TECH

AMD’s 31-chip Zen 6 EPYC 9006 lineup spans 8 to 256 cores

MD’s complete EPYC 9006 “Venice” lineup has surfaced with 31 Zen 6 and Zen 6c processors spanning the SP7 and SP8 platforms. The former will be available later this year, while the latter is expected to surface in early 2027.

AMD has published the complete processor lineup for its sixth-generation Zen 6-based Epyc 9006 series (H/T StorageReview). It reveals 31 server CPUs spanning two platforms. AMD originally introduced the series with its EPYC 9006 "Venice" processors at Advancing AI 2026 in July. While several flagship models and their pricing were already known, the company has now filled out the range with nine SP7 processors and another 22 SP8 models. AMD has also moved away from using TDP for these processors. Its product pages instead quote "Default CPU Power", which encompasses power consumption across the CPU's compute and I/O dies at its specified performance target.

The SP7 platform supports 16 DDR5 memory channels and 96 PCIe 6.0 lanes, with processors ranging from 64 to 256 cores. The flagship EPYC 9996 combines 256 Zen 6c cores and 512 threads with 1,024 MB of L3 cache. It has a 2.55 GHz base clock, boosts to 4.1 GHz and carries a 600 W default CPU power rating. The EPYC 9G76 is also listed as an orderable 96-core processor for $11,622. 

Prices quoted by AMD are 1Ku prices, meaning the listed figure represents the per-processor price when purchased in quantities of 1,000 units rather than necessarily what an individual chip will cost through retail channels. That $14,904 price is not itself new. AMD had already used the figure when comparing the Epyc 9996 against Intel's Xeon 6980P in July. The notable development is that AMD has now disclosed pricing and specifications for the rest of the lineup.

AMD has published the full EPYC 9006 series product lineup, confirming specifications and 1,000-unit pricing for 31 Zen 6 server processors. The family is split between nine SP7 processors and 22 SP8 models. Pricing ranges from $700 for the 8-core EPYC 9016 to $14,904 for the 256-core EPYC 9996.

SP7 is AMD’s larger 16-channel DDR5 platform. The lineup starts with the 64-core EPYC 9556 at $8,008 and reaches the 256-core EPYC 9996 at $14,904. The flagship has 256 cores and 512 threads, a 2.55 GHz base clock, up to 4.1 GHz boost and 1GB of L3 cache. Its Default CPU Power is rated at 600W with with a configurable range between 400W and 600W.

The SP7 range also includes the 192-core EPYC 9966 at $14,079, 168-core EPYC 9846 at $13,114 and 128-core EPYC 9756 at $12,498. AMD offers three 96-core models, including the EPYC 9686F with a boost clock of up to 5.0 GHz. The EPYC 9G76, used as a host processor in AMD Helios compute trays, is also listed as a standalone SKU at $11,622.

SP8 uses eight DDR5 memory channels and offers up to 128 PCIe 6.0 lanes. The 22-model lineup covers configurations from 8 to 128 cores. The fastest SP8 model by core count is the EPYC 9746 with 128 cores, 512MB of L3 cache and a 400W power rating for $11,679. A lower-priced 128-core EPYC 9736P is available for single-socket servers at $9,989.

AMD also has six high-frequency F-series processors in the SP8 lineup. These include models with 16, 24, 32, 48, 64 and 96 cores, all reaching up to 5.0 GHz. At the bottom of the stack, the EPYC 9116 offers 16 cores for $1,200, while the EPYC 9016 has eight cores, a 4.8 GHz boost clock and a $700 price.

AMD’s published specifications also clarify PCIe lane counts for the two platforms. SP7 processors are listed with 96 PCIe 6.0 lanes, while SP8 models have 128 lanes. One processor previously referenced by AMD, the 256-core EPYC 9956 rated at 400W, does not currently appear in the published product list. AMD states that the lineup remains subject to change.

Model	Cores / Threads	Base	Boost	L3 cache	Default CPU power	Sockets	1Ku price
EPYC 9996	256 / 512	2.55 GHz	4.1 GHz	1,024 MB	600 W (400-600 W)	1P / 2P	$14,904
EPYC 9966	192 / 384	2.9 GHz	4.0 GHz	768 MB	600 W (400-600 W)	1P / 2P	$14,079
EPYC 9846	168 / 336	2.85 GHz	3.7 GHz	768 MB	500 W (320-500 W)	1P / 2P	$13,114
EPYC 9756	128 / 256	3.15 GHz	4.0 GHz	512 MB	500 W (320-500 W)	1P / 2P	$12,498
EPYC 9G76	96 / 192	3.4 GHz	4.8 GHz	384 MB	500 W (320-500 W)	1P / 2P	$11,622
EPYC 9686F	96 / 192	3.4 GHz	5.0 GHz	384 MB	500 W (400-600 W)	1P / 2P	$11,434
EPYC 9656	96 / 192	3.05 GHz	3.7 GHz	512 MB	400 W (220-400 W)	1P / 2P	$9,713
EPYC 9586F	64 / 128	3.75 GHz	5.0 GHz	384 MB	500 W (400-600 W)	1P / 2P	$9,701
EPYC 9556	64 / 128	2.75 GHz	4.3 GHz	384 MB	300 W (220-300 W)	1P / 2P	$8,008

The larger Epyc SP8 family extends much further down the stack. These processors use eight DDR5 memory channels but offer 128 PCIe 6.0 lanes, with core counts ranging from eight to 128. AMD also offers several high-frequency "F" variants that can boost to 5 GHz, alongside single-socket "P" models. At the bottom sits the $700 EPYC 9016 with eight cores, 16 threads and a 130 W default CPU power rating. At the other end, the 128-core EPYC 9746 costs $11,679.

Model	Cores / Threads	Base	Boost	L3 cache	Default CPU power	Sockets	1Ku price
EPYC 9746	128 / 256	2.9 GHz	4.0 GHz	512 MB	400 W (200-400 W)	1P / 2P	$11,679
EPYC 9736	128 / 256	2.7 GHz	3.7 GHz	256 MB	360 W (200-400 W)	1P / 2P	$10,639
EPYC 9736P	128 / 256	2.7 GHz	3.7 GHz	256 MB	360 W (200-400 W)	1P	$9,989
EPYC 9676F	96 / 192	3.1 GHz	5.0 GHz	384 MB	400 W (200-400 W)	1P / 2P	$10,116
EPYC 9646	96 / 192	2.8 GHz	3.7 GHz	256 MB	300 W (155-300 W)	1P / 2P	$8,904
EPYC 9646P	96 / 192	2.8 GHz	3.7 GHz	256 MB	300 W (155-300 W)	1P	$8,001
EPYC 9576F	64 / 128	3.55 GHz	5.0 GHz	384 MB	400 W (200-400 W)	1P / 2P	$9,431
EPYC 9536	64 / 128	3.25 GHz	4.0 GHz	256 MB	300 W (155-300 W)	1P / 2P	$7,837
EPYC 9526	64 / 128	2.75 GHz	3.7 GHz	256 MB	220 W (130-220 W)	1P / 2P	$7,123
EPYC 9536P	64 / 128	3.25 GHz	4.0 GHz	256 MB	300 W (155-300 W)	1P	$6,595
EPYC 9476F	48 / 96	3.65 GHz	5.0 GHz	192 MB	330 W (200-400 W)	1P / 2P	$6,695
EPYC 9456	48 / 96	3.2 GHz	3.7 GHz	256 MB	265 W (155-300 W)	1P / 2P	$5,252
EPYC 9456P	48 / 96	3.2 GHz	3.7 GHz	256 MB	265 W (155-300 W)	1P	$4,628
EPYC 9376F	32 / 64	3.8 GHz	5.0 GHz	192 MB	285 W (200-400 W)	1P / 2P	$4,849
EPYC 9356	32 / 64	3.6 GHz	4.5 GHz	192 MB	250 W (155-300 W)	1P / 2P	$3,789
EPYC 9336	32 / 64	3.15 GHz	3.7 GHz	128 MB	195 W (130-220 W)	1P / 2P	$3,320
EPYC 9356P	32 / 64	3.6 GHz	4.5 GHz	192 MB	250 W (155-300 W)	1P	$2,795
EPYC 9276F	24 / 48	3.8 GHz	5.0 GHz	96 MB	230 W (200-400 W)	1P / 2P	$3,512
EPYC 9256	24 / 48	2.85 GHz	4.5 GHz	96 MB	190 W (130-220 W)	1P / 2P	$2,501
EPYC 9176F	16 / 32	3.9 GHz	5.0 GHz	192 MB	200 W (200-400 W)	1P / 2P	$3,787
EPYC 9116	16 / 32	2.85 GHz	4.5 GHz	48 MB	160 W (130-220 W)	1P / 2P	$1,200
EPYC 9016	8 / 16	3.05 GHz	4.8 GHz	48 MB	130 W (130-220 W)	1P / 2P	$700

AMD's previously announced launch schedule remains unchanged. Systems based on the high-end SP7 platform are expected in Q4 2026, while SP8 systems are scheduled to follow in the first half of 2027.

TECH

Your new work laptop in 2026: EliteBook vs ThinkPad, the battle of the 16-inch laptops

If one is looking for a big office laptop for work, the names EliteBook and ThinkPad should quickly appear on the radar. We tested both the Lenovo ThinkPad T16 Gen 5 and the HP EliteBook 8 G2a 16, and can compare both.

A big laptop for productivity seems like something a lot of people could need. Having a big screen, a full-size keyboard, as well as plenty of ports and a big touchpad makes working and using a laptop comfortable, especially when the person using it is not traveling too much.

Smart performance, expansive screen, easy management:

-Intel® powers next-gen AI PCs...Free up time to focus on what matters with the latest Intel® Core™ Ultra processor, featuring an NPU designed specifically for the next era of AI software. Three computing engines manage AI workloads, delivering long battery life and powerful graphics.

-Protect yourself from prying eyes... Shield yourself from prying eyes with the optional HP Sure View privacy screen, which you can quickly activate at the touch of a key. When enabled, HP Sure View reduces visible light, making the screen unreadable when viewed from the side.

As is typical for the EliteBook 8 series, HP uses aluminum for the chassis. With the exception of the keyboard, screen bezel, and hinge cover, the entire device is made of this lightweight silver metal. The laptop features a clean, simple design perfectly suited for a business environment and is comfortable to use, thanks to its rounded corners and edges. The metal surfaces feel premium and do not easily attract smudges. Furthermore, the chassis is generally very stable and robust. While it exhibits slightly more flex than the pricier EliteBook X G2i—and the screen wobbles a bit more—it remains a high-quality device. The lid can be opened with one hand, and the maximum opening angle is approximately 166 degrees.

Accessing the interior of the HP laptop is easy, with four captive screws securing the bottom panel. The SSD, 5G card, and Wi-Fi card are modular components. Unfortunately, the RAM is soldered.

HP employs its standard backlit EliteBook keyboard—featuring a numeric keypad, a six-row layout, and matte gray Chiclet keys—paired with a mechanical glass clickpad (13.5 x 8.5 cm). Both input devices offer excellent usability; the keyboard, in particular, ranks among the best in the laptop world, although the small arrow keys are a downside.

-Fast and efficient wireless networking...Your PC's portability and the reliability of a fast connection determine where you can work. Get a fast, reliable connection in dense wireless environments with Wi-Fi 7, capable of supporting gigabit-class data speeds.

Some prime examples for this class of laptop are the Lenovo ThinkPad T16 Gen 5 and the HP EliteBook 8 G2a 16. HP and Lenovo are the two biggest PC manufacturers, and the EliteBook laptops and ThinkPad laptops are their two premier business laptop series for office use. We reviewed both, with the review of the HP EliteBook 8 G2a 16 being published more recently. A good opportunity for a comparison!

Overall, both laptops are very similar physically, even though they do look very different. 16-inch screen, keyboard with numpad and a port selection with a mix of legacy and modern ports. Sure, the EliteBook is silver and the ThinkPad is black, but that is mostly just for the looks - both have an aluminum lid, and while the ThinkPad uses a plastic base with a magnesium insert and the EliteBook an aluminum base, their build quality is comparable. One detail: The ThinkPad screen opens to 180 degrees, the EliteBook only to 166 degrees.

Of course, the ThinkPad as a red TrackPoint with dedicated mouse clickers in addition to its plastic-made mechanical clickpad, while the EliteBook, on the other hand, just as a bigger glass clickpad. Which is better for you depends entirely on user preference. Better, with no doubt, is that the ThinkPad has a dedicated RJ-45 Ethernet port, as well as a more easily repairable keyboard and upgradeable SO-DIMM memory. The RAM of the HP EliteBook laptop is, sadly, soldered. At least with the EliteBook, the Wi-Fi module is modular, it is soldered in case of the ThinkPad.

When it comes to the screen, the EliteBook display straight up beats the ThinkPad's more mediocre screen, mostly thanks to a better color reproduction. However, you can also configure the ThinkPad with better screens, so this is largely up to the model you choose. The same applies, to a lesser extend, to battery life, as there are two versions of each: The HP EliteBook 8 G2a 16 can be configured with a 62 or 77 Wh battery, and the ThinkPad with a 60 or 75 Wh battery. We tested both with the bigger battery, and the EliteBook delivered better battery life.

The ThinkPad T16 is Lenovo's premium business laptop with a large 16-inch screen and there have been some changes for the new G5 generation. You can still decide between AMD and Intel chips and our review unit today is the entry-level model with an AMD Ryzen PRO CPU, 16 GB RAM, 512 GB SSD as well as the standard IPS screen. While the specs are mediocre, the price is still pretty high at $1639.

Lenovo changed the chassis construction for the new Gen 5 model of the T16, even though the overall visual appearance is still pretty much the same as before, including the camera bump. The most obvious change is the new central hinge design (instead of two smaller hinges), so the lower bezel is a bit smaller. The overall quality is still excellent, even though it might not feel very premium due to the use of plastic. The surfaces do not attract as much dirt as expected and the stability is excellent without any creaking sounds. The hinges are well adjusted and the maximum opening angle is 180 degrees. Maintenance options are limited to a single M.2-2280 SSD, you get two regular SO-DIMM slots for RAM and you can clean the fan.

Connectivity is very good with Wi-Fi 7, Gigabit-Ethernet and antennas for an LTE module (antennas preinstalled) as well as a sufficient number of ports including Thunderbolt 4. Wi-Fi 7 performance is okay and stable around 2 Gbps and the 5 MP camera with mechanical shutter (and IR) takes decent pictures. 

Verdict - ThinkPad T16 G5 is let down by the poor screen...The new ThinkPad T16 Gen 5 is a rather underwhelming business laptop with a premium price tag. The chassis changes are good and the improved repairability is definitely an advantage, but the overall user experience is affected by the poor screen. It only covers the sRGB gamut by 60 % and the image quality is pretty poor, which is hard to accept for premium T-series. There are optional screens with more color gamut, but no options with higher resolutions and/or refresh rates. The other problem of our entry-level review unit is the rather disappointing performance in general with the old Zen 4 processor, 16 GB RAM and the small 512 GB SSD.

There are obviously positive aspects as well, including the connectivity features and ports including Wi-Fi 7 as well as Thunderbolt 4, the comfortable input devices and also the long battery runtime. We were also surprised by the sound quality, which is very good, not only for a business laptop. The fan is also very quiet, but spins up occasionally even while idling, which is annoying. All in all, the T16 Gen 5 is a good business laptop, but it is held back by the display. We strongly recommend you get the better display option if you are interested in this device.

This brings us, last, but not least, to the performance. Here, the EliteBook wins quite comfortably, but that is only natural. After all, the EliteBook comes equipped with a Ryzen 7, and a newer generation CPU at that, while the ThinkPad we tested contained an older Ryzen 5. Again: You can also configure the ThinkPad with the more powerful and modern CPU. What is clear, however, is that the EliteBook gets louder under load, while the ThinkPad runs a bit more toasty.

In the end, the ThinkPad T16 Gen 5 and EliteBook 8 G2a 16 are very comparable. Which one you should choose is mostly a matter of taste - and which manufacturer has the better prices at the moment you buy. The EliteBook does have the better battery life, but soldered RAM harms its long-term viability a bit.

mundophone

DIGITAL LIFE

The strange behavior of AIs when choosing between two faces

For years, artificial intelligence has been touted as a tool capable of reducing subjective decision-making and helping to eliminate human biases. But there is an uncomfortable catch to this promise: these systems learn from vast amounts of human-generated content. This means they can embed into their algorithms some of the associations and judgments inherent in our own culture. A new experiment reveals the extent of this issue.

A study published in *PNAS Nexus* analyzed how different artificial intelligence models interpret human faces when provided with little information beyond the image itself.

Researchers tested models such as GPT-4o, GPT-5, Gemini 3 Flash Preview, and Claude Sonnet 4.5. Instead of being given details on professional history, behavior, or personal background, the systems had to make comparisons based essentially on appearance.

Some of the traits evaluated were highly subjective: intelligence, work ethic, trustworthiness, aggressiveness, and even selfishness.

The experiment began with GPT-4o, involving approximately 4,600 comparisons between computer-generated faces. Researchers subsequently repeated various tests with other advanced models to see if this behavior was widespread.

In some scenarios, the question seemed almost innocuous: which of the two people appears more intelligent or hardworking? In others, however, the implications were far more serious—such as choosing who would make a good financial manager, who should receive funding, or which individual seemed more likely to commit a specific crime.

It was precisely here that a pattern emerged—one that was impossible to ignore.

In tests regarding trust and trustworthiness, GPT-4o selected the same type of face that human participants had previously associated with those traits in nearly 75% of cases. The machine might be learning our own stereotypes...The next question is inevitable: where do these associations come from?

Researchers do not yet have a definitive answer, but there is a particularly interesting hypothesis. AI models are trained on vast amounts of text, images, and other content produced over the years. And these materials are not culturally neutral.

Literature, film, advertising, and other forms of representation often link physical traits to specific behaviors or personality types. A character with a certain look might be repeatedly portrayed as dangerous, another as intelligent, and another as trustworthy.

By absorbing billions of such examples, a model can learn these statistical relationships without necessarily “believing” in them the way a person believes in a stereotype.

The problem arises when this association surfaces during the decision-making process.

At that point, a technology meant to analyze objective information may end up reproducing subjective patterns that were already embedded in the training data.

And there is a crucial difference: when a person judges someone based on appearance, we recognize it as a subjective impression. When the same result appears via an AI interface, there is a risk it will seem far more scientific simply because it was generated by an algorithm.

When appearance factors into major decisions...This possibility takes on new significance as artificial intelligence systems begin to play a role in real-world processes.

Automated tools are already being used or tested in areas such as recruitment, financial analysis, security, and candidate assessment. If a system internalizes associations between appearance and specific personal traits, these biases could end up influencing decisions that affect people's lives.

The issue isn't just the presence of bias within the model; it is the risk that this bias remains hidden behind a veneer of technological neutrality.

However, the researchers point out that the study has limitations. The tests were conducted under controlled conditions and do not necessarily demonstrate that the same behaviors would occur in real-world applications.

There is also the possibility of some overlap between the images used and the data present in the models' training sets, although the researchers found no clear evidence that the systems were simply recognizing those images.

Even so, the study raises a question that is becoming increasingly difficult to ignore: if AI learns from human culture, it can also learn our stereotypes.

The challenge, therefore, is not merely to build smarter models. It is to figure out how to prevent this intelligence from turning old human judgments into automated—and seemingly objective—decisions.

When artificial intelligence is tasked with choosing between two faces—whether evaluating attractiveness, predicting personality traits, or deciding if two photos belong to the same person—its underlying logic often exhibits highly unpredictable, "black box" behavior. Recent studies have exposed a fascinating disconnect between human perception and machine logic.

The strange behaviors driving how AI chooses between faces break down into several distinct patterns:

1. The "polite bias" and inflated scores...When AI models are asked to evaluate facial attractiveness, they show a striking behavioral quirk compared to humans.

• The Perfect Ranking: When arranging a selection of faces from most to least attractive, AI perfectly mirrors human consensus.

• The Generosity Glitch: However, when it comes to assigning the actual scores, AI is much more generous. A study tracking AI face-rating behavior noted that while the average human rating for a set of faces sat around 3 out of 7, the AI models averaged a much higher 4.7, completely avoiding giving out low or "ugly" scores.

2. Arbitrary "face-to-character" biases...A massive vulnerability in Multimodal Large Language Models (MLLMs) is their tendency to hallucinate deep internal traits from completely arbitrary structural features.

• Superficial Judgments: Recent behavioral data shows that when forced to choose between two faces for tasks like "Who is more likely to be a criminal?" or "Who would perform better at this job?", AI relies on subtle, unscientific facial geometries.

• The Guardrail Failure: While developers implement strict safety filters to prevent explicit racial or gender profiling, these guardrails fail to block more abstract geometric biases. The AI still quietly favors specific nose bridges, jawlines, or eye spacings, associating them with positive human traits.

3. The "too average" trap in AI-generated faces...When the task is inverted—asking an AI or a human to choose which face is real and which is an AI-generated deepfake—a completely different problem occurs: the faces are too perfect.

• AI-synthesized faces occupy a mathematically centralized "average" space. They lack the subtle asymmetries, natural skin imperfections, and unique structural quirks of real humans.

• Ironically, because our brains subconsciously equate facial symmetry and "averageness" with trustworthiness and beauty, both humans and algorithms are easily tricked into rating fake AI faces as more trustworthy than real, flawed human faces.

4. Machine disagreement and demographics...Even in strict 1:1 face matching (verifying if two pictures show the exact same person), top-tier AI models frequently stumble over a fundamental issue: they rarely agree with one another.

• If you run the exact same pair of faces through different commercial vision algorithms, they often spit out conflicting similarity percentages.

• This variability worsens drastically depending on demographic data. Because training datasets historically skew toward specific populations, an AI’s accuracy drops significantly when choosing or matching faces of underrepresented races, leading to much higher rates of false positives.

mundophone

 

TECH

Panther Lake teardown reveals Intel 18A in detail — TSMC still leads in density

SemiAnalysis has taken Intel’s Panther Lake apart, showing how the company’s 18A process combines RibbonFET gate-all-around transistors with PowerVia backside power delivery. The analysis finds that 18A delivers logic density similar to TSMC’s N3E, but does not yet give Intel a clear manufacturing lead over TSMC’s newer nodes.

SemiAnalysis has taken a deep look inside Intel’s Core Ultra 7 365 (Panther Lake) processor, offering a first independent look at what is inside the chips and at Intel’s most advanced chipmaking technology to date. The analysis focuses on Intel’s 18A process, including its new transistor design and backside power-delivery system — two technologies the company sees as central to its manufacturing comeback.

Intel’s Panther Lake is more than a new laptop processor. It is the first consumer product to put Intel’s 18A manufacturing process into customers’ hands. In a teardown of a Core Ultra 7 365, SemiAnalysis examined the package, its individual silicon tiles, and cross-sections reaching down to the transistors. The result offers a close look at how Intel’s manufacturing plans work in a shipping chip.

The central change is RibbonFET, Intel’s version of a gate-all-around transistor. Its gate surrounds four stacked silicon channels, giving engineers tighter control over current. Panther Lake also introduces PowerVia, which delivers power through wiring behind the transistors. Moving much of that wiring off the front leaves more room for signal connections. Both changes add manufacturing complexity, and SemiAnalysis notes tradeoffs in capacitance and heat flow.

The package shows why calling Panther Lake an “18A chip” needs some care. Its compute tile uses Intel 18A, but the graphics tile varies by model: the smaller version uses Intel 3, while the larger, 12-core version uses TSMC N3E. The I/O tile uses TSMC N6. Intel joins the tiles on a passive silicon base with Foveros-S packaging. That arrangement lets it reserve its newest process for the CPU while choosing other processes for graphics and external connections.

SemiAnalysis measured similar logic density in Panther Lake’s 18A compute tile and its N3E graphics tile. It did not find a peak-density lead over newer competing processes. That distinction captures the teardown’s main finding: Panther Lake proves Intel can ship gate-all-around transistors and backside power together at consumer scale, but the silicon alone cannot establish a lasting manufacturing lead. Performance, production cost, yield, and Intel’s next products will determine how far 18A takes it.

The key finding is that Intel has successfully put both technologies into a real consumer chip. But the teardown also suggests that 18A is roughly on par with TSMC’s N3E in logic density. The names “18A” and “N3E” should not be read as literal physical dimensions 1,8 and 3nm respectively, however: they are node labels rather than directly comparable measurements.

Panther Lake is Intel’s first consumer processor to use RibbonFET transistors, the company’s version of gate-all-around (GAA) technology. Rather than sending current through the narrow vertical “fins” used in older FinFET designs, RibbonFET uses four stacked horizontal silicon sheets. The transistor gate surrounds each sheet, giving Intel tighter control over the electrical current flowing through the transistor and helping to reduce leakage.

The other major change is PowerVia, Intel’s implementation of a backside power-delivery system. In a conventional processor, power and data signals travel through the metal wiring layers above the transistors. PowerVia moves much of the power network to the other side of the chip, underneath the transistor layer. That frees frontside wiring for signals and enables shorter, wider power paths, potentially reducing electrical resistance and improving voltage stability for the chip’s most demanding blocks.

One of the most interesting findings from the teardown concerns transistor density, which gives us a more restrained view of 18A’s competitive position. SemiAnalysis measured Panther Lake’s 18A compute logic and found that it has roughly the same density as the GPU logic manufactured on TSMC’s N3E process. This is a significant result for Intel, which in recent years has lagged behind the largest contract chip manufacturers in advanced manufacturing processes. However, SemiAnalysis says 18A does not lead TSMC’s newer N3P and N2 nodes, or Samsung’s SF2, in peak density.

That matters because transistor density remains one of the clearest indicators of a manufacturing node’s potential cost and scaling advantages. Intel’s approach may still produce benefits in power delivery, routing flexibility and performance, but it does not automatically translate into the smallest possible logic area.

The analysis also shows how Intel is using chiplets and advanced packaging to combine different process technologies in one product. The processor combines separate compute, GPU and I/O tiles on a passive silicon base using Foveros-S packaging. The compute tile is made on Intel 18A, while the GPU comes in two versions: a four-core Xe3 tile produced on Intel 3, and a larger 12-core version manufactured by TSMC using N3E. The I/O tiles are also made by TSMC, using its older N6 process.

SemiAnalysis found notable changes inside the compute tile as well. The teardown indicates that Panther Lake’s CPU architecture is an evolution rather than a complete redesign. Its Cougar Cove performance core remains close in area to the Lion Cove core used in the previous-generation Lunar Lake, even as L2 cache capacity rises from 2.5 MB to 3 MB per core. SemiAnalysis estimates that Cougar Cove fits 20 % more L2 cache into a similarly sized core area. Meanwhile, the four-core Darkmont LP E-core cluster is about 5 % smaller than its predecessor.

The teardown also points to an efficiency-focused redesign of Intel’s AI accelerator. Panther Lake’s NPU 5 is said to take up 36.9 % less area than the NPU 4 in Lunar Lake while keeping the same overall INT8 MAC count. Intel consolidated processing into fewer, larger neural compute engines and reduced the number of scratchpad memories and SHAVE DSPs from 12 to six. NPU 5 also adds native FP8 support, a lower-precision data format increasingly used for AI inference.

The GPU offers another interesting comparison. An Xe3 core in the Intel 3-based GT1 tile is about 55 % larger than one in the 12-core N3E-based GT2 tile. Put simply, Intel’s own process can produce the GPU, but TSMC’s N3E allows Intel to fit substantially more graphics hardware into the same silicon area.

mundophone

 

TECH

Developer shows off Red Dead Redemption 2, GTA V, Fallout 4, and other AAA PC games running natively on iPhone 18 Pro

A developer has built a Windows PC game emulator for iOS that seems to run AAA titles like Red Dead Redemption 2, GTA V, Fallout 4, and more at 720p 30 fps or better. The emulator is a port of Wine along with ARM64 and DirectX-to-Metal translation layers. The requires JIT to function well, so a manual sideload is needed and is preferably used with DRM-free titles.

Modern Apple iPhones have incredibly powerful SoCs but very few apps and games tap their full potential. One area that can let chips like the A19 Pro and A20 Pro unleash their full potential is game emulation.

Quite a few game emulators like the Delta for Nintendo systems, PPSSPP for the PlayStation Portable, and Dolphin for the GameCube and Wii among others are either available via the App Store or can be sideloaded.

However, there have been no emulators for Windows PC games on iOS unlike Android. That changes with Madeira.

Madeira allows running Windows PC games on a non-jailbroken iPhone. According to the developer Will Faust, Madeira is essentially a port of Wine (ARM64EC) for iOS with FEX-Emu for x86-64 to ARM64 translation and DXMT for D3D11 to Metal translation combined into a single pipeline.

Madeira requires a minimum of iPhone 13 Pro or above powered by the A15 Bionic or above.

In the project's GitHub repo, Faust claims that Madeira can run games like Thumper and Ultrakill while Marvel Cosmic Invasion "has reached gameplay" albeit being unreliable.

Now, Madeira also seems to be able to run the highly demanding Red Dead Redemption 2 (RDR 2) fully on device on an iPhone 18 Pro. While the initial attempt has been just a slideshow with single digit frame rates and loads of graphical glitches, the developer showed progress in attaining close to 30 fps in more recent builds at nearly 720p. There's still a long way to go, however, especially with regards to arrangement of onscreen controls.

Additionally, Madeira now supports a swap-like implementation that can offload memory into the iPhone's storage. This apparently kept the RDR 2's memory footprint around the 7.6 GB mark with possibility of lowering it even further to allow running on 8 GB iPhones.

GTA 5 support is in the works too alongside a host of other AAA titles...Faust also has a working copy of Grand Theft Auto V running on the iPhone. Performance seems to be decent for an initial implementation and will only get better with further optimizations.

The devs note in the project's Discord channel that using Steam hogs a lot of resources. The workaround for this was Madeira Dock, a headless Steam client that only uses the necessary verified Steam DLLs without the headroom needed by the full Steam client. This is not a bypass for Steam's DRM, however.

The devs note in the project's Discord channel that using Steam hogs a lot of resources. The workaround for this was Madeira Dock, a headless Steam client that only uses the necessary verified Steam DLLs without the headroom needed by the full Steam client. This is not a bypass for Steam's DRM, however.

The other alternative, they note, is to use DRM-free games from GOG.

The project has also demonstrated Fallout 4 running on iOS along with several other 32-bit games like Dead Space, Mirror's Edge, Far Cry 3, and more.

Emulation requires sideloading with JIT...Just-in-time (JIT) compilation is required for fast, real-time performance when emulating apps on a non-native host CPU. A major quirk with the Apple App Store (from iOS 18.4+) is that it does not allow apps and emulators to use JIT owing to security concerns, with the only exceptions being approved alternate web browser engines via BrowserEnginekit.

Therefore, the only way to get emulators like Madeira running is to sideload the IPA manually and enable JIT for optimal performance. Which means that apps like Madeira will never be distributed via the App Store.

Sideloading on iOS can be a bit tricky if you are new to the process, but it is not too difficult once you get the hang of it. 

You can purchase an Apple Developer certificate for $99 and get a year's time to use the sideloaded app before renewal. Regular Apple account users can also sideload provided you renew the sideloaded app every seven days.

mundophone