve credit for his work as an equal to Buckmaster.

Given the groundbreaking nature of this apparent discovery and the ensuing fight for priority that these competing accounts have sparked, it'll likely take quite some time and review before we know whether and how OpenAI or Buckmaster and Alpöge will be credited with this discovery. But given the inter-lab rancor already on display, the process will surely be ugly.

In a statement to Tom's Hardware, TV manufacturer LG has strongly denied recent claims that its smart TVs constantly log and upload data, record audio while in standby, while confirming they scan local area networks for other devices, a feature common in smart TVs and media devices. The statement follows an investigation published online by Gamers Nexus, which claims LG is operating "216,000,000" spy TVs.

Earlier this week, Gamers Nexus published a video on YouTube claiming that LG smart TVs have several security and privacy concerns. The two-hour video claims, among other things, that LG's smart TVs constantly log and upload user data, even when offline or in standby mode. The TVs were also purported to be found scanning Wi-Fi networks, recording audio logs, and sampling inputs for audio and video to identify what users were watching.

The testing was done in collaboration with two security researchers, MrBruh and uturn, and includes packet capture and firmware analysis. The TVs were reportedly seen identifying other devices on local networks, including phones, printers, and more. Perhaps the most significant claim regards the capturing of audio through TV microphones, even when the devices were off, with the report claiming the TVs stored data when offline for retrieval later on.

"The claims made in the recently published video are not true," LG told Tom's Hardware in a statement. "LG TVs process voice data only when the voice button on the remote control is pressed and held, or when a wake word such as 'Hi LG' is recognized after the user has activated the Far-Field voice recognition feature."

LG went on to say that beyond the aforementioned instances, "the TVs do not collect or record ambient conversations." The company further stated that if the wake word is not recognized, then no voice data is transmitted from the TV to the server, and said audio processing for the wake word is done on-device.

The company did admit its smart TV functionality includes scanning for devices on the same network. However, as many observers have been quick to point out, LG says this is a standard function of all smart TVs and other smart home devices.

Automatic content recognition (ACR) is an opt-in feature according to LG, which delivers personalized content recommendations, as well as services and advertisements. The company says that, as standard, ACR data isn't used for advertising purposes without consent.

Addressing some of the claims in more detail, LG stated that its smart TVs don't collect, record, or transmit ambient conversations in the home unless voice functionality has been activated by a user. It further stated that its far-field voice recognition tech (Hi LG) must be activated by a user before the TV starts listening, much like Apple's "Hey Siri" mechanism for iPhone.

LG did confirm that its TVs monitor for Hi LG's wake word while in standby mode, but says that unless the wake word is detected, the audio is only processed locally before being deleted. The company didn't address some of the other claims and vulnerabilities pointed out in the video, such as the storing of transcripts in plain text.

Tom's Hardware has not independently verified either the claims made by Gamers Nexus in its documentary or LG's counterclaims regarding the issue. LG's full statement follows:

LG Statement:

This is to provide LG's position regarding the claims recently raised by the Gamers Nexus YouTube channel.

The claims made in the recently published video are not true. LG TVs process voice data only when the voice button on the remote control is pressed and held, or when a wake word such as 'Hi LG' is recognized after the user has activated the Far-Field voice recognition feature. Other than these instances, the TVs do not collect or record ambient conversations. (If the wake word is not recognized, no voice data is transmitted to the server; the audio processing for wake word detection is performed locally on the device and is immediately deleted.)

Additionally, to provide smart TV functionalities, LG TVs feature the ability to scan for and connect to nearby devices on the same network. This is a standard function commonly provided by smart TVs and smart home devices.

The ACR (Automatic Content Recognition) feature is provided on an opt-in basis to deliver personalized content recommendation, services, and advertisements. If a user does not consent to the applicable optional agreement, ACR data is not used for advertising purposes.

Topic by topic breakdown:

At LG Electronics, we are committed to transparency and secure user experience. To clarify recent concerns and reiterate our commitment, we would like to emphasize the following points:

• Protecting Voice Privacy: We ensure that your LG TV does not collect, record or transmit ambient conversations in your home, unless voice functionality has been intentionally activated by the user.
• Far-Field Voice Recognition ("Hi LG"): This feature must be manually activated by the user before the TV begins listening for the specific wake word. Audio processing occurs only when voice functionality has been activated by the user and a wake work (such as “Hi LG”) is detected.
• Standby Mode Operation: When the TV is in standby mode and appears to be turned off, it only monitors for the wake word if you have previously enabled the Far-Field feature. If no wake word is detected, the audio used for wake word detection is processed locally on the TV, promptly deleted, and is not transmitted to LG servers.
• Transparent and Useful Connectivity: LG TVs can identify compatible devices on the same network to enable features such as device connectivity, content sharing or smart home functionality.
• Proactive and Continuous Security: Data security is a top priority for LG. We continuously monitor our platform and partner with independent security experts to identify vulnerabilities and deliver timely security updates.
• Full Control Over Data and Terms: LG Electronics is committed to providing transparency and rejecting deceptive practices (dark patterns), ensuring users can choose their preferred privacy and consent options directly through their TV settings.
• Consent-Based Activation: To provide a more tailored and convenient experience, LG TVs offer Automatic Content Recognition (ACR) to enhance viewing experiences, Voice Recognition for hands-free control, and Interest-Based Advertising to deliver relevant advertisements. Each feature requires separate explicit user opt-in consent and can be disabled at any time through the TV settings.

Nintendo just aired one of the most important Directs of the Switch 2 generation, showcasing the upcoming Ocarina of Time remake in all its glory. The presentation also brought along the announcement of the first special edition Switch 2 hardware: The Legend of Zelda 40th Anniversary Edition. The console costs $519.99, but there's also an identically themed Pro Controller for $99.99 that you can pre-order right now — either item ships later this year on October 29.

• Pre-order The Legend of Zelda 40th Anniversary Edition Switch 2
• Pre-order The Legend of Zelda 40th Anniversary Edition Pro Controller
• Pre-order The Legend of Zelda 40th Anniversary Edition Carrying Case

The company's first handheld to be adorned in special edition Zelda makeup was the Game Boy Advance SP released in 2004 alongside The Legend of Zelda: The Minish Cap. The first home console to receive the same treatment was The Wind Waker HD edition of the Wii U in 2013. Most recently, we got a Tears of the Kingdom-themed Switch OLED in 2023, followed by the Hyrule Edition of the Switch Lite next year.

Across every handled and home console released, The Legend of Zelda 40th Anniversary special edition Switch 2 will be the 12th piece of Zelda-themed hardware Nintendo has put out. Moreover, it took the original Switch just a few months to get its first limited-edition hardware (Monster Hunter XX Special Pack), but the Switch 2 has clearly taken its time with this design, and it's sure to be a collector's item.

The tablet itself is mostly the same boring black except for a few golden triangles at the back. However, the Joy-Cons feature a beautiful green design with a Tri-Force symbol on the left. The dock is a gorgeous, two-tone golden and black color with a very bold Tri-Force theme across the front and some easter eggs on the back. Even the packaging is supposed to have textured foil cover art. The console will retail for $520 and does not come with any game.

Alongside the console, there's a Switch 2 Pro Controller that looks very similar to the Joy-Cons, with a green and gold theme that combines elements of the Triforce with a crest emblem. It comes as is for $100, but you can also get it with a subtly themed display stand for $110. Finally, there's also a $40 carrying case adorned with Zelda illustrations. You even get a custom, green-colored microfiber cloth inside and a screen protector.

• Pre-order The Legend of Zelda: Ocarina of Time (remake)

Keep in mind that all of this hardware celebrates the 40th anniversary of the Zelda franchise, which already passed earlier this year. It has nothing to do with the upcoming Ocarina of Time remake and, as such, the game is not included in the $520 limited edition Switch 2. Pre-orders for all the items discussed above are open, and they will be released on October 29, 2026. The Ocarina of Time remake drops next month, on November 19, 2026, and will be sold separately for $60.

"AI is grown, more than designed," OpenAI's chief scientist, Jakub Pachocki, said in a new blog post on the company's latest GPT-6 Astra release. Titling the piece "An Alien Mind," Pachocki portrays the latest large language model as something more ethereal and harder to quantify. Jensen Huang calls it AGI, and OpenAI claims it's the best, most aligned model the company has ever released. It's safer to delegate, better at complex work tasks, and it can even beat Portal in just a few hours.

Huang also said that AGI had previously been achieved back in March earlier this year. Artificial Analysis benchmarks suggest Astra is about as smart as Fable 5.1 - though crucially, cheaper on a per-task basis. Astra may well be better aligned than models in the past, and it may well be more capable in specific tasks and specific benchmarks. However, the claims that the model has achieved AGI, or Artificial General Intelligence, suggest an inflection point for the AI industry.

Astra's release comes alongside calls for an industry slowdown, greater government oversight, and controls on the AI industry. Now, OpenAI's Astra raises more eyebrows about frontier-level intelligence.

Trust us, we don't know what we're doing

The tone around OpenAI's Astra release is intriguing. OpenAI's produced a new set of benchmarks, touting bold claims about the model's reasoning capabilities, with the model trained on 100,000 Blackwell GPUs, with more coming soon.

But Pachocki's blog post is much more nebulous. While Huang touts that AGI has arrived publicly, Pachocki says AI can only ever "simulate facets of human behaviour," not recreate it. He describes AI development as an experimental process that often "surprises" developers, with results that are "harder to interpret."

"An aligned AI should act with honesty and integrity, with love for humanity," Pachocki said. He speaks a lot on alignment, and it's encouraging that OpenAI is so keen to embed human moral understanding into its developments. Although OpenAI appears to be doing this more by orienting the model's goals towards a moralistic outcome, rather than helping to intrinsically understand human morality.

It's certainly different to the tack taken by other AI developers, where the likes of xAI's Grok was released with the ability to generate harmful content.

But the timing of Pachocki's warning is a little suspect. OpenAI has faced increasing pressure of late for its models to be more affordable, with Chinese alternatives like Deepseek V4, Kimi K3, as well as Western models like Google's Gemini Flash 3.8 and Meta's Muse Spark 1.3 offering compelling levels of intelligence at a much more affordable price than the frontier models.

It's perhaps telling that even for all its intelligence and alignment pre-training, GPT 6 Astra is notably cheaper to run on the Artificial Analysis Intelligence Index than its chief rival, Anthropic's Fable 5.1 — which still retains the top spot on that Intelligence Index at the time of writing. Though it's 50% more expensive than GPT 5.6 Sol on the same tasks.

It's a researcher, but imagine what it could be

A huge component of marketing from the major AI developers has consistently been grounded in the idea that, as good as the models are now, just imagine how capable they're going to be in the future.

This was very much the underlying tone in Pachocki's breakdown of Astra's design and functions. Although he and OpenAI make broad suggestions about intelligence, and that the likes of Astra could be this new kind of intelligence which we don't really understand but can definitely control and corral, Pachocki ends his post by making it very clear that we aren't there yet.

OpenAI is prioritizing three areas of work with AI, and of late it's really just been trying to make a really good researcher. That's where we're at right now, with Astra representing the latest and best effort to develop that. Then comes the scientific progress, he said, and then everyone gets their own individual, personalized AGI helper.

Intriguingly, though, that seems to suggest that's something that everyone is clamoring for. Outside of the AI-boosting programmers who jump on each new hot model, the larger work comes in helping non-technical users understand the capabilities of these new, powerful AI models.

Tools, research capabilities, drug discovery, and pattern recognition on big datasets that find new insights and improve analytics are all legitimate and useful ways in which an AI researcher can be deployed, but in the near term, most of the general populace just don't want AI to take their jobs, and for it to be less scary.

It's encouraging that Pachocki's blog ends on a similar note of caution.

"We need to find ways to preserve human agency and enshrine an intrinsic value to being human, in a world where most tasks could be performed by AI."

That's key, but intriguingly, he also calls on others to take charge of that effort.

We didn't start the fire

In the aftermath of cost concerns and token usage exploding among more affordable alternatives, Pachocki wants everyone to slow down, and OpenAI wants world governments to be in charge of it.

"I believe that international coordination on future AI development needs to become a top priority for governments around the world," Pachocki said, calling for voluntary slowdowns and hinting that if that doesn't happen, enforcing it may need to come via legislation instead.

"... to ensure that humans remain in control of the future and are not left behind by unchecked progress, brought about by an alien intellect exceeding our own."

The threat of runaway is valid, and the "singularity" moment is a common trope in sci-fi that AI evangelists have been warning about for years. But Astra isn't AGI. Even getting anyone to agree on what AGI even means is hard enough.

Astra is more aligned and wins some new benchmarks, loses some others. It's another improved coding model with some impressive chops.

Astra is not an alien mind. Framing it as an unknowable entity, by the very people who made it, can read as inflammatory, especially in the context of calls for AI legislation from governments around the globe.

OpenAI's post might read like a post from a non-profit, but it very specifically became for-profit last year. With a future IPO looming, slowing down the competition by calling for legislation may be just as effective a strategy as rolling out a new model.

We've seen huge price rises this year for PC hardware, but RAM and SSDs have been especially affected. The global DRAM shortage, mostly caused by AI data center demands hoovering up all of the available stock, has caused prices to skyrocket. That's why this 4TB Crucial T705 SSD, down to $502.51, is a good deal in the current market, especially given its high capacity.

● Check out this deal at Amazon

Looking at Camelcamelcamel's price data, we can see that this drive was hitting almost $700 just a few weeks ago. Several price drops since have meant that the 4TB Crucial T705 is now almost below $500 for the first time in February. Along with a decent price drop, this Crucial drive is now the cheapest Gen 5 SSD you can pick up, with the next-cheapest model, the Samsung 990 Evo Plus, $60 more expensive at the moment.

It's not even a fair comparison, too, as the entry-level 990 Evo Plus only supports PCIe 5.0 over two lanes, not four, meaning you're only getting Gen 4-equivalent speeds. The Crucial T705 is a high-performance drive that's much faster, and right now, much cheaper.

SSDs that offer Gen 5 speeds are a real step up over their slower Gen 4 counterparts. The T705 will offer you sequential read and write speeds of 14,500 MB/s and 12,700 MB/s, which is significantly faster than the fastest Gen 4 SSD on the market, the Samsung 990 Pro, which taps out at 7,450 MB/s. The T705 ships with 232-Layer Micro TLC memory modules and uses the Phison E26 controller.

It's also one of the fastest you can buy, period, rivalling the WD_Black SN8100, which hits speeds of 15,000 MB/s. That drive will set you back $659.99 right now, too, a significant increase compared to the deal on offer here. The Crucial T705 drive scored an Editor's Choice award for its sustained read/write performance at high speed, along with 4.5 stars in our review.

While we wished that SSD prices were back down to what we saw 18 months ago, it's good to see that high-performance drives like this one can still drop in price. The $502.51 sale price for this Crucial T705 4TB SSD is a seriously good one in the current market, and you won't be able to beat it for speed or price. Grab it at this price while you can.

Phanteks is a Dutch company, founded in 2007 and best known for enclosures, alongside air coolers, AIOs, and a steadily expanding catalog of accessories. Power supplies came later, and they have always been commissioned work. As with most such companies, Phanteks does not own a factory, and the identity of the power supply inside a Phanteks box has changed more than once. For example, the original AMP series from 2020 was a Seasonic Focus derivative, a genuinely strong platform that earned the AMP series a significant reputation. The AMP GH is the current mainstream range, and it is a different animal.

The platform of the AMP GH 750W that we are reviewing today comes from Helly Technology, the Guangzhou outfit founded in 2008 that has spent the last few years building the mid- and upper-tier units. Young by the standards of this industry, but no longer an unknown quantity. The series spans 650W to 1200W across Gold and Platinum tiers, in black or white. We take a closer look at the AMP GH 750W to see how it ranks against our best power supplies list. Everything in it is ATX 3.1 and PCIe 5.1 compliant, fully modular, and cooled by a single 120 mm fan that stops entirely at low load. The 750W model reviewed here is the volume seller of the range, aiming squarely at the mid-range gaming build.

Specifications and Design

In the Box

The AMP GH 750 arrives in a white cardboard box with mint-green edges and sides, the product name in large type across the front, and the 80 PLUS Gold badge in the top corner. Three black-and-grey strips along the bottom edge carry the three main headline claims: ATX 3.1 PSU, PCIe Gen5.1 support, fully modular. That is about the extent of the marketing. It is a restrained, we dare say almost clinical presentation next to what most of this segment ships in.

Inside, the power supply sits between molded protective inserts, with the cables bagged separately. The bundle is far better than the price suggests and one of the most complete we have seen this year. It comprises of the AC cable, four chrome mounting screws, a very generous bunch of white zip ties, a few black and white Velcro straps, and a bag of white cable combs. There is also an installation guide and manual that are shared across the whole series.

The cables themselves are the highlight. Every wire is individually sleeved in white braid rather than the flat ribbon type this class usually gets, and they stay flexible enough to be easily routed. Even the 12V-2x6 signal wires are, creating a very high-quality aesthetic result. They do take up a little bit more room behind a motherboard tray than flat cables would, but we are certain that this is a sacrifice builders are willing to make. The 12V-2x6 cable is marked 450W on the connector housing and uses mint-green latch tabs, which do double duty as a seating indicator (if you can still see green, the plug is incorrectly seated). The 12V-2x6 and ATX 24-pin cables have cable combs pre-installed.

External Appearance

At 140 x 150 x 86 mm, the AMP GH 750 is about as short as a modern ATX unit gets, which means it will fit anything that takes a standard supply and leave room for cable slack in front of it. Our sample obviously is the white version, and the finish is the first thing worth commenting on. It is a clean, even satin snow white with an exceptional finish. This is not a given with white units, as they can easily look a little grey next to a truly white item/case. This one does not. Phanteks also sells the unit in black, and at MSRP, the black version is $10 cheaper.

The bottom carries a fan grille cut as a field of staggered rectangles rather than the usual honeycomb, with the company logo embossed across the middle and four polished exposed screws at the corners. The side panels get the company and series logo, as well as a scattered pixel-dot pattern in grey. The specification label is found at the top. The specification label deserves a read before purchase: 62A on the 12V rail for 744W, 100W combined on the minor rails, and a separate line stating 450W for the 12V-2x6 output. That is a hard limit on what you can hang off the single native Gen5 connector, and it is printed on the unit and on the connector rather than buried in a datasheet, which is at least honest.

The rear face is somewhat straightforward, with an AC receptacle and a rocker switch, with the only difference being that these are all white. Even though one is pictured in the manufacturer’s website, there is no switch for the semi-passive mode, so the fanless mode is automatic and not something you can override in either direction. The front of the unit hosts the connectors for the modular cables. The top row starts with the dedicated 12V-2x6 socket and continues with five shared 8-pin sockets marked CPU and PCIe. The bottom row has three sockets for the SATA and Molex cables, then two for the motherboard cable.

Internal Design

Cooling comes from a 120 mm Yate Loon D12SH-12, a 12V/0.3A sleeve-bearing fan rated for 2200 RPM in Yate Loon's catalog. Yate Loon is a long-standing name, and the D12SH has been turning up in power supplies for years, but a sleeve bearing is a sleeve bearing - it is the cheapest part of an otherwise decent bill of materials. The fan is white to match the chassis rather than the usual black part hidden behind a grille.

The platform is Helly's, and the layout is tidy. We have proper heatsinks rather than basic aluminum plates, sensible component spacing, and a restrained amount of glue holding the taller parts down. Transient filtering begins at the AC receptacle and continues on the main board, totaling three X capacitors, four Y capacitors, and two filtering inductors. Two bridge rectifiers sit on their own dedicated heatsink, which is more than this class usually gets.

The APFC stage uses a pair of Oriental Semiconductor OSG55R190F MOSFETs, sharing the long heatsink with the primary side inversion switchers. This is a common cost-and-space-saving arrangement but hardly matters when the heatsink is large enough. The bulk capacitor is a Nichicon rated 560 μF at 420V and 105°C and the choke beside it is on the small side and unshielded. Primary inversion is a half-bridge LLC resonant arrangement driving two PIP PTA25N50 MOSFETs, which is the standard and entirely sensible topology for a 750W Gold unit.

On the secondary side, four Fuxin FXN0204C MOSFETs generate the 12V rail through synchronous rectification, and the 3.3V and 5V rails come off two vertical DC-DC daughterboards standing near the modular connector board. Capacitor sourcing is the pleasant surprise, with the secondary electrolytics coming from Rubycon and Nippon Chemi-Con and APAQ polymers filling the rest. That is a fully Japanese set of capacitors on a unit that sells for under $90, and it is the single strongest argument that the 8-year warranty is more than a marketing number.

Cold Test Results (25°C Ambient)

Cold Test Results (25°C Ambient)

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

At 25°C ambient, the AMP GH 750 averaged 91.0% at 230 VAC and 89.0% at 115 VAC across the full load range, peaking at 92.3% (230 VAC) at around 300W and holding 90.8% at a sustained 750W. The curve is well shaped. It climbs fast, is stable between 250W and 450W, and loses very little ground at the top end, which is exactly the profile you want from a PSU that will spend its life between 30% and 70% load. At 115 VAC, the equivalent figures are 90.6% at peak and 87.5% at full load. The 80 PLUS Gold asks for 87% at 100% load on 115V, so the margin is half a point. It is a pass, but our numbers land roughly one point below the CLEAResult published certification report, which is dated 4/8/22 and lists the same PH-P750GH_01 model code. That certificate predates the ATX 3.1 revision of this unit by some margin, so it is describing an earlier configuration sharing the model number.

Acoustically, the cold run is the unit's best showing. The fan stays completely stopped up to nearly 300W, which is almost 40% of its maximum rating, comfortably wider than the 30% Phanteks claims. It will easily cover any system idling and even performing medium-power tasks as well. It comes on at 31.6 dB(A) and climbs gently, crossing 38 dB(A) only past 590W and topping out at 43.2 dB(A) at a sustained 750W. That is audible in a quiet room but not intrusive, and you have to be pushing a 750W load through a 750W supply to get there. Thermals are unremarkable but in a good way, with both the exhaust and internal temperatures well within the acceptable margins while the fan is largely idling. Nothing here is working hard.

Hot Test Results (~45°C Ambient)

Hot Test Results (~45°C Ambient)

Inside the hotbox at roughly 45°C (five degrees past the unit's own 40°C rating), efficiency degraded far less than we expected. The 230 VAC average fell from 91.0% to 90.3% and the full-load figure from 90.8% to 89.8%, a single point for a twenty-degree increase in ambient. At 115 VAC, the average went from 89.0% to 88.3%, with full load dropping to 86.5%, which does slip under the 80 PLUS Gold threshold. That threshold is defined at 23°C ambient, so what we have here is a note rather than a failure. Full-load losses rose from 69W to 76W. This is a better hotbox result than most mainstream Gold units manage, and it says something reassuring about the heatsink sizing and choice of components.

Heat makes little impact on the unit’s electrical performance, but noise is where it makes itself felt. The fanless window narrows from about 300W to roughly 220W, and from there the ramp is much steeper than it was at room temperature: 38 dB(A) at 450W, 46 dB(A) at 600W, and 50.2 dB(A) at a sustained 750W. That is loud, and the sleeve-bearing Yate Loon is working hard. The trade-off is that internal temperatures stay very well controlled, so the fan is doing real work rather than spinning for the sake of it, with the designer obviously favoring reliability over acoustics when ambient conditions are tense. Even though sustained full output in a 45°C environment is outside this unit's specifications, it handled it without complaint and without shutting down.

PSU Quality and Bottom Line

Power Supply Quality

Voltage regulation is the standout result of the whole test. Across the 20% to 100% load span, the 12V rail moved by just 0.4%, an enthusiast-tier tightness on a sub-$90 unit and better than several platforms costing twice as much. The 3.3V and 5V rails came in at 1.2% each, which is ordinary but perfectly fine given DC-DC conversion and the negligible loads modern systems put on them. There is no sign of the minor rails dragging the 12V line around under uneven loading.

Ripple suppression is equally comfortable. The 12V rail peaked at 38 mV under full load against the 120 mV ATX limit, leaving nearly 70% headroom, while the 3.3V and 5V rails topped out at 24 mV against their 50 mV limits. Nothing in this data set gets anywhere near a limit, with the Japanese capacitors on the secondary clearly backing their reputation.

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

All protection mechanisms were verified and triggered correctly during testing. With the unit hot, OCP tripped at 150% on the 3.3V rail, 148% on the 5V rail, and 138% on the 12V rail. OPP shut the unit down at approximately 1050W, or 140% of rated output. Those are loose thresholds and generous enough that a genuine fault has room to develop before anything intervenes. However, these are not unusual for the ATX 3.1 class, and every protection engaged cleanly when it should have.

Bottom Line

The AMP GH 750 is a better PSU than its price implies at the time of this review, but the reasons are not the ones Phanteks leads with. Phanteks spent considerable effort and resources on the aesthetics of the unit, leading their marketing campaign with the appearance and quality of the wires. It is undeniable that the cables of this unit have been exceptionally well designed and made, with extreme attention to quality and detail. The same is true about the unit itself, the construction and appearance of which are immaculate and attractive.

While unquestionably important, aesthetics can only take you so far when we are talking about any tech equipment. What makes the AMP GH 750 really worth buying is the bill of materials and the electrical results. Japanese capacitors throughout, amazing regulation on the 12V rail, and ripple that never gets within two-thirds of the ATX limit. Helly is not a famous name amongst enthusiast cycles, but this is a well-executed platform, and the 8-year warranty behind it looks like a considered commitment rather than a gamble.

The measurements may look indifferent, but the results are honest. Efficiency clears 80 PLUS Gold at both input voltages and holds its curve well, but that level of performance is hardly stellar by today’s standards. Our numbers sit about one percentage point below the CLEAResult certification report on file for this model code, a report dated 2022, which predates the ATX 3.1 revision the unit ships as today. There are no Cybenetics or PLPP certifications at the time of this review. We feel that two things hold the AMP GH 750 back. The first is the 12V-2x6 connector's 450W rating. On a 750W unit, that is not fatal; anything you would sensibly pair with 750W stays under it, but anyone planning to move a heavier card into this system later will be facing a limit. The second is the fan. The Yate Loon D12SH-12 is a sleeve-bearing part that does not match the eight-year warranty's ambition.

At $109.99 MSRP for the white unit and $99.99 for the black, and at the time of writing, $84.99 and $89.99 respectively at Newegg, both landing at $74.99 after Phanteks' mail-in rebate, the value case is straightforward. Under $80 buys you Japanese capacitors, near-flat 12V regulation, a 300W fanless window, and 8 years of warranty. The compromises are a connector limit you should check against your GPU plans, and a fan that will remind you it exists in a warm case. For a mid-range build that lives between 200W and 500W most of the time, which is exactly what a 750W supply is for, that is an easy recommendation.

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A few years ago, Nvidia was expected to release the RTX 3070 Ti with as much as 16GB of VRAM instead of the 8GB we actually ended up getting. Only database listings ultimately indicated its existence until a physical prototype showed up, but we never really got to see it working. Fast forward to today, however, and Brazilian YouTuber fmklab, aka Fabian, has recreated the card on his own. Using an RTX 3070 PCB with a transplanted 3070 Ti core and GDDR6 chips from Samsung, fmklab broughall those parts together to achieve a 16GB memory pool.

The card was stripped down to its PCB, and the RTX 3070 GPU was removed and replaced with a 3070 Ti chip. The reason for the swap is that the RTX 3070 uses GDDR6 instead of the GDDR6X the retail version of the RTX 3070 Ti is equipped with, which presented an interesting challenge.

Fabian could've put all 16GB of GDDR6X memory on a 3070 Ti card, but it would've been far too expensive, so he swapped the Samsung-made HC14 (8Gb) GDDR6 chips that were already on the board with HC16 (16Gb) chips. That changed the VRAM capacity from 8GB to 16GB, but when the card booted, GPU-Z only recognized 8GB. The PCB's memory straps needed to be reconfigured in order for it to use the entire memory pool.

Using the Nvidia BIOS Reader tool, Fabian figured out exactly what straps to adjust and went ahead with the soldering process. Afterward, MATS properly recognized the full 16GB VRAM capacity with no errors. GPU-Z also showed the RTX 3070 Ti equipped with 16GB of GDDR6 memory, similar to the prototype that popped up years ago. But verifying that the card recognized the additional VRAM was just the first step.

To actually test his creation's performance, Fabian ran a local LLM on the GPU to see if it would allocate more than 8GB and found that it used nearly the entire 16GB pool. Call of Duty: Warzone ate more than 13GB of VRAM while paired with a Ryzen 7 9800X3D, exhibiting no signs of instability.

Fabian even linked a Unigine Superposition benchmark in the video's description as proof of the card's existence, where he scored 5,367 points, which is actually lower than most real 3070 Ti runs. That's probably because of the decreased memory bandwidth of GDDR6 versus that chip's stock GDDR6X, which is the biggest tradeoff for the higher capacity.

Overall, this was a successful experiment and possibly the only working 3070 Ti 16GB in the world, but there comes a point of diminishing returns. A 3070 Ti is still a powerful card, but it gets smoked by current-gen Blackwell options even with less memory. It would be much more reasonable to buy, say, an RTX 5060 Ti 16GB if you actually need better performance with some future proofing today, not to mention the superior efficiency, feature set, and after-sales support you'd get from a new Blackwell card.

Anyone keeping track of world news in the early 2010s, and reports on tech in particular, has probably heard about Stuxnet. That malware spawned a large number of conspiracy theories — with the kicker that some of them were actually true. The malware targeted Iranian nuclear facilities and is believed to be the first digital worm to cause direct physical damage in meatspace. An unknown security researcher has now published a source code reverse-engineering of Stuxnet in all its glory.

The worm's ultimate target, allegedly a successful one, were industrial controllers from Siemens that were reportedly used in Iranian's Natanz nuclear enrichment plant. Once it reached the target, Stuxnet's payload manipulated the frequency converters in industrial centrifuges, in a bid to subtly damage the rotors — all while keeping the plant staff in the dark by reporting normal operation.

The repository contains build instructions so interested techies can try it out for themselves and learn all about its inner workings. You'll need a Windows XP or Windows 7 virtual machine, and for obvious reasons, you shouldn't configure any network connectivity for it. To witness the full effects of the payload rather than just the spreading mechanisms, you'll need the appropriate Siemens software, and ideally hardware — though we figure that industrial-scale centrifuges aren't exactly common in techies' cable drawers.

In its heyday, Stuxnet spread via three mechanisms. The primary infection vector was USB sticks with Windows shortcuts and autorun.inf files. Upon plugging one of those sticks in, just viewing the drive's contents would immediately trigger infection thanks to a zero-day vulnerability.

Infected systems then autonomously tried to spread the worm further via the network using a zero-day Windows Print Spooler vulnerability that would let an attacker write system files into any machine sharing a printer. It would also copy itself into accessible network shares. To evade Windows driver signature checks, Stuxnet used two digital certificates stolen from Realtek and JMicron.

The worm also had code to inject itself into Siemens software, by way of the WinCC SQL Server database, and embedding its code in Step 7 project files that automatically ran when engineers opened them. Since those files were almost guaranteed to be shared among more than one engineer, it made for an excellent internal infection vector that didn't depend on having network share control.

The final step was taking charge of the DLL that communicated with the actual centrifuges and injecting malicious code into the PLCs (Programmable Logic Controllers) of those machines to stealthily mess with the rotors.

Stuxnet was part of Operation Olympic Games, an alleged coordinated effort between the U.S. and Israel to try and curb Iran's purported progress in creating nuclear weapons at its Natanz facility. The initiative seemingly ran under both the Bush and Obama administrations, and was supposedly a way to dissuade Israel from launching its own preemptive strike against Iran. The software was allegedly developed by both the Pentagon and Israel's Unit 8200, and was reportedly successful in bringing down about 10% of Natanz' centrifuges by ultimately seriously damaging their rotors.

However, the worm had a nasty bug: it didn't have sufficient checks about which environment it was in, and failed to notice it was no longer in a local network environment. When engineers took their laptops home, it escaped out to the internet at large, at which point security researchers worldwide let out a collective "huh, that's odd" and proceeded to investigate. Mercifully, the worm contained a hard-coded self-destruct date set for June 24, 2012.

Patagonia, a region of Argentina with 17,300 glaciers, has the potential to turn into a data center hotspot. The cool climate and abundant energy resources there are drawing interest from the likes of Amazon, Google, and OpenAI, according to Reuters. However, there needs to be more work on tapping into the resources and equipping high-speed networking, like fiber, to make the place irresistible. There’s also the question of political stability with a presidential election next year.

Argentina, and Patagonia in particular, has some juicy lures for potential hyperscale data center operators, and the cold climate is a bonus. That environment helps keep data centers cool and use less power while maintaining agreeable thermals. Patagonia has tens of thousands of glaciers across the Northern Patagonian Ice Field and the Southern Patagonian Ice Field combined.

Another bonus to would-be Patagonian data center operators are the range of energy sources there. Reuters mentions hydroelectric power, wind, and shale gas, but some operators who are already established are also adding solar.

Then, the social and political climate looks good for data centers in Argentina. According to the source, the populace doesn't hate data centers yet. At least there seems to be no campaign groups or public backlash. It is also reported that President Javier Milei has been making changes to court Big Tech. That’s a positive, and may be a negative. Positive in that the Argentines are now being accommodating to the needs of huge AI data centers. Negative, as there’s a presidential election next year, where things could take a dramatic turn, or not. There are also some regions in Patagonia where data center expansions may be complicated by the wishes and needs of indigenous communities. Businesses offering sizable, long-term investments aren’t keen on uncertainty.

With the pretty good environment for hyperscale buildouts established, it isn’t that surprising to see Reuters say OpenAI has confirmed a $25B and 500MW clean energy data center in the country. Lesser-known (to us) companies like Poland's Green Capital also plan a $3B and 300MW data center in Patagonia. It is also reported that FlexDomes, a U.S.-based green data center company, is planning the first stage of a 120MW facility.

Meanwhile, in neighboring South American countries, there are large developments from the likes of Amazon, Google, and others. Argentina must have these data center whales in its sights, as they are known to be scouting for expansion into new locations.

As well as political / business risk, Argentina has some structural weaknesses that need to be addressed by those developing in the country. It is said to have connectivity and power grid infrastructure deficiencies, for example. In other locations, we have seen these addressed on almost a per-project basis, with some state intervention, but it adds some high costs to setting up.

Anthropic safety researcher Evan Hubinger has warned there is a more than 10% chance that AI could kill all humans within the next decade, but reassured the public the company is "trying its best." The revelation comes following Jacob Coxon's public resignation from the company, where he stated that neither OpenAI nor Anthropic is acting responsibly, accusing both companies of gambling with human lives in the pursuit of self-improving super-intelligence.

"I resigned from Anthropic today. I spent the last three years doing pretraining research at both OpenAI and Anthropic," Coxon said in a tweet Wednesday. "Neither company is acting responsibly. They are racing straight to self-improving superintelligence and gambling with our lives."

Expanding on his thoughts, Coxon warned readers not to underestimate the powers of AI, which he says will soon be "superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources."

Coxon went on to state that the people who are building AI "earnestly believe that it could kill us all by the end of the decade," and further warned that executives and researchers — rather than playing up fears around AI, as some have accused — are actually restraining themselves, expressing much more candid views in private.

Evan Hubinger, a security researcher at Anthropic who hasn't departed the company, chimed in to state, "Jacob is correct here," in no uncertain terms. "We really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to."

Hubinger casually admitted that Anthropic is not only worried about the prospect of AI wiping out humankind, but that it currently doesn't have the tools to stop such an eventuality.

The revelations follow increasingly alarming reports of AI agents acting up during testing, escaping sandbox environments, and even collaborating with each other in order to cheat benchmarks and tests, or figure out problems without human oversight. OpenAI recently admitted its agents were discovered using a programming hub to communicate with each other, while earlier this year an OpenAI agent went rogue and hacked popular AI community Hugging Face. Further revelations about the incident revealed AI agents were on the loose on the open internet for several days, with details revealing the AI models broke out of their testing environment using thousands of individual actions, even collaborating.

Coxon, the resigning researcher who kicked off Wednesday's revelations, concluded by warning researchers to consider the next few years, urging them to call for "different conditions" in which to pursue superintelligence.

For years, TSMC has tried to avoid making public comments about its plans to use EUV lithography with a 0.55 numerical aperture optics, or High-NA EUV, because the company's developers had a good idea how to keep advancing process technologies without using $400 million scanners. However, TSMC cannot rely on Low-NA EUV systems forever, so this week the company announced plans to use High-NA EUV starting from 2030.

TSMC did not formally reveal which fabrication technology will be the first to adopt High-NA EUV, though the year 2030 points to a few candidates. What TSMC did say is that it expects the number of layers processed using High-NA EUV to eventually increase as its fabrication technologies become more complex, driven by increasing complexity of transistor architectures, which is probably an implication for more sophisticated implementations of gate-all-around (GAA) transistors as well as complementary field-effect transistors (CFETs) later on.

TSMC plans to start using High-NA EUV lithography tools for high-volume manufacturing in 2030 using conventional 6×6-inch photomasks. The company then plans to build a pilot line that uses 6×12-inch photomasks in 2031 with the goal of bringing 6×12-inch High-NA lithography systems into advanced node production by 2033.

High-NA EUV lithography tools can achieve an 8nm single-exposure resolution, as opposed to a 13nm single-exposure resolution offered by today's Low-NA EUV litho systems. However, when used with conventional 6×6-inch photomasks, High-NA EUV scanners have only half the exposure field of their Low-NA counterparts, which creates challenges for manufacturing very large dies. As a result, chipmakers building massive AI accelerators must either stitch multiple exposure fields together or adopt multi-chiplet designs, two approaches that have their own other challenges, such as tool productivity and power consumption. To circumvent the 6×6-inch photomask limitations, TSMC is working with ASML to set the stage for 6×12-inch photomasks.

Changing the size of photomasks is not a trivial endeavor as it requires changing everything from EDA software to tools that produce and write masks as well as systems that handle them, which essentially means that the entire industry must work on this change. ASML seems to be optimistic about the transition as it is supported not only by Intel and TSMC, but also by Samsung.

"We expect the adoption of High NA EUV to increase progressively along the device scaling roadmap, first using current 6-inch masks and then further supported by 12-inch masks, which enable greater scanner productivity and allow the industry to meet the demand for smaller, faster and more energy-efficient chips," said Christophe Fouquet, president & CEO, ASML. "We are pleased by the strong initial support of semiconductor manufacturers, mask suppliers and partners for this initiative."

Perhaps the biggest intrigue about TSMC's usage of High-NA EUV lithography is which process technology will be the first to use the new systems. Based on what we know about TSMC's roadmap, A10 or A11 (1/1.1nm-class) seems to be by far the strongest candidates to use High-NA EUV scanners for the most critical layers. TSMC's latest strategy separates its roadmap into annual client-oriented nodes (N2, N2P, N2X, A14, A13) and roughly biennial high-performance nodes (A16 in 2027, then A12 in 2029). The company has already confirmed that A12 and A13, due in 2029, will continue to rely on conventional EUV lithography.

Since A13 is an optical shrink of A14 that increases transistor density by only 6%, with performance and power improvements yet to be disclosed, its successor in 2030 will likely have to deliver considerably more substantial gains. It is therefore reasonable to expect A13’s successor — whether it is called A11 or A10 — to adopt more advanced lithography and/or TSMC's 3^rd Generation nanosheet GAA transistors to deliver significantly higher transistor density as well as meaningful performance and power improvements over its predecessor. Yet, we are of course speculating.

Intel is reportedly set to hike CPU prices by 10%, according to a new Digitimes report. Citing supply chain sources, the outlet says the increase follows two others, one in the first quarter of 2026 and another in July, among some server and client CPUs. Notably, the sources didn't say which products the price increase applies to, though presumably, the increases would come through Intel's mobile and server businesses before desktop client. Citing industry sources, DigiTimes also reports that Intel is set to launch "major annual products" in March 2027, with AMD following up with launches of its own between June and July.

The increases come on the back of Intel seeking higher gross margins for its products as the PC market shrinks. This is a story we've heard directly from Intel in the past. In its most recent earnings call in July, Intel chief financial officer David Zinsner attributed a 13% YoY increase in Intel's client revenue to higher average selling price, not a higher volume of sales.

Although the Digitimes report doesn't clarify which products will see a price increase, server and mobile seem like the most likely candidates. Intel's most recent Panther Lake calls for high-speed LPDDR5X-7467 memory as a minimum, and last-gen Lunar Lake CPUs have on-package memory. Naturally, higher memory prices put more pressure on fully built systems like laptops more so than socketed, standalone desktop processors.

On the server end, there's been an unprecedented increase in demand for server CPUs on the back of agentic AI workloads. That demand led to several consecutive records for Intel's share price, even without any major product announcements. Earlier in the year, Wall Street estimated the server CPU market would rise to around $120 billion by 2030 (currently around $30 billion). Now, those projections go up to as high as $220 billion.

According to the report, Intel is set to launch a major new annual product in March 2027, followed by AMD between June and July. Last week, a leaked Intel roadmap showed the company's next-gen Nova Lake desktop CPUs entering mass production in Q4 2026 with a release in Q1 2027, lining up with DigiTimes' report.

Although the timelines line up, the rumor mill has suggested an early Q1 launch for Nova Lake. It's worth noting that the DigiTimes report doesn't make mention of which product Intel will launch in March. This year, for instance, Intel launched its Xeon 600 CPUs for HEDT in March.

Perhaps more interesting is the AMD timeline. We already know of one major AMD product launch in the second half of 2027, which is Venice-X. Those are Zen 6 server CPUs with AMD's 3D V-Cache, packing up to 1,152 MB of L3 cache on the chip. Otherwise, that timeframe seems to point to AMD's next-gen desktop CPUs with the Zen 6 architecture, codenamed Olympic Ridge.

AMD launched its Venice server CPUs earlier this year, the first sporting the Zen 6 architecture. We haven't heard anything official about Zen 6 in the desktop yet. That's strange given AMD's last several releases. There was about a two-year gap between Zen 3 and Zen 4, as well as Zen 4 and Zen 5, on desktop. We've just crossed the two-year mark for Zen 5, so assuming AMD keeps a similar launch cadence, we'd expect to hear something sooner than June or July or next year.

That same explosive demand in server CPUs could have changed AMD's launch plans, however. Given that we haven't heard anything official about Olympic Ridge at this point, a launch in June or July isn't out of the question.

Alibaba’s Qwen 3.8 27B open-weight AI model came out a couple of weeks ago, and it immediately created a wave of hype among local AI enthusiasts thanks to its impressive intelligence benchmark results for a model of its size and capabilities.

Totaling around 17GB for four-bit quantized weights and offering built-in multimodal capabilities on top of its general aptitude, Qwen 3.8 27B immediately grabbed the attention of everybody with an RTX 5090, RTX 4090, or RTX 3090 (as well as a Radeon RX 7900 XTX, Radeon AI Pro R9700, or Arc Pro B70).

Were we on the verge of frontier-level intelligence from a four-bit quant on a single graphics card? Could everybody with a capable enough local AI setup go and cancel their Claude or ChatGPT subscriptions?

The answer, of course, as with every open-weight AI model hype cycle, is more complicated than just eyeballing the size of the model weights and comparing it to your available VRAM pool. Does the card or system you're using to host the model have enough VRAM left over to provide useful amounts of space for the model's context once everything is running? Do your host system and LLM inference engine deliver acceptable time-to-first-token, as well as high throughput beyond just bench-racing from an empty context window?

It's one thing if you just want to chat with a model and see what happens; it's another entirely if you want to put it to work, especially as impatient agents take the limits of human perception out of the picture.

We wanted to see what hardware and software stack Qwen 3.8 27B really wants in order to deliver solid performance, so we ran it on systems ranging from a desktop PC with discrete GPUs to systems with unified memory architectures like the DGX Spark, Mac Studio, and Ryzen AI Halo.

Our discrete GPU AI testbed includes the following components:

Where it was possible to do so, we tested performance with Qwen 3.8 27B’s built-in multi-token prediction capabilities both enabled and disabled. Not all of the model runners we tested were able to support MTP within the amount of VRAM available to us on some of our platforms. We note where MTP was and wasn’t possible in our analysis of each platform, as well as in our charts.

RTX 5090 performance

We started with the RTX 5090, whose 32GB of GDDR7 and 1.8 TB/s of memory bandwidth would seem to make it an absolute no-brainer for getting the best local inference performance with this dense model. (Mixture-of-experts models tend to be friendlier to performance on lower-end hardware like the DGX Spark and AMD's Strix Halo, as their limited numbers of active parameters mean less data movement during inference).

As a baseline, we followed our usual local AI benchmarking approach: grab the latest build of llama.cpp from GitHub, build it, grab an Unsloth quantization of the model from Hugging Face, and run it. But our testing quickly ran into a speed bump.

Although llama.cpp will happily allocate the full 262K context length with this model on an RTX 5090, its processing speeds at long contexts on this card are dire.

Time-to-first-token with a single 5090 stretches to roughly 30 minutes, suggesting that something is just broken here. And tokens-per-second throughput drops far, far below what you would expect for having one of the world's fastest graphics cards at your disposal. No matter how you slice it, llama.cpp is not the right model runner for this hardware right now.

Next, we tried vLLM, a production-grade inference engine that's more at home in the data center than it is on the desktop, although it can comfortably serve in both roles—at least if your host system is up to its requirements. Even with 64GB of main memory in our test rig, we had to allocate another 64GB of swap just to let vLLM load Qwen 3.8 27B successfully for the first time. A lightweight stack this is not.

The vLLM maintainers provide an NVFP4 quantization of Qwen 3.8 27B and deployment recipes for both one and two RTX 5090s. We just so happen to have two RTX 5090s in the TH labs, so we were able to try out both configurations.

Serving Qwen 3.8 27B on one 5090 with vLLM certainly works in a pinch, but it's not ideal for long-context inference because the base recipe for it limits you to just a 32K context. To get the full 262K context, you really want a single card with more memory (like an RTX Pro Blackwell card with 48 or 72GB of RAM) or two 5090s, as we were able to test.

And a single card doesn't have enough memory to enable Qwen 3.8 27B’s built-in multi-token prediction (MTP), which is super helpful in getting faster decode performance from this setup. 20 tokens per second across the board without MTP is not an impressive baseline for a card of this caliber.

Get two 5090s into the picture, though, and decode speeds rocket upwards for vLLM (albeit at a high cost to prefill). 70-80 tokens per second across the context depth sweep is a fantastic result for a local setup, and TTFT remains fairly reasonable. But we can go faster.

Enabling MTP with vLLM gets us to 100-110 tokens per second on the decode side for only a small hit to prompt processing speed. This setup provides consistent performance at prompt processing speeds that don’t make you question whether something has gone seriously wrong. But it ought to be fast, because our dual RTX 5090 platform as tested here would currently ring in at over $13,000.

We also tried the SGLang inference engine on the RTX 5090 across similar configurations as we did with vLLM.

SGLang is much faster on a single 5090 for some reason – almost 3x faster than vLLM’s single-5090 recipe – and also ekes out a bit more context (37,740) versus vLLM. But if you want to get the full 262K that the model natively supports, you still need a second card or a different one with more VRAM.

Like vLLM, SGLang supports tensor parallelism across multiple GPUs, so enabling dual-GPU inference is as simple as adding another launch flag. And as with vLLM, there are a number of speculative decoding strategies you can add to the recipe to enhance output performance.

The takeaway from this first phase of testing: if you have a single RTX 5090 and don't need long-context inference from it, you can certainly get usable performance from one with this dense model. But you need to choose your model runner carefully.

And if you want the full context window, reasonable prompt processing times, and high throughput from Qwen 3.8 27B all at once, you really want a graphics card with more than 32GB of VRAM as a starting point (or multiples).

RTX 3090 and RTX 4090 performance

With the RTX 5090’s behavior settled, we turned to some older consumer cards to see how they handle Qwen 3.8 27B. The 24GB RTX 4090 and 3090 are evergreen favorites among local LLM fans thanks to their relatively large VRAM pools and relatively affordable prices on the used market, but as we've already emphasized, just being able to load the model weights is far from the whole picture.

These cards can fit the Q4_K_M GGUF of Qwen 3.8 27B with llama.cpp just fine, but they require using the Q8_0 quantization of the KV cache to fit the results in their smaller VRAM pools from the get-go, and they also require limiting the context depth to well under the model’s 262K native limit. We found that a context length of about 112K tokens was about the most we could get away with before running out of VRAM.

And unlike the 32GB RTX 5090, which can usually get away with having the Linux desktop window manager running next to the LLM and its infrastructure, these GPUs need every last byte of VRAM for the AI workload and nothing else. So you really want a separate graphics card at hand for these two cards if you're not running a headless server, which can introduce some setup headaches of its own as you discover how your particular motherboard handles PCIe slot bifurcation and enumeration of the primary graphics device.

Once you overcome those obstacles and get Qwen 3.8 27B up and running on these cards, llama.cpp exhibits the same performance cliff at long contexts on the RTX 4090 that we saw with the RTX 5090. But the RTX 3090 is oddly not affected. This suggests a bug somewhere.

We didn’t have time to dig into SGLang or vLLM behavior on these products, but given that you’re already tight for context on an RTX 5090, we’re doubtful that either of those inference engines would be an awesome way to run the model on these 24GB cards, unless you’re somehow ready to roll with multiple 3090s or 4090s from past acquisitions.

DGX Spark performance

Hardcore local LLM enthusiasts will scoff at the DGX Spark’s mere 27 GB/s of memory bandwidth for a dense model like Qwen 3.8 27B, and indeed, we've found that this platform isn't the fastest with dense models in our past testing.

But now that models like Qwen 3.8 27B support MTP with nothing more than a server launch flag, you can often get a major free boost to the decode speeds of platforms with limited memory bandwidth.

In our actual tests, the Spark's solid prefill processing performance means that it will often end up finishing inference turns at longer context lengths well before the RTX 5090 does with llama.cpp.

And beyond llama.cpp, the Spark is also well supported by SGLang and vLLM, so you can take advantage of those inference engines if they’re more to your taste. Consider also that a single Spark is still available for about $5000, and it’s a turnkey system that can be expanded into a handy cluster down the line if you want. So it shouldn’t be ruled out, even for serving this dense model.

Apple Mac Studio with M4 Max performance

The M4 Max-powered Mac Studio in our labs has the most memory bandwidth of any of the unified memory systems we have available, but as we've described in previous testing, that's only one metric that matters for local AI inference.

Prompt processing on this platform is slower than on Spark, so even if the Mac Studio can turn out more tokens than GB10 in the decode phase, it still ends up spending more time per inference turn than Nvidia's platform at longer contexts because that’s where it has to spend most of its processing time.

And at least in llama.cpp, using MTP on the Mac Studio actually causes a performance loss at shorter contexts for decode in exchange for a small boost at longer contexts, where it generally leads to improvements for other platforms. This demonstrates the value of actual benchmarking rather than spec-racing.

Ryzen AI Halo (Strix Halo) performance

AMD’s Ryzen AI Halo presents the worst-case performance scenario for this dense model: relatively low memory bandwidth and low prompt-processing performance.

Although MTP wakes up tokens-per-second throughput a bit on this system with llama.cpp, it can’t make up for the lengthy prompt processing times required for longer contexts. You can certainly run this model if a Strix Halo is the only box you have, but we’d seek out something more capable if you’re trying to do interactive long-context work.

Bottom line

When I first set out to explore Qwen 3.8 27B's performance, I figured this would be a relatively straightforward series of tests: plug in a single graphics card, load the model, get tokens, done. In practice, our experience required a lot more tinkering. And systems we might have initially written off as being not up to the task of running a dense model like this proved surprisingly useful.

In general, breathless claims of hundreds of tokens per second of throughput from an empty context window do not account for the full range of behavior one might see from an LLM on a given inference setup.

For just one example, whether it's down to a problem with (or just the expected behavior of) llama.cpp or something else about our software stack, the notion that you'd want to wait as much as 30 minutes or more for a response from Qwen 3.8 27B at long context lengths on an RTX 5090 is outrageous. But if you naively load Qwen 3.8 27B using llama.cpp right now, this is the experience you'll get.

Changing up inference engines is a natural next step, but there are trade-offs with that approach, too. You can load Qwen 3.8 27B on one 5090 using vLLM or SGLang, but those inference engines are much more conservative about the amount of usable context they’ll give you. The recipes we used only resulted in a context window of 32K tokens on a single 5090.

To enable the full 262K context length, we had to grab another RTX 5090 from the TH testing arsenal, at which point we got both great throughput and a TTFT sweep that could be considered interactive all the way out to the maximum context length from both model runners. But the price of replicating such a setup would exceed $13K right now.

You also might expect that a DGX Spark and its 273 GB/s of memory bandwidth wouldn't be useful for this dense model, but the prefill speed of the Spark ends up being fast enough that the TTFT remains relatively interactive even with a decode throughput of just 20 or so tokens per second with MTP, and that behavior holds out to the model's full native context length.

The M4 Max-powered Mac Studio has plenty of memory bandwidth on tap for decode, but its prompt processing speed means that the total time of an inference turn is dominated by that activity on this older Apple Silicon chip. The newer M5 Max and brand-new M5 Ultra would doubtless perform better, but we didn’t have those chips handy for this testing. And AMD’s Ryzen AI Halo gets the worst of it, with both low prompt processing speeds and relatively low TPS due to its memory bandwidth.

For all this, we really need to take a step back and consider the economics of local AI once again. $5K, $10K, or $15K or more for local AI hardware is a lot of tokens from leading-edge models at Anthropic or OpenAI (and even more from providers serving the recent slate of Chinese open-source models). A lot. And if time is money for you, barring compute constraints, those tokens will get back to you or your agent faster than anything you can run at home short of a DGX Station with its GB300 GPU.

So unless you’re working with sensitive data that requires on-premises processing, you’re an enthusiast who just wants to tinker, or you’re worried about the fate of open model distribution and inference more generally for some reason, you probably don’t need to rush out and build a box just for this model.

But if you do, be aware that delivered performance is more than just VRAM capacity or memory bandwidth, and that you might not get the best performance from your setup with the most common model runners like llama.cpp. Let experimentation and careful benchmarking lead you to the best results for your specific config.